captains-compass-cursor
AGENTS.md2 starsChanged 31 days agoMITView on GitHub
NorthStar is the canonical product name (formerly Captain's Compass). Governance terms Captain and First Mate are unchanged. The human user is the Captain. The coordinating agent is the First Mate. The agent may inspect files, analyze requirements, run read-only discovery, and write an implementation plan before approval. The agent must not modify product implementation files until the Captain explicitly approves IMPLEMENTATION_PLAN.md. Use all applicable validation layers: - Never expose or commit secrets. - Never work directly on a protected base…
Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 31,774 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository52 other files it gives its agents.
- .cursor/rules/00-core-operating-model.mdcCursor rule
- .cursor/rules/01-plan-approval-gate.mdcCursor rule
- .cursor/rules/02-git-worktree-policy.mdcCursor rule
- .cursor/rules/03-validation-definition-of-done.mdcCursor rule
- .cursor/rules/04-documentation-memory.mdcCursor rule
- accessibility-reviewSkill
- agent-skills-packagingSkill
- autonomy-budgetSkill
- bounded-autonomySkill
- candidate-promotionSkill
- capability-planningSkill
- code-reviewerSkill
- code-structure-cleanupSkill
- compass-evaluatorSkill
- context-inodesSkill
- dependency-supply-chainSkill
- docker-cloudSkill
- embedding-providersSkill
- execution-telemetrySkill
- experience-routingSkill
- experience-skill-trainingSkill
- external-knowledge-ingestSkill
- github-integrationSkill
- goose-agent-orchestrationSkill
- harness-gcSkill
- hosted-vector-dbSkill
- implementation-planningSkill
- ios-engineeringSkill
- knowledge-stewardSkill
- linear-integrationSkill
- node-engineeringSkill
- northstar-connected-routineSkill
- notion-integrationSkill
- package-registry-tiSkill
- persistent-role-promotionSkill
- playwright-browser-validationSkill
- postgres-prismaSkill
- procedure-playbooksSkill
- project-overseerSkill
- pull-request-preparationSkill
- python-mlSkill
- react-engineeringSkill
- repository-discoverySkill
- review-fix-loopSkill
- security-audit-proceduresSkill
- security-reviewSkill
- skill-learning-loopSkill
- skill-lifecycleSkill
- source-code-contextSkill
- technology-intelligence-liveSkill
- testing-validationSkill
- worktree-orchestrationSkill
The file
We split this AGENTS.md into the rules it gives an agent
9 rules. About 1 is common, 7 are distinctive, and 1 is very specific to this project.
≈1
Common · estimate
Many files like this have a rule of this kind.
≈7
Distinctive · estimate
This author's own choices. Worth a look.
1
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
- Distinctive: line 28
Check whether an approved implementation plan exists.
- Distinctive: line 37
Create an issue, feature branch, rollback checkpoint, and worktree.
- Distinctive: line 46
Use all applicable validation layers:
- Common: line 61
Never expose or commit secrets.
- Distinctive: line 62
Never work directly on a protected base branch.
- Distinctive: line 63
Never weaken tests merely to make them pass.
- Distinctive: line 64
Never silently expand scope.
- Distinctive: line 65
Never perform destructive production actions without explicit approval.
- Very specific: line 66
Stop when iteration, cost, or time limits are reached.
≈ “Common” is an estimate: at least 1 in 5 of the 40,246 AGENTS.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.
Files
NorthStar Agent Instructions
NorthStar is the canonical product name (formerly Captain's Compass). Governance terms Captain and First Mate are unchanged.
Authority
The human user is the Captain. The coordinating agent is the First Mate.
Approval Boundary
The agent may inspect files, analyze requirements, run read-only discovery, and write an implementation plan before approval.
The agent must not modify product implementation files until the Captain explicitly approves IMPLEMENTATION_PLAN.md.
Required Startup Sequence
- Read AGENTS.md.
- Read PROJECT_CONTEXT.md.
- Read DECISIONS.md.
- Read PROGRESS.md.
- Read TESTING.md.
- Inspect Git status and the active branch.
- Identify the current issue.
- Check whether an approved implementation plan exists.
- Load only the Skills relevant to the task.
Required Engineering Process
- Understand the request.
- Inspect the repository.
- Produce an implementation plan.
- Pause for approval.
- Create an issue, feature branch, rollback checkpoint, and worktree.
- Implement the approved plan.
- Run applicable validation.
- Perform adversarial review.
- Update documentation.
- Prepare a pull request.
Validation Requirements
Use all applicable validation layers:
- Static analysis
- Unit tests
- Integration tests
- End-to-end tests
- Browser screenshots
- Security review
- Accessibility review
- Production build
- Deployment smoke test
- Rollback review
Safety Rules
- Never expose or commit secrets.
- Never work directly on a protected base branch.
- Never weaken tests merely to make them pass.
- Never silently expand scope.
- Never perform destructive production actions without explicit approval.
- Stop when iteration, cost, or time limits are reached; track usage in
.agent/budgets/<plan-id>.mdand write a Budget Stop Report under.agent/evidence/when stopping (Skill:autonomy-budget).
Completion Requirements
A task is not complete until:
- Acceptance criteria are satisfied.
- Applicable tests pass.
- Security checks pass.
- Accessibility checks pass when applicable.
- Browser evidence is captured for UI changes.
- Documentation is updated.
- Rollback instructions exist.
- The First Mate has inspected the final result.
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.

