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# Tooling & analysis — guide (generic tier)

Reference notes, design rationale, and negative results (14).

#### 1. Verify trace recording actually landed at the requested level before relying on a run
`generic` · **negative result** · ⚠ _session-derived, unverified_

Recording is NOT on by default and the plumbing breaks silently. Seen: a launcher's --trace-dir flag did not propagate the trace-LEVEL env var (a 'full' run silently logged only 'decisions', no snapshots.jsonl/frames.bin); a CLI --trace-level whose choices were off|events|decisions overwrote GUIDED_BOT_TRACE_LEVEL=full (fix: add 'full' to the choices); the match runner's per-agent --trace-dir subdirs differed from the env-configured dir, so manifests showed end_tick 0 / role unknown / 0-line JSONL while the real traces lived in the runner's per-agent dirs. When a flag and an env var both influence observability, confirm the flag forwards every dimension, clear the traces dir, enable full tracing, and verify the files exist after the run. Decision-only levels also leave gaps exactly where you need data (e.g. the post-meeting movement window) -- raise to full per-frame for the phase under investigation or you re-run blind. Multi-agent local runs spawn duplicate zero-tick trace session dirs (one transient dir per agent connection besides the real per-bot trace) -- filter to the latest non-empty bot_* session and treat zero-tick top-level dirs as garbage. Instrument logs written to .labels/.json only flush on process exit, so killing the container to read them mid-run yields nothing -- log to a live-appended file or read live logs when you need data before teardown.
  <sub>sources: opencode:ses_1fecece4dffe779FmeajgdwhMD, opencode:ses_1ffe75d89ffeHvW4p2ARluHh51, opencode:ses_1ff325156ffei2UiDygaVOZo5O, opencode:ses_20101b42dffeJU331wyDQFEn9W (+4)</sub>

#### 2. Auto-generate the decision-point/branch-ID taxonomy from instrumented call sites
`generic` · tool: `gen_branch_ids`

When an outer-loop harness or trace consumer keys on branch IDs, AUTO-GENERATE the taxonomy with a tool (e.g. gen_branch_ids) that scans the instrumented call sites rather than hand-maintaining it; regenerate after editing any call site and add a CI drift check, because a stale generated table silently breaks trace-keyed analysis.
  <sub>sources: opencode:ses_21ffeb5ffffeA1Pk6sdyHDzwQW, opencode:ses_2202cf626ffenXSjna0lj4fEs2, bitworld/among_them/players/mod_talks/BRANCH_IDS.md, bitworld/among_them/players/modulabot/BRANCH_IDS.md (+1)</sub>

#### 3. Treat a policy critique as an evidence document grounded in code AND trace
`generic` · ⚠ _session-derived, unverified_

Treat any code/behavior critique as an evidence document: ground every claim against BOTH the source code AND runtime evidence (logs/traces), tag observations (from the runtime) distinctly from inferred root causes (from the code), separate validated strengths from inferences, and cite exact source locations (e.g. perception/actors.nim:437-451) with severities.
  <sub>sources: claude-code:91f456a7-41e8-4a89-98e9-44e379160b15</sub>

#### 4. Instrument per-stage timing and disprove the cost theory before fixing a perf problem
`generic` · **negative result** · ⚠ _session-derived, unverified_

Reasoning about cost from code repeatedly misleads. A team capped a brute-force spiral localization from ~800px to 120px to 48px with zero measurable fps change while the real culprit was a 22ms full-frame OCR sweep every interstitial frame (plus a one-time 393ms patch-index build on the first gameplay frame; steady-state 3-18ms). A perf fix with no measurable change is positive evidence the theory is wrong -- stop tuning that knob. Emit a per-frame timing line tagged by phase (perceive/localize/tasks+ocr/A*), and instrument game-time vs wall-time and frame counts ('592 frames in 300s = ~2fps' proved a bot stalling on blocking calls). A hang that reproduces only via the built binary but not under `nim c -r` signals a frame-content/state-dependent slowdown (an unbounded spiral search), not a logic bug -- treat it as 'extremely slow on specific inputs' and bisect by frame.
  <sub>sources: opencode:ses_21af0e707ffeWhoJeouK9agHhn, opencode:ses_21b008642ffeCM6Q48G44aM6k0</sub>

#### 5. Build tooling on demand (when a question recurs), and document it in the same change
`generic`

Build tooling only when a question recurs (asked twice). Do not speculatively scaffold frameworks or import infrastructure from other projects unless it pays for itself immediately, and document any new tooling in the same change that adds it.
  <sub>sources: opencode:ses_21fa102bfffedRhIfKKjobTRkK, alpha_cog/AGENTS.md</sub>

#### 6. Verify a function runs on a live path, and that every config field and new struct has a producer AND a consumer
`generic` · ⚠ _session-derived, unverified_

Before reasoning about whether a function is correct, find its call site and guard -- this can collapse a hard correctness question into 'this code is dead.' Dead code nests inside dead code (computed-but-unused vars inside a never-executed block), so rank bug findings by whether the enclosing block runs at all and don't present inner dead code as 'the bug.' Treat a post-refactor policy as guilty of incomplete wiring: enumerate every new type/blob/struct and confirm each has BOTH a producer and a consumer on a live path. Config-field presence is necessary-but-not-sufficient: trace each field to a use site (Cogs v Clips initial_clips_start / initial_clips_spots were defined but never referenced in events()).
  <sub>sources: opencode:ses_201972733ffexPIP9mq6yEDpqJ, opencode:ses_1f6f88f62ffe6S4x2gevW9UGvE</sub>

#### 7. Orchestrate CLI coding agents as subprocesses; generate docs in dependency order; validate drafts by content not exit code; scope writes to output
`generic` · **negative result** · tool: `cogbase`

When building a pipeline that orchestrates LLM coding agents, invoke the Claude Code and Codex CLIs as subprocesses rather than making direct LLM API calls: the CLIs bring their own memory, tool use, file access, web search, context management, and settings, so you orchestrate agents instead of building an LLM wrapper. Point runners at source file PATHS and instruct them to read what they need rather than inlining/pre-digesting source (inlining all prior context would exhaust the window). Generate interdependent documents in dependency order across sequential stages, passing later runners pointers to earlier docs (later docs like observation decoding are stronger grounded in established docs like the interface contract); docs within one stage generate concurrently. For coverage run two independent runners (Claude and Codex) then a synthesizer that merges drafts and resolves contradictions against the source (~3 LLM calls/doc), with a single-runner mode that promotes the lone draft directly for cheaper debugging. On runner failure retry once then skip the failed doc and its dependents with a warning, but treat a foundational Stage-1 failure as cascading and abort. A dual-runner pipeline only pays off if BOTH runners produce content, so validate per-draft BYTE SIZES and content, not just exit codes: a healthy draft is tens of KB; sub-1KB is almost always error text (one pipeline captured Codex's read-only-sandbox error as 'Draft B' via an _ensure_file_content fallback, adding 3-7 min/document for zero value) -- REJECT drafts that look like errors (too short, contain 'error'/'permission denied', no markdown structure). An output-capture flag and the agent's own Write tool collide: leaving Codex -o draft_file overwrites the real document with the agent's chat message -- when the agent writes files directly, DROP the capture flag and have the runner read the agent-written file first, falling back to stdout only if absent. Scope sub-agent writability to the OUTPUT directory only and keep the source strictly read-only (Codex -s read-only -> -s workspace-write with the output dir declared via --add-dir, confirming the game source is neither -C cwd nor --add-dir). Spot-check generated docs against source by verifying file paths AND line ranges (confirm 'kill strategy at policy_imp.nim:154-174' matches the stated lines). Evaluate the suite along explicit NAMED dimensions (pipeline health, coverage/jurisdiction, accuracy, non-duplication, utility, synthesis quality, prompt-template quality) rather than rubber-stamping length, and when repointing at a NEW game run a fresh from-scratch generation isolated in per-game dirs (rename output/ to output_<game>, clear existing outputs) to prevent contamination.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/designs/guide_v1_design.m, players_checkouts/players/tools/cogbase/README.md, claude-code:7bca307c-7349-423e-90c7-544957a43935</sub>

#### 8. Make generated artifacts structured inputs to later stages via a contract-first front door; avoid hand-coded game-specific derivers
`generic` · **negative result** · tool: `cogbase`

Design an artifact-generation pipeline so generated outputs are STRUCTURED INPUTS to later stages, not just human-readable reports: understanding docs feed generated helper tools, which produce fixtures/traces, which feed downstream code generation. Emit a stable machine-readable handoff (e.g. a contract.json with a versioned schema) alongside Markdown so downstream stages depend on the contract, not on exact wording. Make one classification/understanding stage the canonical front door for a new target, deciding which downstream artifacts are actually needed instead of generating them speculatively, and deprecate redundant tools when responsibilities overlap. Prefer driving code generation with a coding agent over accreting target-specific parsing in the toolkit: hand-coded 'derivers' (Markdown parsers, classifiers, extractors) become rigid and compound per target, and they fight the structured-contract handoff. An auto-generated doc format is an implicit, unenforced contract -- treat it as a primary failure surface: hundreds of lines of regex can pass on test fixtures yet silently break on real generated docs, so add cheap sanity assertions (output size above a floor, counts matching the known shape) before consuming it. Keep a machine-readable contract an OPTIONAL stable handoff, not a HARD input; reserve general-purpose code for glue, validation, packaging, and verification rather than extraction. Shared parsing logic must live in ONE place (duplicated copies drift). Keep reusable toolkit code separate from per-target generated artifacts (under output/<slug>/, never authoritative docs), and verify which target a debug/overlay script was written against before advertising it as shared. When deprecating a tool a successor has not fully replaced, keep it runnable but make every entry point emit a deprecation warning and a banner pointing at the successor, keep its regression tests running, and record any capability gap as a successor requirement.
  <sub>sources: players_checkouts/players/tools/cogbase/docs/artifact_pipeline.md, players_checkouts/players/tools/cogbase/README.md, players_checkouts/players/tools/cogbase/docs/designs/guide_v1_design.m, players_checkouts/players/tools/cogbase/docs/designs/maker_v2_design.m (+5)</sub>

#### 9. Load heavy NLP models off the hot path; don't fall back to crude matching
`generic`

Load a heavy model (e.g. a spaCy NLP model, ~1.5-2s under a hosted quarter-core cap) OFF the hot path on a background daemon thread kicked off at startup, so the load overlaps an idle window and is ready before the first time it is needed; have callers degrade gracefully (no signal from that subsystem) while it loads. When the heavy model is intentionally disabled, do NOT fall back to crude keyword/heuristic matching -- its false positives are exactly the failure mode the heavy model exists to avoid, so a clean 'no signal' is better than a noisy wrong one.
  <sub>sources: personal_labs/crewrift_lab/crewrift/crewborg/design.md</sub>

#### 10. Ground every cited stat in comparative context and avoid fake precision in narrative reports
`generic` · ⚠ _session-derived, unverified_ · _see related: U0887 (other tier)_

In any LLM- or template-generated narrative report, ground every statistic you cite with comparative context (rank, leader/baseline value, margins) or it is meaningless -- '577 tasks' says nothing without 'near the top; leader did 590'. Feed the report generator every stat the underlying reporter computes, not a curated subset, so it can choose the comparison. Cut absurd significant figures ('54.46%' reads as fake precision); round in narrative prose while keeping full precision in the data artifact.
  <sub>sources: codex:019eb326-beb5-7b03-890e-732da6aa6746, claude-code:2e0ca80b-d747-4358-a5dc-650397742760</sub>

#### 11. Localize an injection/config bug to an explicit fault boundary, use timestamps to rule out timing, keep IDs untruncated
`generic` · ⚠ _session-derived, unverified_

Localize an injection/config bug to an explicit FAULT BOUNDARY and let evidence move the diagnosis: walk the chain layer by layer (e.g. upload payload -> config/secrets entry by key name -> dispatcher -> job records -> per-job secret bundle -> the runtime merging it into the process/pod), ruling each segment in or out and re-stating the current boundary when evidence shifts (expect the diagnosis to flip more than once). Use creation/modification timestamps to rule timing in or out (a secret created 19:42 UTC, well before 21:31 failing jobs, kills 'added too late'). Keep privacy discipline: print only key names and boolean presence (FLAG=true), never values, presigned URLs, or env blobs. This kind of debugging is far cheaper when result-inspection tooling exposes a single aggregate stats command and full untruncated IDs -- reconstructing one case took ~100 API calls and a custom script, with truncated UUIDs and inconsistent JSON shapes (bare arrays vs {entries:[...]}) the biggest friction.
  <sub>sources: codex:019ea93a-43b0-7800-b34e-7b3053c46177, claude-code:e5e23b1b-d4da-478d-b010-bb374c477eec</sub>

#### 12. Port for parity first then idiomatic; keep source-parsing verifiers language-scoped
`generic` · ⚠ _session-derived, unverified_

When porting a working component to a new language or platform, treat exact behavioral PARITY as the primary correctness signal first; once parity is green, do a dedicated 'is this idiomatic?' pass, because a port's value is a maintainable component, not a faithful museum copy (e.g. after parity: per-pixel nested loops -> vectorized mask operations, manual OOB index guards -> a single sentinel-filled helper, O(n^2) dedup -> a stamped occupancy mask). Verification ideas that parse the TARGET's source code are language-bound and do not generalize across targets (a Python-AST verifier cannot check a Nim engine), so scope source-parsing verifiers to their language and prefer portable mechanisms (a structured sidecar contract, inline-summary prompting) that work regardless of source language.
  <sub>sources: claude-code:1c7117ad-9bee-4658-8937-e92adbc1a6ae, claude-code:33356b24-de19-46ae-ad01-810557f4d9b1</sub>

#### 13. Keep module orchestration in the module; document and avoid duplicated constants
`generic`

For module swappability, keep a module's orchestration inside that module rather than in a test/harness: the actor-scan pipeline was originally orchestrated in the parity harness, which was fixed by moving a compute_actor_percept entrypoint into the actor module and having its scan procs return their own outputs instead of mutating a shared out-parameter, so rewriting one module touches only that file. When the same constant must be known in two layers (e.g. an action-layer ReportRange of 20px duplicated as ReportRangeLocal in the mode for in-range detection), document that both copies must stay in sync; duplicated tuning values that silently drift are a maintenance hazard. Disabling a subsystem cleanly is easiest when downstream call sites already guard on a single 'running' state flag: if every entry point (submit, receive, drain) no-ops when the worker thread is never started, the minimal disable is to stop SPAWNING the worker at its one entry point rather than touching every call site.
  <sub>sources: players_checkouts/players/archive/players/among_them/coborg/PLAN.md, players_checkouts/players/users/james/personal_cogs/among_them/guided_, claude-code:0bd5f20d-10ad-41ef-8372-ae778cd59000</sub>

#### 14. Assert on semantic content, not framework-rendered exact strings; accept both config types on mismatch
`generic` · **negative result** · ⚠ _session-derived, unverified_

Do not assert on framework- or interpreter-rendered exact strings in tests; assert on semantic/lowercased substring content instead. A coworld submit-CLI test broke on Typer's exact rendered '--league' option text (fix: assert 'league' in output.lower(), and drop an unnecessary HTTP-server fixture), and a test on an exact dataclass repr broke (fix: assert serialization succeeded and contains the dataclass's VALUE, not its repr). When docs and code disagree on a config type, make the surface accept BOTH rather than picking one: a doc described log levels as strings while code used a LogLevel enum, so hardening the helper to accept Logger(level='events') as well as the enum avoided the mismatch biting callers.
  <sub>sources: codex:019e1914-2445-7fa2-af5e-8d3ca4d01c57, codex:019e0383-1db4-7f90-a085-04e6f27b26e5</sub>
