Use an agent readiness report to identify and implement improvements that make a repository more agent-friendly. Proposes repo-appropriate fixes for each pillar, then implements them on request. Use after running a readiness report or when a user wants to improve their repo's AI-readiness.
Take a readiness report and turn its gaps into concrete, repo-appropriate fixes.
This skill expects an agent readiness report to already exist — either from
running agent-readiness-report on this repo or provided by the user. If no report
exists, run the readiness report skill first.
Identify every ✗ (missing) feature across all five pillars.
Don't try to fix everything. Pick the 5–10 changes that would help agents the most across all pillars, ranked by impact. Not every pillar needs to be represented — focus on what matters most for this repo. The goal is to meaningfully improve the score, not to hit 74/74.
Ranking heuristic — rank by how directly the change helps an agent complete a coding task in this repo:
A single change that lets an agent build and test the project outranks a 2-for-1 that addresses minor gaps.
Proposals should fit this specific repo:
For each proposal, include:
references/criteria.md to explain what goes wrong for agents without this
feature. Don't just say "improves Agent Instructions" — say what the agent
can't do today and what it'll be able to do after the fix.When the user approves fixes (all or a subset), implement them, then update the readiness report to reflect the new state. Flip each addressed feature from ✗ to ✓ and update the pillar counts and summary. This ensures the next run of this skill won't re-propose fixes that have already been applied.
Follow these rules:
The highest-impact fix is almost always an agent instruction file at the root. A good one includes:
If the repo is a monorepo, also consider per-component instruction files that cover component-specific conventions.
Other common fixes:
.env.example if the project uses environment variablesFocus on what gives agents the fastest signal:
Focus on structure that helps agents understand the process:
Focus on boundaries the agent needs to know:
Focus on reproducibility:
Present proposals like this:
# Agent Onboarding Proposals: {repo name}
Ranked by impact. Implementing all of these would improve:
Agent Instructions (+4), Feedback Loops (+2), Policy & Governance (+1)
1. **Create AGENTS.md** — Agent Instructions
- Include build commands from Makefile, test commands from CI, project structure
- Path: `./AGENTS.md`
- Right now agents have no way to learn this repo's conventions, banned
patterns, or how to build/test — they'll guess and get it wrong.
2. **Add pre-commit hooks** — Feedback Loops
- Configure with ruff (already in pyproject.toml) and mypy
- Path: `./.pre-commit-config.yaml`
- Agents currently don't find out about lint/type errors until CI runs.
Pre-commit hooks catch these in seconds instead of minutes.
3. **Add .env.example** — Agent Instructions, Build & Dev Environment
- Document the 3 env vars referenced in docker-compose.yml
- Path: `./.env.example`
- Agents can't start the dev server without knowing which env vars to set.
They'll either skip setup or hallucinate values.
...
Ready to implement? Reply with "all" or specify which proposals to apply.