skill-autonomy-bootstrap

A guided setup process for creating or refining CLARE's autonomy configuration. The configuration assigns file paths to levels of AI independence and records authoritative data sources.

In plain words
What is it for?
It helps inspect a repository, identify sensitive and low-risk areas, classify paths, and draft or update clare/autonomy.yml.
Why use it?
It helps project teams set boundaries that match their architecture and risk instead of leaving agent permissions unclear.

Cursor rule for Cursor

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add rules/jketreno/clare/skill-autonomy-bootstrap
Clone the repo
git clone --depth 1 https://github.com/jketreno/clare

Made for: Cursor.

Per session 7 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,395 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00007 $0.01395
Opus 5 $0.00003 $0.00698
Sonnet 5 $0.00001 $0.00279
Haiku 4.5 $0.00001 $0.00139

Measured 2d ago against content hash ae3a62dc65af, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

skill-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 2d 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.

.cursor/rules/skill-autonomy-bootstrap.mdc · 209 lines

How it starts

The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.


name: autonomy-bootstrap description: "Draft or refine clare/autonomy.yml boundaries and sources_of_truth via guided interview" mode: agent

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

  1. Read the repository tree and identify major modules.
  2. Ask the user which areas are high-risk or compliance-sensitive.
  3. 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.

Read the full file on GitHub · 209 lines

Changes

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.

  1. 2d ago First seen · 209 lines · 7 tokens per session scan A ae3a62dc65af

Subscribe to this mod's changes

skill-autonomy-bootstrap is a cursor rule published in the GitHub repository jketreno/clare (5 stars, last pushed 1mo ago), licensed MIT. It adds 7 tokens to every session and 1,395 once invoked, about $0.0000 per session on Opus 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.