context-docs
Skill6 starsChanged 4 months agoApache-2.0View on GitHub
Use when a subsystem needs persistent context for future agents or engineers -- its boundaries keep being rediscovered the hard way, invariants keep getting violated, or sessions repeatedly ask the same questions about it.
2 other projects use a version of this.
Adopted by
Most recently taken up by
- jaredchu/context-docstheir own version, first seen Oct 20, 2026
- theKashey/skillstheir own version, first seen Jul 28, 2026
Other versions2 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 2
- jaredchu/context-docs★ 188same name
- theKashey/skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository116 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- skills/graphify/references/CLAUDE.mdCLAUDE.md
- agent-config-security-auditSkill
- agent-harness-designSkill
- agent-readable-docsSkill
- agent-runtime-patternsSkill
- agent-session-forensicsSkill
- ai-regression-testingSkill
- ai-slop-cleanerSkill
- api-doc-reviewSkill
- architecture-decision-recordsSkill
- architecture-simplificationSkill
- ask-claudeSkill
- ask-codexSkill
- ask-geminiSkill
- autonomous-delivery-pipelineSkill
- baseline-comparison-reviewSkill
- bigquery-cost-auditSkill
- brainstormingSkill
- browser-automation-safetySkill
- bug-huntSkill
- build-fix-minimal-diffSkill
- canonical-advisor-routingSkill
- change-impact-checkSkill
- changelog-generatorSkill
- ci-failure-readoutSkill
- codebase-migration-batchesSkill
- context-compressionSkill
- context-minimizationSkill
- context-retrieval-loopSkill
- data-qualitySkill
- dataset-split-reviewSkill
- dbt-incremental-strategy-auditSkill
- dbt-preflightSkill
- dbt-strategySkill
- dead-code-removalSkill
- defuddleSkill
- dependency-cleanupSkill
- deploy-pipeline-runbookSkill
- dispatching-parallel-agentsSkill
- doc-consistency-checkSkill
- environment-doctorSkill
- experiment-tracking-reviewSkill
- failure-triageSkill
- filesystem-mcp-guardrailsSkill
- finishing-a-development-branchSkill
- flaky-test-investigationSkill
- gh-address-commentsSkill
- github-ci-failure-triageSkill
- graphifySkill
- handoff-disciplineSkill
- hard-won-skill-extractorSkill
- hook-safety-reviewSkill
- incident-readoutSkill
- incremental-migrationSkill
- json-canvasSkill
- local-rag-mcpSkill
- log-driven-diagnosisSkill
- managed-codebase-contextSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 14
After a refactor changed how a module communicates with the rest of the app
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Purpose
Large centralized documentation files consume too much context window. Decentralized, module-specific context docs provide targeted information exactly when an agent needs it.
When to Use
- A module's boundaries or invariants keep being rediscovered the hard way
- Onboarding or agent sessions repeatedly ask the same questions about a subsystem
- After a refactor changed how a module communicates with the rest of the app
Inputs
- The subsystem directory and its public interface
- Existing docs, to avoid duplicating the central README or architecture docs
Workflow
Decide where the context belongs. Module-specific knowledge goes in a context file inside the subsystem directory (e.g.,
src/auth/CONTEXT.md); repo-wide knowledge belongs in the central README or architecture docs — one home per fact, never both:find src -iname "CONTEXT.md" -o -iname "README.md" # existing context docs rg -ln "<module name>" docs/ # already documented centrally?- Document only the boundaries. How does this module communicate with the rest of the app? What are its critical invariants? Not implementation detail the code already states.
- Keep it terse. Bullet points and exact file paths. No transient notes — session state, TODO lists, and progress logs belong in handoff documents, not context docs.
Validate every path the doc references:
rg -no "src/[A-Za-z0-9_./-]+" src/auth/CONTEXT.md | sort -u # referenced paths test -f src/auth/session.ts && echo OK # confirm each exists- Update these files inline when refactoring the module. A context doc that lags the refactor is worse than none.
Output
- A short, boundary-focused context file inside the subsystem directory
- Stale or central-doc-duplicating content removed or relocated
Verification
- Doc lives inside the subsystem it describes
- Content is boundaries and invariants, not implementation detail or transient notes
- Every referenced file path verified to exist
- No duplication of central docs — one home per fact
- Doc updated in the same change set as the refactor that affected it
Failure Modes
- Context dumping — pasting session notes or progress logs into a context doc; that is a handoff, not context.
- Central duplication — the same fact in the central README and the module doc, guaranteed to diverge.
- Path rot — referenced files moved since the doc was written; validate paths on every touch.
- Novel-length context — if it doesn't fit on a screen, agents won't load it when they need it.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

