blindspot

Knowledge resilience report

Repository: /private/tmp/cohort10/agent-rules
Generated 2026-05-20 15:39 UTC · window: last 90 days · blindspot v0.2.1

Executive brief

Fragile Resilience 40/100 · F · doc only profile

Business implication Code surface is too small for confident structural claims; treat any concentration signal as informational only.

Executive summary

Establish a successor for Peter Steinberger (steipete@gmail.com)'s work — 1 files would orphan without them

Resilience is **Fragile** (40/100). Weakest dimension: ownership concentration (F). Repo profile: doc-only — very little code surface for this analysis. Treat results with caution.

Risk inventory: 1 file(s) would become orphans if the top contributor leaves.

Top recommended action: Establish a successor for Peter Steinberger (steipete@gmail.com)'s work — 1 files would orphan without them

💡 Want a richer, prose-style executive summary? Add a cloud LLM key (Anthropic or OpenAI) to your scan:
--api-key sk-ant-... --provider anthropic --model claude-sonnet-4-6
or set narrative.api_key in .blindspot.yaml. Without a key, this rule-based narrator is used — deterministic, in-process, no network.

Overview

Engineering Resilience Score

Overall
40/100 F
Fragile

Fragile resilience overall (score 40). Weakest dimension: ownership concentration at 0.

Doc-only Very little code surface — treat results with caution.

Key signals — the six questions

Six concrete questions, each with a one-number answer. This is the report. Each signal opens up below into the files, services and people behind its number — run with --detailed for the architecture deep-dive.

Ownership concentration
No service rests on a single owner
Every service has at least two people who know it.
Single-engineer dependency
1 file orphans if the top contributor leaves
These files would have no confident owner the day that person walks out.
Knowledge decay
No file is critically decayed
Owners are still close to the code they own.
Review depth
No review data (local git only)
Connect a GitHub/Bitbucket remote to measure review depth.
Correction load
Features land without a bugfix tail
Code ships and stays shipped — low rework pressure.
AI-readable context
Repo lacks AI-readable operational context (0/5)
No agent rules (CLAUDE.md), specs, architecture notes / ADRs at the repo root — a new human or AI agent must reverse-engineer the codebase.

Signal details

Each signal above, opened up — the files, services and people behind the headline number. Risk signals are expanded by default; healthy ones stay collapsed.

Ownership concentration — service risk map healthy

Bus factor measures how many people would need to leave before knowledge of a service is critically lost. A bus factor of 1 means a single person carries the service.

ServiceFilesBus factor RiskTop ownerTheir coverage
(root) 1 1 critical Peter Steinberger (steipete@gmail.com) 100%

Files with single ownership

Entirely owned by one person — the most acute knowledge risk.

FileOwnerCoverage
LICENSE Peter Steinberger (steipete@gmail.com) 100%
Single-engineer dependency — departure scenarios risk

For each of the top contributors by aggregate ownership coverage, this is what would happen if they left tomorrow: how many files lose their primary expert, how many become unowned (orphan, top remaining coverage < 30%), and which services take the largest hit. Use this to prioritise pair-work and knowledge transfer.

CRITICAL If Peter Steinberger (steipete@gmail.com) leaves
Files affected
1 / 1
Orphan files
1 (100%)
Avg coverage loss
100%
Most-affected services
  • (root) 1/1 files, 1 orphan, 100% avg loss
Knowledge decay — top concerns healthy

Decay rises when an owner stops touching a file and others have been changing it. The 90-day projection shows the trajectory if nothing changes.

FileTop owner Days since touch Decay score Risk 90-day projection
LICENSE Peter Steinberger (steipete@gmail.com) 23 13% low 37%
Review depth — review lineage healthy
🔌 Review metrics unavailable for this scan. A GitHub remote was detected, but no credentials were available. Enable it once and re-run the scan to unlock rubber-stamp ratio, reviewer diversity, fast-approval detection, and the PR activity mix:
gh auth login                          # uses gh CLI (recommended)
or pass a token directly:
--github-token ghp_...
or add it to .blindspot.yaml:
github:
  token: ghp_...
Correction load — files with a bugfix tail healthy
No correction-load data for this window.
AI-readable context — operational docs coverage risk

Coverage of AI-readable organizational memory — agent rules, specs, prompts, architecture decisions, skills. The repo-root row is what the signal grades; per-service rows are shown for context. This is not an AI-generated-code detector.

Surface Agent rules Specs Prompts Architecture Skills Coverage
(repo) 0%