                                                                                
 ▐▛███▜▌   Claude Code v2.1.12                                                  
▝▜█████▛▘  Opus 4.5 · Claude Max                                                
  ▘▘ ▝▝    ~/code/upskill                                                       
                                                                                
❯ https://agentskills.io/home https://platform.claude.com/docs/en/agents-an     
  d-tools/agent-skills/overview research and propose a library for              
  generating agent skills for coding agents. it should solve this use case      
  <context>I'm still pretty convinced that skills are going to be generated     
  more than shared, because they're not entirely transferable across model      
  (generations) and will become stale and/or irrelevant as models improve.      
  So I think we were right not to go for a skills registry.                     
  The skill generation side still seems underdeveloped to say it's one of       
  the main usages for anthropic. i.e stuff like this:                           
  get agents to reliably generate skills for a given the task or tool           
  eval how good an agent is with a skill vs without                             
  generate skills with bigger models to use with smaller ones                   
  these should be big unlocks for local models too, because you could           
  basically use Opus for new tasks and local+skills for regular tasks.          
  (edited) </context>                                                           
                                                                                
⏺ I'll research both URLs to understand the current state of agent skills, then 
  propose a library design.                                                     
                                                                                
⏺ Fetch(https://agentskills.io/home)                                            
  ⎿  Received 182.1KB (200 OK)                                                  
                                                                                
⏺ Fetch(https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overvi
       ew)                                                                      
  ⎿  Received 17.5KB (200 OK)                                                   
                                                                                
⏺ Let me also fetch the specification and the engineering blog for deeper       
  understanding.                                                                
                                                                                
⏺ Fetch(https://www.anthropic.com/engineering/equipping-agents-for-the-real-worl
       d-with-agent-skills)                                                     
  ⎿  Received 128.9KB (200 OK)                                                  
                                                                                
⏺ Fetch(https://github.com/agentskills/agentskills)                             
  ⎿  Received 234.9KB (200 OK)                                                  
                                                                                
⏺ Fetch(https://agentskills.io/specification)                                   
  ⎿  Received 381.5KB (200 OK)                                                  
                                                                                
⏺ Fetch(https://github.com/anthropics/skills)                                   
  ⎿  Received 270.8KB (200 OK)                                                  
                                                                                
⏺ Now I have a solid understanding. Let me propose a library design that        
  addresses your specific use cases.                                            
                                                                                
  ---                                                                           
  Proposed Library: skillforge                                                  
                                                                                
  A Python library for generating, evaluating, and managing agent skills        
  programmatically.                                                             
                                                                                
  Core Philosophy                                                               
                                                                                
  Skills are ephemeral, task-specific distillations rather than permanent shared
   artifacts. The library treats skill generation as a form of knowledge        
  distillation where larger models teach smaller ones via structured procedural 
  knowledge.                                                                    
                                                                                
  ---                                                                           
  Architecture                                                                  
                                                                                
  skillforge/                                                                   
  ├── generate/           # Skill generation from tasks, tools, traces          
  │   ├── from_task.py    # Generate skill from task description                
  │   ├── from_trace.py   # Extract skill from successful agent traces          
  │   ├── from_tool.py    # Generate skill for MCP/tool integration             
  │   └── refine.py       # Iterative skill refinement                          
  ├── evaluate/           # Skill effectiveness measurement                     
  │   ├── benchmark.py    # A/B testing with/without skills                     
  │   ├── metrics.py      # Success rate, token efficiency, time                
  │   └── staleness.py    # Detect when skills degrade                          
  ├── distill/            # Cross-model skill transfer                          
  │   ├── teacher.py      # Big model generates skills                          
  │   └── student.py      # Small model uses skills                             
  ├── format/             # SKILL.md spec compliance                            
  │   ├── schema.py       # Pydantic models for validation                      
  │   ├── render.py       # Generate valid SKILL.md                             
  │   └── parse.py        # Parse existing skills                               
  └── registry/           # Local skill management (not sharing)                
      ├── versioned.py    # Track skill versions per model                      
      └── selector.py     # Pick best skill for model/task                      
                                                                                
  ---                                                                           
  Core API                                                                      
                                                                                
  from skillforge import SkillForge, Skill, EvalResult                          
  from skillforge.models import ModelConfig                                     
                                                                                
  # Initialize with teacher (big) and student (small) models                    
  forge = SkillForge(                                                           
      teacher=ModelConfig(provider="anthropic",                                 
  model="claude-opus-4-5-20251101"),                                            
      student=ModelConfig(provider="ollama", model="qwen2.5-coder:32b"),        
  )                                                                             
                                                                                
  # 1. Generate skill from task description                                     
  skill = await forge.generate.from_task(                                       
      task="Parse and validate JSON Schema files, reporting errors with line    
  numbers",                                                                     
      examples=[                                                                
          {"input": "schema.json", "expected": "Valid schema with 3             
  definitions"},                                                                
          {"input": "invalid.json", "expected": "Error at line 42: missing      
  required field 'type'"},                                                      
      ],                                                                        
      max_iterations=3,  # Refine until student succeeds                        
  )                                                                             
                                                                                
  # 2. Generate skill from successful agent trace                               
  skill = await forge.generate.from_trace(                                      
      trace=agent_execution_trace,  # Recorded successful execution             
      generalize=True,  # Abstract specific values to patterns                  
  )                                                                             
                                                                                
  # 3. Generate skill for an MCP tool                                           
  skill = await forge.generate.from_tool(                                       
      tool_schema=mcp_tool_definition,                                          
      usage_examples=["List all pods in namespace X", "Get logs from pod Y"],   
  )                                                                             
                                                                                
  # 4. Evaluate skill effectiveness                                             
  result: EvalResult = await forge.evaluate.benchmark(                          
      skill=skill,                                                              
      model=forge.student,                                                      
      test_cases=test_suite,                                                    
      baseline=True,  # Also run without skill for comparison                   
  )                                                                             
                                                                                
  print(f"With skill: {result.with_skill.success_rate:.1%}")                    
  print(f"Without skill: {result.without_skill.success_rate:.1%}")              
  print(f"Token savings: {result.token_delta:+d}")                              
                                                                                
  # 5. Check if skill is stale for newer model                                  
  staleness = await forge.evaluate.staleness(                                   
      skill=skill,                                                              
      original_model="claude-3-5-sonnet-20241022",                              
      current_model="claude-sonnet-4-20250514",                                 
      test_cases=test_suite,                                                    
  )                                                                             
                                                                                
  if staleness.is_stale:                                                        
      print(f"Skill degraded: {staleness.reason}")                              
      # Regenerate for new model                                                
      skill = await forge.generate.from_task(task, model=current_model)         
                                                                                
  ---                                                                           
  Key Features                                                                  
                                                                                
  1. Iterative Generation with Verification                                     
                                                                                
  @dataclass                                                                    
  class GenerationConfig:                                                       
      max_iterations: int = 5                                                   
      verification_threshold: float = 0.8  # 80% test pass rate                 
      include_antipatterns: bool = True    # What NOT to do                     
      progressive_disclosure: bool = True  # Split into references/             
                                                                                
  async def from_task(task: str, examples: list[Example], config:               
  GenerationConfig) -> Skill:                                                   
      """                                                                       
      1. Teacher model generates initial skill                                  
      2. Student model attempts test cases                                      
      3. Teacher analyzes failures, refines skill                               
      4. Repeat until threshold met or max iterations                           
      """                                                                       
                                                                                
  2. Trace-to-Skill Extraction                                                  
                                                                                
  async def from_trace(trace: AgentTrace, generalize: bool = True) -> Skill:    
      """                                                                       
      Extract reusable skill from successful agent execution:                   
      1. Identify decision points and tool usage patterns                       
      2. Abstract specific values (file paths, IDs) to placeholders             
      3. Capture error handling and edge cases encountered                      
      4. Generate SKILL.md with examples from actual execution                  
      """                                                                       
                                                                                
  3. Cross-Model Distillation                                                   
                                                                                
  class DistillationPipeline:                                                   
      """                                                                       
      Use Opus to generate skills that unlock capabilities in local models.     
                                                                                
      The insight: local models can follow detailed procedural instructions     
      even when they can't derive the procedure themselves.                     
      """                                                                       
                                                                                
      async def distill(                                                        
          self,                                                                 
          task: str,                                                            
          teacher: Model,  # Opus                                               
          student: Model,  # Local Qwen/Llama                                   
          difficulty_calibration: bool = True,                                  
      ) -> Skill:                                                               
          # 1. Teacher solves task, explains reasoning                          
          # 2. Teacher generates skill at student's level                       
          # 3. Verify student can follow the skill                              
          # 4. Teacher refines based on student failures                        
                                                                                
  4. Skill Evaluation Framework                                                 
                                                                                
  @dataclass                                                                    
  class EvalResult:                                                             
      with_skill: TestResults                                                   
      without_skill: TestResults                                                
                                                                                
      @property                                                                 
      def skill_lift(self) -> float:                                            
          """Improvement in success rate from using skill."""                   
          return self.with_skill.success_rate - self.without_skill.success_rate 
                                                                                
      @property                                                                 
      def token_efficiency(self) -> float:                                      
          """Ratio of tokens used with/without skill."""                        
          return self.with_skill.total_tokens / self.without_skill.total_tokens 
                                                                                
      @property                                                                 
      def is_beneficial(self) -> bool:                                          
          """Skill provides net benefit (better results OR same results         
  cheaper)."""                                                                  
          return self.skill_lift > 0.05 or (                                    
              self.skill_lift >= 0 and self.token_efficiency < 0.8              
          )                                                                     
                                                                                
  5. Staleness Detection                                                        
                                                                                
  async def check_staleness(                                                    
      skill: Skill,                                                             
      original_model: str,                                                      
      current_model: str,                                                       
      test_cases: list[TestCase],                                               
  ) -> StalenessReport:                                                         
      """                                                                       
      Detect when a skill becomes counterproductive due to model improvements.  
                                                                                
      A skill is stale when:                                                    
      1. Current model performs BETTER without the skill                        
      2. Current model ignores/contradicts skill instructions                   
      3. Skill references deprecated patterns/APIs                              
      """                                                                       
                                                                                
  ---                                                                           
  Skill Schema (Pydantic)                                                       
                                                                                
  from pydantic import BaseModel, Field, field_validator                        
  import re                                                                     
                                                                                
  class SkillMetadata(BaseModel):                                               
      author: str | None = None                                                 
      version: str = "1.0"                                                      
      generated_by: str | None = None  # Model that created it                  
      generated_for: str | None = None  # Target model                          
      generated_at: datetime | None = None                                      
      test_pass_rate: float | None = None                                       
                                                                                
  class Skill(BaseModel):                                                       
      name: str = Field(..., min_length=1, max_length=64)                       
      description: str = Field(..., min_length=1, max_length=1024)              
      license: str | None = None                                                
      compatibility: str | None = Field(None, max_length=500)                   
      metadata: SkillMetadata = Field(default_factory=SkillMetadata)            
      allowed_tools: list[str] | None = None                                    
                                                                                
      # Content                                                                 
      body: str  # Main instructions markdown                                   
      references: dict[str, str] = {}  # filename -> content                    
      scripts: dict[str, str] = {}  # filename -> code                          
                                                                                
      @field_validator('name')                                                  
      @classmethod                                                              
      def validate_name(cls, v: str) -> str:                                    
          if not re.match(r'^[a-z0-9]+(-[a-z0-9]+)*$', v):                      
              raise ValueError('name must be lowercase alphanumeric with        
  hyphens')                                                                     
          return v                                                              
                                                                                
      def render(self) -> str:                                                  
          """Generate valid SKILL.md content."""                                
          ...                                                                   
                                                                                
      def save(self, path: Path) -> None:                                       
          """Write skill directory with all files."""                           
          ...                                                                   
                                                                                
  ---                                                                           
  CLI Interface                                                                 
                                                                                
  # Generate skill from task description                                        
  skillforge generate --task "Parse YAML configs with validation" \             
      --teacher claude-opus-4-5 \                                               
      --student qwen2.5-coder:32b \                                             
      --output ./skills/yaml-parser/                                            
                                                                                
  # Evaluate skill effectiveness                                                
  skillforge eval ./skills/yaml-parser/ \                                       
      --model qwen2.5-coder:32b \                                               
      --test-cases ./tests/yaml-tests.json \                                    
      --baseline                                                                
                                                                                
  # Check for staleness against newer model                                     
  skillforge staleness ./skills/yaml-parser/ \                                  
      --original-model claude-3-5-sonnet-20241022 \                             
      --current-model claude-sonnet-4-20250514                                  
                                                                                
  # Batch regenerate stale skills                                               
  skillforge refresh ./skills/ \                                                
      --model claude-opus-4-5 \                                                 
      --target qwen2.5-coder:32b                                                
                                                                                
  ---                                                                           
  Integration with Claude Code / Agent SDK                                      
                                                                                
  # In your agent setup                                                         
  from skillforge import SkillForge                                             
  from claude_code import Agent                                                 
                                                                                
  forge = SkillForge(teacher="claude-opus-4-5", student="local-model")          
                                                                                
  # Dynamic skill generation during agent execution                             
  @agent.on_task_failure                                                        
  async def generate_skill_on_failure(task: str, error: Exception):             
      """When agent fails, generate skill to help future attempts."""           
      skill = await forge.generate.from_task(                                   
          task=task,                                                            
          failure_context=str(error),                                           
      )                                                                         
      skill.save(Path.home() / ".claude/skills" / skill.name)                   
                                                                                
  ---                                                                           
  Why This Design                                                               
  Your Concern: Skills not transferable across models                           
  How Addressed: metadata.generated_for tracks target model; regenerate per     
  model                                                                         
  ────────────────────────────────────────                                      
  Your Concern: Skills become stale                                             
  How Addressed: staleness detector; refresh command for batch updates          
  ────────────────────────────────────────                                      
  Your Concern: Reliably generate skills                                        
  How Addressed: Iterative generation with verification loop                    
  ────────────────────────────────────────                                      
  Your Concern: Eval with vs without                                            
  How Addressed: Built-in A/B benchmark framework                               
  ────────────────────────────────────────                                      
  Your Concern: Big model → small model                                         
  How Addressed: Explicit distillation pipeline with teacher/student            
  ────────────────────────────────────────                                      
  Your Concern: Unlock local models                                             
  How Addressed: Skills as knowledge transfer from Opus to local                
  ---                                                                           
  Next Steps                                                                    
                                                                                
  Want me to:                                                                   
  1. Scaffold the library - Create the actual Python package structure          
  2. Implement core generation - Start with from_task generator                 
  3. Build evaluation framework - Metrics and benchmarking                      
  4. Design the distillation pipeline - Teacher/student protocol                
                                                                                
  Let me know which direction to prioritize.                                    
                                                                                
✻ Crunched for 1m 57s                                                           
                                                                                
❯ this should not be a python framework. it should be a cli that does           
  things like 'generate skill for x' and then show an eval for the skill.       
  student/teacher is just one use case, not a feature                           
                                                                                
⏺ You're right, I overcomplicated it. Let me simplify.                          
                                                                                
  ---                                                                           
  skillforge CLI                                                                
                                                                                
  A command-line tool for generating and evaluating agent skills.               
                                                                                
  Core Commands                                                                 
                                                                                
  # Generate a skill                                                            
  skillforge generate "parse and validate JSON Schema files"                    
                                                                                
  # Generate with examples                                                      
  skillforge generate "parse JSON Schema" \                                     
    --example "input: schema.json | output: Valid, 3 definitions" \             
    --example "input: bad.json | output: Error line 42: missing 'type'"         
                                                                                
  # Generate skill for an MCP tool                                              
  skillforge generate --from-tool ./mcp-server.json --tool-name "query_database"
                                                                                
  # Evaluate a skill (shows with/without comparison)                            
  skillforge eval ./json-schema-skill/                                          
                                                                                
  # Eval against specific test cases                                            
  skillforge eval ./json-schema-skill/ --tests ./tests.json                     
                                                                                
  # Regenerate a skill (when stale or for different model)                      
  skillforge regen ./json-schema-skill/ --model claude-sonnet-4                 
                                                                                
  # Watch mode: generate, eval, refine until threshold                          
  skillforge generate "parse JSON Schema" --refine --threshold 0.8              
                                                                                
  Output: Generate                                                              
                                                                                
  $ skillforge generate "parse and validate JSON Schema files"                  
                                                                                
  Generating skill...                                                           
                                                                                
  ┌─────────────────────────────────────────────────────────────┐               
  │ json-schema-validator                                       │               
  ├─────────────────────────────────────────────────────────────┤               
  │ Parse and validate JSON Schema files, reporting errors      │               
  │ with line numbers and suggestions for fixes.                │               
  ├─────────────────────────────────────────────────────────────┤               
  │ Files:                                                      │               
  │   SKILL.md              (847 tokens)                        │               
  │   references/ERRORS.md  (312 tokens)                        │               
  │   scripts/validate.py   (not loaded into context)           │               
  └─────────────────────────────────────────────────────────────┘               
                                                                                
  Running eval...                                                               
                                                                                
  ┌─────────────────────────────────────────────────────────────┐               
  │ Eval Results                                                │               
  ├───────────────────┬─────────────────┬───────────────────────┤               
  │                   │ Without Skill   │ With Skill            │               
  ├───────────────────┼─────────────────┼───────────────────────┤               
  │ Success Rate      │ 60%             │ 95% (+35%)            │               
  │ Avg Tokens        │ 2,847           │ 1,203 (-58%)          │               
  │ Avg Turns         │ 4.2             │ 1.8                   │               
  └───────────────────┴─────────────────┴───────────────────────┘               
                                                                                
  Skill saved to ./json-schema-validator/                                       
                                                                                
  Output: Eval                                                                  
                                                                                
  $ skillforge eval ./json-schema-validator/ --tests ./schema-tests.json        
  --verbose                                                                     
                                                                                
  Running 12 test cases...                                                      
                                                                                
  Test 1: valid_simple.json ✓ (with) ✓ (without)                                
  Test 2: missing_type.json ✓ (with) ✗ (without)                                
  Test 3: circular_ref.json ✓ (with) ✗ (without)                                
  Test 4: nested_allOf.json ✓ (with) ✗ (without)                                
  ...                                                                           
                                                                                
  ┌─────────────────────────────────────────────────────────────┐               
  │ Summary                                                     │               
  ├───────────────────┬─────────────────┬───────────────────────┤               
  │                   │ Without Skill   │ With Skill            │               
  ├───────────────────┼─────────────────┼───────────────────────┤               
  │ Passed            │ 7/12 (58%)      │ 11/12 (92%)           │               
  │ Total Tokens      │ 34,164          │ 15,636                │               
  │ Total Cost        │ $0.42           │ $0.19                 │               
  └───────────────────┴─────────────────┴───────────────────────┘               
                                                                                
  Skill lift: +34% success rate, -54% token usage                               
  Recommendation: ✓ Keep this skill                                             
                                                                                
  Config                                                                        
                                                                                
  # ~/.skillforge/config.yaml                                                   
  model: claude-sonnet-4           # default model for generation               
  eval_model: claude-sonnet-4      # model to eval against                      
  auto_eval: true                  # run eval after generate                    
  output_dir: ~/.claude/skills     # where to save skills                       
                                                                                
  Flags                                                                         
                                                                                
  --model, -m        Model to use (default: from config)                        
  --output, -o       Output directory                                           
  --eval-model       Model to evaluate against (can differ from generation      
  model)                                                                        
  --no-eval          Skip automatic eval after generation                       
  --refine           Iterate until eval threshold met                           
  --threshold        Success rate threshold for --refine (default: 0.8)         
  --from-tool        Generate skill from MCP tool schema                        
  --from-trace       Generate skill from agent execution trace file             
  --tests            JSON file with test cases for eval                         
  --verbose, -v      Show detailed output                                       
                                                                                
  ---                                                                           
  Test Case Format                                                              
                                                                                
  {                                                                             
    "cases": [                                                                  
      {                                                                         
        "input": "Validate schema.json",                                        
        "context": {"files": {"schema.json": "{\"type\": \"object\"}"}},        
        "expected": {"contains": "valid", "success": true}                      
      },                                                                        
      {                                                                         
        "input": "Validate broken.json",                                        
        "context": {"files": {"broken.json": "{\"type\": }"}},                  
        "expected": {"contains": "error", "contains": "line"}                   
      }                                                                         
    ]                                                                           
  }                                                                             
                                                                                
  ---                                                                           
  How Generation Works                                                          
                                                                                
  1. Send task + examples to model with meta-prompt asking it to generate a     
  skill                                                                         
  2. Model outputs SKILL.md structure                                           
  3. Parse and validate against spec                                            
  4. Run eval (unless --no-eval)                                                
  5. If --refine: analyze failures, regenerate with feedback, repeat            
                                                                                
  The meta-prompt instructs the model to:                                       
  - Write clear, actionable instructions                                        
  - Include examples and edge cases                                             
  - Structure for progressive disclosure                                        
  - Add scripts for deterministic operations                                    
                                                                                
  ---                                                                           
  Want me to build this?                                                        
                                                                                
✻ Cooked for 35s                                                                
                                                                                
❯ plan more. 1. make tool name optional, and just generate. 2. use one          
  command for gen and regen. 3. change name to upskill                          
                                                                                
⏺ Let me plan this out properly.                                                
                                                                                
  ---                                                                           
  upskill                                                                       
                                                                                
  Generate and evaluate agent skills.                                           
                                                                                
  Commands                                                                      
                                                                                
  upskill generate "task description"    # Generate a skill                     
  upskill eval ./skill/                  # Evaluate a skill                     
  upskill list                           # List generated skills                
                                                                                
  That's it. Three commands.                                                    
                                                                                
  ---                                                                           
  generate                                                                      
                                                                                
  # Basic                                                                       
  upskill generate "parse JSON Schema files with error reporting"               
                                                                                
  # With examples (improves generation quality)                                 
  upskill generate "parse JSON Schema" \                                        
    --example "validate schema.json → Valid schema with 3 definitions" \        
    --example "validate bad.json → Error at line 42: missing 'type'"            
                                                                                
  # From an MCP tool definition                                                 
  upskill generate --tool ./server.json#query_database                          
                                                                                
  # From a recorded agent trace                                                 
  upskill generate --trace ./trace.json                                         
                                                                                
  # Refine until eval passes threshold                                          
  upskill generate "parse JSON Schema" --refine                                 
                                                                                
  # Target a specific model (skill optimized for that model)                    
  upskill generate "parse JSON Schema" --for qwen2.5:32b                        
                                                                                
  # Regenerate existing skill (same command, detects existing)                  
  upskill generate "parse JSON Schema"                                          
  # → Skill 'json-schema-parser' exists. Regenerating...                        
                                                                                
  Behavior:                                                                     
  - Generates skill name from task automatically                                
  - If skill with that name exists, regenerates it (with option to keep old     
  version)                                                                      
  - Runs eval automatically after generation                                    
  - Saves to ~/.config/upskill/skills/ by default                               
                                                                                
  ---                                                                           
  eval                                                                          
                                                                                
  # Eval a skill (compares with vs without)                                     
  upskill eval ./json-schema-parser/                                            
                                                                                
  # Eval with custom test cases                                                 
  upskill eval ./json-schema-parser/ --tests ./tests.json                       
                                                                                
  # Eval against a different model than it was generated for                    
  upskill eval ./json-schema-parser/ --model claude-haiku                       
                                                                                
  # Just show stats, no comparison run                                          
  upskill eval ./json-schema-parser/ --no-baseline                              
                                                                                
  ---                                                                           
  Output Design                                                                 
                                                                                
  $ upskill generate "parse and validate JSON Schema files"                     
                                                                                
  Generating skill...                                                           
                                                                                
    json-schema-validator                                                       
    Parse and validate JSON Schema files, reporting errors with                 
    line numbers and suggestions for fixes.                                     
                                                                                
    SKILL.md              847 tokens                                            
    references/ERRORS.md  312 tokens                                            
    scripts/validate.py   (exec only)                                           
                                                                                
  Evaluating...                                                                 
                                                                                
                      baseline    with skill                                    
    success           60%         95%         +35%                              
    tokens            2,847       1,203       -58%                              
    turns             4.2         1.8         -57%                              
                                                                                
  Saved to ~/.config/upskill/skills/json-schema-validator/                      
                                                                                
  $ upskill generate "parse JSON Schema" --refine                               
                                                                                
  Generating skill... (attempt 1)                                               
  Evaluating... 65% success                                                     
  Refining... (attempt 2)                                                       
  Evaluating... 82% success                                                     
  Refining... (attempt 3)                                                       
  Evaluating... 94% success ✓                                                   
                                                                                
    json-schema-parser                                                          
    ...                                                                         
                                                                                
  Saved to ~/.config/upskill/skills/json-schema-parser/                         
                                                                                
  $ upskill eval ./json-schema-parser/ --tests ./tests.json -v                  
                                                                                
    1. valid_simple.json      ✓ baseline  ✓ skill                               
    2. missing_type.json      ✗ baseline  ✓ skill                               
    3. circular_ref.json      ✗ baseline  ✓ skill                               
    4. nested_allOf.json      ✗ baseline  ✓ skill                               
    ...                                                                         
                                                                                
                      baseline    with skill                                    
    success           7/12        11/12       +33%                              
    tokens            34,164      15,636      -54%                              
    cost              $0.42       $0.19       -55%                              
                                                                                
  Recommendation: keep skill                                                    
                                                                                
  ---                                                                           
  Config                                                                        
                                                                                
  # ~/.config/upskill/config.yaml                                               
  model: claude-sonnet-4        # model for generation                          
  eval_model: claude-sonnet-4   # model for eval (defaults to same)             
  skills_dir: ~/.config/upskill/skills                                          
  auto_eval: true                                                               
  refine_threshold: 0.8                                                         
                                                                                
  ---                                                                           
  Skill Storage                                                                 
                                                                                
  ~/.config/upskill/                                                            
  ├── config.yaml                                                               
  └── skills/                                                                   
      ├── json-schema-parser/                                                   
      │   ├── SKILL.md                                                          
      │   ├── references/                                                       
      │   └── scripts/                                                          
      └── csv-analyzer/                                                         
          └── SKILL.md                                                          
                                                                                
  Skills can be:                                                                
  - Copied to ~/.claude/skills/ for Claude Code                                 
  - Uploaded via API for claude.ai                                              
  - Used directly by any agent that supports the spec                           
                                                                                
  ---                                                                           
  Flags Reference                                                               
                                                                                
  generate                                                                      
  --example, -e     Input → output example (can repeat)                         
  --tool            Generate from MCP tool schema (path#tool_name)              
  --trace           Generate from agent execution trace                         
  --for             Target model to optimize skill for                          
  --refine          Iterate until eval threshold met                            
  --threshold       Success threshold for --refine (default: 0.8)               
  --model, -m       Model to use for generation                                 
  --output, -o      Output directory (overrides config)                         
  --no-eval         Skip automatic eval                                         
                                                                                
  eval                                                                          
  --tests, -t       JSON file with test cases                                   
  --model, -m       Model to evaluate against                                   
  --no-baseline     Skip baseline (without skill) comparison                    
  --verbose, -v     Show per-test results                                       
                                                                                
  ---                                                                           
  Open Questions                                                                
                                                                                
  1. How to handle eval without explicit test cases? Generate synthetic tests   
  from task description? Require tests?                                         
  2. Skill naming collision - if task changes but generates same name, overwrite
   or version?                                                                  
  3. Integration - should upskill install copy to Claude Code skills dir? Or    
  keep manual?                                                                  
  4. Trace format - what format for --trace? Claude Code's format? Generic?     
                                                                                
  ---                                                                           
  Want me to start building, or refine the design further?                      
                                                                                
✻ Crunched for 36s                                                              
                                                                                
❯ replane: 1. lose --for 2. lose --refine and instead just do that with         
  generate. . 3. lose --threshold                                               
                                                                                
⏺ ---                                                                           
  upskill                                                                       
                                                                                
  Generate and evaluate agent skills.                                           
                                                                                
  Commands                                                                      
                                                                                
  upskill generate "task description"    # Generate a skill (refines until good)
  upskill eval ./skill/                  # Evaluate a skill                     
  upskill list                           # List generated skills                
                                                                                
  ---                                                                           
  generate                                                                      
                                                                                
  # Basic - generates, evals, refines automatically                             
  upskill generate "parse JSON Schema files with error reporting"               
                                                                                
  # With examples (improves quality)                                            
  upskill generate "parse JSON Schema" \                                        
    --example "validate schema.json → Valid schema with 3 definitions" \        
    --example "validate bad.json → Error at line 42: missing 'type'"            
                                                                                
  # From an MCP tool definition                                                 
  upskill generate --tool ./server.json#query_database                          
                                                                                
  # From a recorded agent trace                                                 
  upskill generate --trace ./trace.json                                         
                                                                                
  Behavior:                                                                     
  - Generates skill                                                             
  - Evals it (with vs without baseline)                                         
  - If skill doesn't help, refines and tries again                              
  - Stops when skill shows clear improvement or gives up after N attempts       
  - Regenerating existing skill: same command, detects and overwrites           
                                                                                
  ---                                                                           
  eval                                                                          
                                                                                
  upskill eval ./json-schema-parser/                                            
  upskill eval ./json-schema-parser/ --tests ./tests.json                       
  upskill eval ./json-schema-parser/ --model claude-haiku                       
                                                                                
  ---                                                                           
  Output                                                                        
                                                                                
  $ upskill generate "parse and validate JSON Schema files"                     
                                                                                
  Generating skill...                                                           
  Evaluating... 62% → 68% (+6%) not good enough                                 
  Refining...                                                                   
  Evaluating... 62% → 89% (+27%) ✓                                              
                                                                                
    json-schema-validator                                                       
    Parse and validate JSON Schema files, reporting errors with                 
    line numbers and suggestions for fixes.                                     
                                                                                
    SKILL.md              847 tokens                                            
    references/ERRORS.md  312 tokens                                            
    scripts/validate.py   (exec only)                                           
                                                                                
                      baseline    with skill                                    
    success           62%         89%         +27%                              
    tokens            2,847       1,203       -58%                              
                                                                                
  Saved to ~/.config/upskill/skills/json-schema-validator/                      
                                                                                
  $ upskill generate "parse JSON Schema"                                        
                                                                                
  Skill 'json-schema-parser' exists. Regenerating...                            
                                                                                
  Generating skill...                                                           
  Evaluating... 62% → 91% (+29%) ✓                                              
                                                                                
    ...                                                                         
                                                                                
  ---                                                                           
  Flags                                                                         
                                                                                
  generate                                                                      
  --example, -e     Input → output example (can repeat)                         
  --tool            Generate from MCP tool schema (path#tool_name)              
  --trace           Generate from agent execution trace                         
  --model, -m       Model to use for generation                                 
  --output, -o      Output directory                                            
  --no-eval         Skip eval and refinement (just generate once)               
                                                                                
  eval                                                                          
  --tests, -t       Test cases JSON file                                        
  --model, -m       Model to evaluate against                                   
  --no-baseline     Skip baseline comparison                                    
  -v                Verbose per-test output                                     
                                                                                
  ---                                                                           
  Open Questions                                                                
                                                                                
  1. Eval without explicit tests - generate synthetic tests from task? Or       
  require --example flags to double as tests?                                   
  2. When to stop refining - fixed iteration count? Or until improvement        
  plateaus?                                                                     
  3. Trace format - standardize on something or accept multiple formats?        
                                                                                
  ---                                                                           
  Build it?                                                                     
                                                                                
❯ 1. yes generate synthetic tests. 2. plateau. 3. use claude code's format      
                                                                                
⏺ Let me check the current directory and start building.                        
                                                                                
⏺ Bash(ls -la /Users/ben/code/upskill)                                          
  ⎿  total 32                                                                   
     drwxr-xr-x    4 ben  staff    128 Jan 21 07:56 .                           
     drwxr-xr-x  197 ben  staff   6304 Jan 21 07:44 ..                          
     … +2 lines (ctrl+o to expand)                                              
                                                                                
⏺ Bash(ls -la /Users/ben/code/upskill/.claude)                                  
  ⎿  total 8                                                                    
     drwxr-xr-x@ 3 ben  staff   96 Jan 21 07:47 .                               
     drwxr-xr-x  4 ben  staff  128 Jan 21 07:56 ..                              
     -rw-r--r--@ 1 ben  staff  128 Jan 21 07:47 settings.local.json             
                                                                                
⏺ Read(propose-skills.sh)                                                       
  ⎿  Read 482 lines                                                             
                                                                                
⏺ Good context. There's already a bash script for proposing skills from history.
   I'll build upskill as a proper CLI that does generation with eval/refinement.
                                                                                
⏺ Write(pyproject.toml)                                                         
  ⎿  User rejected write to pyproject.toml                                      
     [project]                                                                  
     name = "upskill"                                                           
     version = "0.1.0"                                                          
     description = "Generate and evaluate agent skills"                         
     readme = "README.md"                                                       
     requires-python = ">=3.11"                                                 
     dependencies = [                                                           
         "click>=8.1",                                                          
         "anthropic>=0.40",                                                     
         "pydantic>=2.0",                                                       
     … +18 lines                                                                
                                                                                
⏺ Bash(mkdir -p /Users/ben/code/upskill/src/upskill)                            
  ⎿  Interrupted · What should Claude do instead?                               

