project-skill-audit

name	project-skill-audit
description	Analyze a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

Project Skill Audit

Overview

Audit recurring project workflows before recommending skills. Prefer evidence from memory, rollout summaries, existing repo-local skills, and current repo conventions over generic brainstorming. Recommend updates before new skills when an existing local skill is already close to the needed behavior.

Audit Workflow

1. Map the current project surface first.

   • Identify the repo root.
   • Read the most relevant project guidance first, usually AGENTS.md, README.md, and the current roadmap, execution board, or workflow docs.
   • Capture the repo's validation rules, ownership boundaries, and any required run or test commands.

2. Build the memory and session paths before searching.

   • Resolve the memory base as $CODEX_HOME when set, otherwise default to ~/.codex.
   • Use:
     • memories/MEMORY.md
     • memories/rollout_summaries/
     • sessions/

3. Read past project sessions in the right order.

   • If the current prompt already includes a memory summary, start there.
   • Search MEMORY.md for:
     • repo name
     • repo basename
     • current cwd
     • important module or file names
   • Open only the 1-3 most relevant rollout summaries first.
   • Fall back to raw session JSONL only when summaries are missing exact evidence you need.

4. Scan existing project-local skills before suggesting anything new.

   • Check relative to the repo root:
     • .agents/skills
     • .codex/skills
     • skills
   • Read SKILL.md and agents/openai.yaml when present.

5. Compare current skills against recurring work.

   • Look for repeated:
     • validation sequences
     • failure shields
     • ownership boundaries
     • root-cause categories
     • workflows that repeatedly require the same repo-specific context
   • If a pattern recurs and is not already captured well, treat it as a candidate.

6. Separate new skill from update existing skill.

   • Recommend an update when an existing local skill already covers most of the workflow but has stale triggers, outdated paths, weak guardrails, or incomplete validation instructions.
   • Recommend a new skill only when stretching an existing skill would make it vague or confusing.

7. Check global overlap after reviewing project-local skills.

   • Review $CODEX_HOME/skills and $CODEX_HOME/skills/public only after the local scan.
   • Do not reject a project-local skill just because a global skill exists. Project-specific guardrails can still justify a local specialization.

Session Analysis

Search Memory First

• Search MEMORY.md with rg using the repo name, basename, and cwd.
• Prefer entries that already cite rollout summaries with the same repo path.
• Capture:
  • repeated workflows
  • validation commands
  • failure shields
  • ownership boundaries
  • milestone or roadmap coupling

Open Targeted Rollout Summaries

• Open the most relevant summary files under memories/rollout_summaries/.
• Prefer summaries whose filenames, cwd, or keywords match the current repo.
• Extract:
  • what the user asked for repeatedly
  • what steps kept recurring
  • what broke repeatedly
  • what commands proved correctness
  • what project-specific context had to be rediscovered

Use Raw Sessions Only as a Fallback

• Search raw sessions/ JSONL only if rollout summaries are insufficient.
• Search by:
  • exact cwd
  • repo basename
  • thread ID from a rollout summary
  • specific file paths or commands
• Use raw sessions to recover exact prompts, command sequences, diffs, or failure text, not to replace the summary pass.

Turn Evidence Into Skill Candidates

• A new skill candidate must correspond to a repeated workflow, not just a repeated topic.
• A skill update candidate must correspond to a workflow already covered by a local skill whose triggers, guardrails, or validation instructions no longer match recorded sessions.
• Prefer concrete evidence such as:
  • a validation sequence recurring across multiple sessions
  • repeated ownership confusion across the same subsystems
  • the same script, probe, or context file being rediscovered repeatedly

Recommendation Rules

• Recommend a new skill when:

  • the same repo-specific workflow or failure mode appears multiple times across sessions
  • success depends on project-specific paths, scripts, ownership rules, or validation steps
  • the workflow benefits from strong defaults or failure shields

• Recommend an update when:

  • an existing project-local skill already covers most of the need
  • SKILL.md and agents/openai.yaml drift from each other
  • paths, scripts, validation commands, or milestone references are stale
  • the skill body is too generic to reflect how the project is actually worked on

• Do not recommend a skill when:

  • the pattern is a one-off bug rather than a reusable workflow
  • a generic global skill already fits with no meaningful project-specific additions
  • the workflow has not recurred enough to justify the maintenance cost

What To Scan

• Past sessions and memory:

  • memory summary already in context, if any
  • $CODEX_HOME/memories/MEMORY.md or ~/.codex/memories/MEMORY.md
  • the 1-3 most relevant rollout summaries for the current repo
  • raw $CODEX_HOME/sessions or ~/.codex/sessions JSONL only if summaries are insufficient

• Project-local skill surface:

  • ./.agents/skills/*/SKILL.md
  • ./.agents/skills/*/agents/openai.yaml
  • ./.codex/skills/*/SKILL.md
  • ./skills/*/SKILL.md

• Project conventions:

  • AGENTS.md
  • README.md
  • roadmap, ledger, architecture, or validation docs
  • current worktree or recently touched areas if needed for context

Output Contract

Return a compact audit with these sections:

1. Existing skills

   • List the project-local skills found and the main workflow each one covers.

2. Suggested updates

   • For each update candidate, include:
     • skill name
     • why it is incomplete or stale
     • the highest-value change to make

3. Suggested new skills

   • For each new-skill candidate, include:
     • recommended skill name
     • why it should exist
     • what would trigger it
     • the core workflow it should encode

4. Priority order

   • Rank the top recommendations by expected value.

Naming Guidance

• Prefer short hyphen-case names.
• Use project prefixes for project-local skills when that improves clarity.
• Prefer verb-led or action-oriented names over vague nouns.

Failure Shields

• Do not invent recurring patterns without session or repo evidence.
• Do not recommend duplicate skills when an update to an existing skill would suffice.
• Do not rely on a single memory note if the current repo clearly evolved since then.
• Do not bulk-load all rollout summaries; stay targeted.
• Do not skip rollout summaries and jump straight to raw sessions unless the summaries are insufficient.
• Do not recommend skills from themes alone; recommendations should come from repeated procedures, repeated validation flows, or repeated failure modes.
• Do not confuse a project's current implementation tasks with its reusable skill needs.

Follow-up

If the user asks to actually create or update one of the recommended skills, switch to $skill-creator and implement the chosen skill instead of continuing the audit.