agentleFS
GabrielHenriqueCAGabrielHenriqueCA/cortex-ai/CLAUDE.md

cortex-ai

CLAUDE.md1 starsChanged 6 months agoMITView on GitHub

Stack: <STACK> Status: <STATUS> Replace the three placeholders above after cloning. Everything below is ready to use. What this project does: [One sentence. What problem does it solve for whom?] What it does NOT do: [Explicit scope boundary — prevents feature creep] Primary consumers: [Who uses this? Internal team / external users / downstream systems] [Add project-specific architecture notes here — key modules, data flow, critical paths] Language / runtime: [e.g., Python 3.12, Node 20, TypeScript strict] Package management: [e.g.,…

The file

We split this CLAUDE.md into the rules it gives an agent

9 rules. About 0 are common, 2 are distinctive, and 7 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 7

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  • could be automatic: a setting could enforce it
  1. Very specific:

    Plan before coding: Use /plan for anything non-trivial.

    line 22
  2. Very specific:

    For vague requests, invoke @product-manager first: No implementation starts without testable acceptance criteria.

    line 24
  3. Very specific:

    ADR for architectural decisions: If a decision is hard to reverse, or future engineers will wonder why it was made, use /adr.

    line 25
  4. Distinctive:

    Use these agents proactively.

    line 69
  5. Very specific:

    Run just init for first-time setup (installs deps, pre-commit hooks, builds Graphify graph)

    line 120
  6. Very specific:

    Run just format before committing — also runs automatically via pre-commit hooks

    line 121
  7. Very specific:

    Run just lint to check all linters without auto-fixing

    line 122
  8. Very specific:

    Run just health to invoke tech-debt-hunter and generate a codebase health report

    line 123
  9. Distinctive:

    Read before editing: Always read a file before modifying it.

    line 135

≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md 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.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
CLAUDE.md

Project: <PROJECT_NAME>

Stack: <STACK> Status: <STATUS>

Replace the three placeholders above after cloning. Everything below is ready to use.


Project Identity

What this project does: [One sentence. What problem does it solve for whom?]

What it does NOT do: [Explicit scope boundary — prevents feature creep]

Primary consumers: [Who uses this? Internal team / external users / downstream systems]


Critical Rules

  1. Plan before coding. Use /plan for anything non-trivial. Coding without a plan produces code that solves the wrong problem correctly.
  2. For complex tasks, invoke @tech-lead first. It decomposes the task, identifies required agents, and parallelizes independent work. Don't route multi-domain problems to a single agent.
  3. For vague requests, invoke @product-manager first. No implementation starts without testable acceptance criteria.
  4. ADR for architectural decisions. If a decision is hard to reverse, or future engineers will wonder why it was made, use /adr. The code does not explain the tradeoffs.
  5. No redundant comments. Comments explain WHY — constraints, workarounds, non-obvious invariants. Comments that restate what the code does are noise and will be flagged in review.
  6. Tests before merging. New behavior requires tests. Refactoring requires existing tests. No exceptions.
  7. English in code, commits, and comments. This codebase is public-facing. All identifiers, commit messages, and inline comments must be in English.

Architecture

  • Full diagram: docs/architecture.md
  • Knowledge graph (read before broad searches): graphify-out/GRAPH_REPORT.md
  • Decision log: docs/adr/
  • Current session state: CONTEXT.md

[Add project-specific architecture notes here — key modules, data flow, critical paths]


Conventions

Language / runtime: [e.g., Python 3.12, Node 20, TypeScript strict] Package management: [e.g., pip + uv, npm, poetry] Formatting: [e.g., ruff + black for Python, prettier for TS/JSON/YAML] Linting: [e.g., ruff, eslint]

Branch naming:

  • feat/<ticket>-<short-description>
  • fix/<ticket>-<description>
  • hotfix/<ticket>-<description> (production only)
  • docs/<description>

Commit style: Conventional Commits — see .claude/skills/commit-style/SKILL.md

PR requirements:

  • Self-reviewed before requesting review
  • No 🔴 BLOCKER issues from code-reviewer
  • Tests added for new behavior
  • CHANGELOG.md updated if user-facing change
  • ADR created if architectural decision was made

Active Subagents

Use these agents proactively. They are specialized and will produce better output than asking the general Claude session.

Leadership Layer (start here for complex work)

TaskAgentCommand
Complex multi-domain tasks — decompose + delegatetech-lead/plan
Vague requests — clarify requirements + acceptance criteriaproduct-manager—

Execution Layer

TaskAgentCommand
Feature design, system decompositionarchitect/plan
Code review, PR quality gatecode-reviewer/review, /ship
Bug investigationdebugger/debug
Code structure improvementrefactor/refactor
Writing teststest-writer—
Data pipelines, SQL, dbtdata-engineer—
ML experiments, model designml-engineer—
React, TypeScript, UIfrontend—
APIs, services, authbackend—
Security reviewsecurity-auditor—
Performance investigationperformance—
Documentationdocs-writer—
CI/CD, Docker, IaC, observabilitydevops—

Quality & Meta Layer

TaskAgentCommand
Full codebase debt audittech-debt-hunter/audit
Pre-release validation + release notesrelease-manager/release
Technology research + comparisonresearch-analyst/research
Rewrite or improve underperforming agentsprompt-engineer—

Second Brain

Obsidian vault: ~/vault/projects/<project-name>/

  • Session logs: synced by /sync-vault
  • Decision copies: decisions/ (mirrors docs/adr/)

Setup guide: docs/obsidian-vault-setup.md


Local Development

  • Run just init for first-time setup (installs deps, pre-commit hooks, builds Graphify graph)
  • Run just format before committing — also runs automatically via pre-commit hooks
  • Run just lint to check all linters without auto-fixing
  • Run just health to invoke tech-debt-hunter and generate a codebase health report
  • Pre-commit hooks enforce: formatting (ruff/prettier/sqlfluff), no secrets (gitleaks), Conventional Commits (commitlint), markdown consistency (markdownlint)

Tool Usage Rules

  1. Before broad file searches → read graphify-out/GRAPH_REPORT.md for architecture context.
  2. For "where is X defined?" → graphify query "X" is faster and smarter than grep.
  3. For cross-module relationships → graphify path "ModuleA" "ModuleB" or graphify explain "concept".
  4. After modifying source files → run graphify update . to keep the graph current.
  5. For SQL questions → apply the sql-review skill before proposing changes.
  6. Read before editing. Always read a file before modifying it. Never assume structure.

Download now (ZIP)

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
What did you use it for? e.g. Reviewing pull requests in a Rails app

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.