Hierarchical AGENTS.md infrastructure so agents navigate codebases like senior engineers.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionintent-layerExecute the skills CLI command in your project's root directory to begin installation:
Fetches intent-layer from crafter-station/skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate intent-layer. Access via /intent-layer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Hierarchical AGENTS.md infrastructure so agents navigate codebases like senior engineers.
Only ONE root context file. CLAUDE.md and AGENTS.md should NOT coexist at project root. Child AGENTS.md in subdirectories are encouraged for complex subsystems.
1. Detect state
scripts/detect_state.sh /path/to/project
→ Returns: none | partial | complete
2. Route
none/partial → Initial setup (steps 3-5)
complete → Maintenance (step 6)
3. Measure [gate - show table first]
scripts/analyze_structure.sh /path/to/project
scripts/estimate_tokens.sh /path/to/each/source/dir
4. Decide
No root file → Ask: CLAUDE.md or AGENTS.md?
Has root file → Add Intent Layer section + child nodes if needed
5. Execute
Use references/templates.md for structure
Use references/node-examples.md for real-world patterns
Validate: one root, READ-FIRST directive, <4k tokens per node
6. Maintenance mode (when state=complete)
Ask user:
a) Audit nodes → Use references/capture-protocol.md for SME questions
b) Find candidates → Re-measure tokens, suggest new nodes
c) Both
| Signal | Action |
|---|---|
| >20k tokens in directory | Create AGENTS.md |
| Responsibility shift | Create AGENTS.md |
| Hidden contracts/invariants | Document in nearest ancestor |
| Cross-cutting concern | Place at LCA |
Do NOT create for: every directory, simple utilities, test folders (unless complex).
When documenting existing code, ask:
Scripts:
scripts/detect_state.sh - Check Intent Layer state (none/partial/complete)scripts/analyze_structure.sh - Find semantic boundariesscripts/estimate_tokens.sh - Measure directory complexityReferences:
references/templates.md - Root and child node templatesreferences/node-examples.md - Real-world examplesreferences/capture-protocol.md - SME interview protocolMake data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Useful defaults in intent-layer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
intent-layer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for intent-layer matched our evaluation — installs cleanly and behaves as described in the markdown.
We added intent-layer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend intent-layer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
intent-layer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
intent-layer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend intent-layer for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
intent-layer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: intent-layer is focused, and the summary matches what you get after install.
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