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.
git clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/commands/adriannoes/awesome-agentic-ai/autopilot)<a href="https://agentmods.dev/commands/adriannoes/awesome-agentic-ai/autopilot"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/autopilot/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/commands/adriannoes/awesome-agentic-ai/autopilot"><img src="https://agentmods.dev/badge/commands/adriannoes/awesome-agentic-ai/autopilot.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 | $0.00045 | $0.00746 |
| Opus 5 | $0.00023 | $0.00373 |
| Sonnet 5 | $0.00009 | $0.00149 |
| Haiku 4.5 | $0.00005 | $0.00075 |
Grade A, and why
autopilot 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 5d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/autopilot
Autonomous hunt loop with deterministic scope safety and configurable checkpoints.
Usage
/autopilot target.com # default: --paranoid mode
/autopilot target.com --normal # batch checkpoint after validation
/autopilot target.com --yolo # minimal checkpoints (still requires report approval)
/autopilot target.com --quick # fast surface scan, fewer checks, lower token use
/autopilot targets.txt # multiple targets — one domain per line in the file
Session Isolation (Important)
Start a fresh Claude Code session per target. Claude accumulates context across a session — testing multiple targets in one session causes cross-contamination where findings, payloads, and tech stack assumptions from target A bleed into target B.
Best practice:
# Terminal 1: target A
claude → /autopilot targetA.com
# Terminal 2: target B (separate process)
claude → /autopilot targetB.com
If you must test multiple targets in one session, run /pickup target.com at the start of
each target switch to reload the correct context.
Token Optimization
Use --quick for faster, lower-cost scans (skips deep fuzzing and extended nuclei templates):
/autopilot target.com --quick # ~40% fewer tokens, covers main attack surface
/hunt target.com --vuln-class idor # single bug class — lowest token use
For long hunts, run /compact (Claude Code built-in) periodically to compress context
without losing findings.
What This Does
Runs the full hunt cycle without stopping for approval at each step:
1. SCOPE Load and confirm program scope
2. RECON Run recon (or use cached if < 7 days old)
3. RANK Prioritize attack surface (recon-ranker agent)
4. HUNT Test P1 endpoints systematically
5. VALIDATE 7-Question Gate on findings
6. REPORT Draft reports for validated findings
7. CHECKPOINT Present to human for review
Safety Guarantees
- Every URL is checked against the scope allowlist before any request
- Every request is logged to
hunt-memory/audit.jsonl - Reports are NEVER auto-submitted — always requires explicit approval
- PUT/DELETE/PATCH require human approval in --yolo mode (safe methods only)
- Circuit breaker stops hammering if 5 consecutive 403/429/timeout on same host
- Rate limited at 1 req/sec (testing) and 10 req/sec (recon)
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.
- 5d ago First seen · 84 lines · 0 tokens per session scan A 25668438c469
autopilot is a command published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 11d ago), licensed MIT. It adds 45 tokens to every session and 746 once invoked, about $0.0002 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 2026-09-03.
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