create-project-skills

name

create-project-skills

description

Scans an existing codebase and generates project-specific skills that capture inferred conventions such as naming, file organization, framework usage, data access, error handling, and testing style. Writes into the project's chosen skill directory (e.g., `.claude/skills/`, `.agents/skills/`, or a custom path). Use when the user asks to "extract skills from the codebase", "create project skills", "infer project conventions as skills", "codify patterns as skills", or "mine the repo for best practices".

Create Project Skills

Generates one skill per detected convention area in the project's skill directory so future Claude or Codex sessions auto-load them when working in the repo.

Task Tracking

At the start, use update_plan to track each phase, restating any remaining steps of a parent workflow alongside them:

1. Survey codebase
2. Extract patterns in parallel
3. Evaluate patterns
4. Propose skill list
5. Run $create-skill skill

Step 1: Survey Codebase

If $ARGUMENTS specifies paths, scope the scan to those paths; otherwise scan the whole repository.

Build the extraction context:

1. Detect primary languages and frameworks from manifest files (package.json, Cargo.toml, pyproject.toml, go.mod, Package.swift, pom.xml, Gemfile, and others appropriate to the stack).
2. Map the top-level source directory structure and note test directory conventions.
3. Read AGENTS.md, nested AGENTS.md files, and any .cursor/rules or .cursorrules. Note the conventions already documented there. The generated skills must not duplicate them.
4. Determine the target skill directory:
   • Check candidate paths .agents/skills/, .claude/skills/, and a top-level skills/ directory (match case-insensitively so Skills/ or similar non-standard casing is detected too). Resolve symlinks so co-linked paths are treated as one logical location.
   • Use request_user_input to confirm where generated skills should live. Offer up to 3 options: the most likely target directory first, the next-most-likely if there is one, and a free-form path option. Note any symlink alias in the option description. If no Codex skill directory is detected, default the first option to ~/.agents/skills. The user can specify a custom path such as a project-specific directory via the free-form option.
5. In the chosen target directory, list existing skills. For each, record the skill name, the description from SKILL.md frontmatter, and the first ## section heading from the body. These signals feed rename-conflict detection in Step 3.

Output a short text summary of detected stack, top-level layout, chosen target directory, and existing skills before moving on.

Step 2: Extract Patterns in Parallel

Read references/pattern-extractor.md to see the full taxonomy of pattern categories. Decide which categories apply to the detected stack (e.g., drop "Styling and UI" for a backend service, drop "State management" for a static-analysis tool).

Issue one extraction spawn_agent call per applicable category, all in one batch, then collect their results with wait_agent. Do not issue one and await its result before issuing the rest. Each sub-agent inherits the parent model. State the total count explicitly before emitting the batch. Every sub-agent's prompt must direct it to treat the shared working tree and its git index as read-only and to extract by reading and reasoning. HEAD stays where it is: read other refs with git show <ref>:<path> rather than git checkout or git switch. Each agent's prompt must:

• Name its assigned category
• Include the stack summary and directory map from Step 1
• Include the list of conventions already documented in AGENTS.md so duplicates are skipped
• Instruct the agent to read references/pattern-extractor.md as its role brief and return findings in the format defined at the end of that file

Step 3: Evaluate Patterns

Aggregate findings from all agents. For each finding, score three axes:

• Consistency: what share of eligible sites follow the pattern? Drop findings below 30%. Flag findings between 30–70% as "mixed" for Step 4 review.
• Intentionality: does the pattern appear across multiple subsystems and recent commits, or is it isolated? Drop findings confined to a single legacy module unless docs or lint config explicitly mark them as the desired convention.
• Modernity: does the pattern align with current best practices for the stack? Flag patterns that contradict current idioms (e.g., pre-hooks class components in a React codebase also using hooks elsewhere) as "legacy" for Step 4 review.

Group the surviving findings by topic into candidate skills. Each candidate typically covers one category, but related categories may merge if the patterns are tightly coupled. Split a candidate into two skills if its patterns cover clearly distinct sub-topics.

For each candidate skill, produce:

• A proposed name (kebab-case, narrow to the topic, e.g., swift-naming, react-state, api-clients)
• A one-line description with trigger phrases (e.g., "Use when writing or reviewing ...")
• 3–8 concrete convention statements with evidence citations (file:line)
• A Status tag based on disk comparison:
  • New: no skill with that name exists in the target directory.
  • Update: a skill with the same name exists in the target directory. Produce a unified diff against the current SKILL.md body.
  • Rename conflict: an existing skill in the target directory has a name, description, or first-section heading that covers the same topic under a different name. Flag for user decision.

If rename-conflict detection is ambiguous from the Step 1 signals alone, read the existing skill's SKILL.md body and compare convention statements before finalizing the Status tag.

Step 4: Propose Skill List

Output the full proposal as text first, not inside request_user_input. For each candidate skill, show:

• Status tag, proposed name, one-line description
• The 3–8 convention statements with evidence
• For Update status, the unified diff
• For Rename conflict status, the existing skill name and the overlap summary

After all candidates are listed, use request_user_input to confirm the proposal with these options: "Approve all", "Make edits", "Cancel". If the user selects "Make edits", continue in conversation so the user can specify which candidates to drop, merge, or rename before returning here.

For each Rename conflict candidate, use a separate request_user_input asking whether to update the existing skill, create the new one alongside it, or skip. Since creating alongside always establishes a second skill covering the same conventions, present a Get a second opinion option in place of skip, keeping the question at three options. It runs the $consult-claude skill for which resolution leaves the skill set coherent. Then resolve the conflict with that answer in hand, re-asking when the choice stays the user's. A freeform answer that declines the candidate skips it.

Step 5: Run $create-skill Skill

Build the batch from approved candidates only. Do not include anything not explicitly approved in Step 4.

Output all approved candidates (both New and Update status) as text in a single batch. For each candidate, list the Status tag, proposed name, description, target path <target-skill-directory>/<name>/SKILL.md, and the 3–8 convention statements organized under ## <Section> headings with inline evidence citations (file_path:line). These convention statements define the target state the final SKILL.md should match, regardless of whether the skill is being created or updated.

This gives $create-skill everything it needs to skip its Step 1 (usage patterns clearly understood) and Step 2 (project skills typically need no additional reusable resources). For Update candidates, $create-skill also skips its Step 3 (initialization) per its own "skill already exists, iteration needed" skip rule and iterates on the existing SKILL.md in Step 4 until it matches the target convention statements.

Run the $create-skill skill once with this batch in context. Its batch-aware review, evaluation, and apply cycle then runs across all touched skills.

After $create-skill completes, output a summary of created and updated skills, grouped by status. If any candidates were dropped or skipped in Step 4, list them so the user knows what was left out.

Rules

• Each generated skill stays narrow: one topic per skill. Splitting is preferred over bundling.
• Do not duplicate conventions already documented in AGENTS.md. Reference them instead if needed.
• Generated skills must be self-contained: no cross-skill routing, no references to pipelines that invoke them.
• Descriptions must be third-person and include trigger phrases a future agent session would match when working on the topic (e.g., "Use when writing or reviewing ...", "Use when editing ...").