Borrowing it
Nothing to install: this file belongs to jketreno/clare. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jketreno/clare/main/.codex/skills/autonomy-bootstrap/SKILL.mdgit clone --depth 1 https://github.com/jketreno/clareWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jketreno/clare/autonomy-bootstrap)<a href="https://agentmods.dev/skills/jketreno/clare/autonomy-bootstrap"><img src="https://agentmods.dev/badge/skills/jketreno/clare/autonomy-bootstrap/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jketreno/clare/autonomy-bootstrap"><img src="https://agentmods.dev/badge/skills/jketreno/clare/autonomy-bootstrap.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.01378 |
| Opus 5.5 | $0.00008 | $0.00551 |
| Sonnet 5.5 | $0.00004 | $0.00276 |
| Haiku 4.5 | $0.00002 | $0.00138 |
Grade A, and why
autonomy-bootstrap scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bootstrap CLARE Autonomy Configuration
What this skill does: Guides the user through creating or refining clare/autonomy.yml so AI boundaries and sources of truth match the real project architecture. When to use: Use when adopting CLARE in a new repository, reworking module boundaries, or tightening AI safety zones. Output: A proposed or updated clare/autonomy.yml plus a short checklist of follow-up setup actions.
Context
CLARE's Limited principle requires explicit autonomy boundaries per path. CLARE's Reality-Aligned principle requires sources_of_truth for important domain concepts.
The canonical source for both in a CLARE project is clare/autonomy.yml.
Instructions
When invoked, run this process.
Step 1: Gather project structure and risk profile
- Read the repository tree and identify major modules.
- Ask the user which areas are high-risk or compliance-sensitive.
- Ask which areas are safe for AI iteration and regeneration.
Classify candidate paths into:
- full-autonomy: low-risk utilities, generated code, repetitive glue code
- supervised: most application code requiring review
- humans-only: auth/payment/compliance/safety-critical logic
Decision matrix:
| Path characteristics | Suggested level | Why |
|---|---|---|
| Authentication, payments, legal/compliance workflows, production access controls | humans-only | High impact mistakes require intentional manual authorship |
| Core business logic, API handlers, shared domain models | supervised | AI can draft quickly, but behavior needs human review |
| Generated code, boilerplate wiring, test fixtures, low-risk utilities | full-autonomy | Fast regeneration and iteration are low risk |
Step 2: Draft module boundaries
Draft a modules section with specific path patterns and reasons.
Requirements:
- Use specific paths first, then end with a wildcard default.
- Include a human-readable reason for every entry.
- Keep ambiguous paths out of humans-only until confirmed.
Template:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 204 lines · 19 tokens per session scan A 3acb36f245c5
autonomy-bootstrap is a skill published in the GitHub repository jketreno/clare (5 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 1,378 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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