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.
npx skills add tonone-ai/tonone --skill audit-recongit clone --depth 1 https://github.com/tonone-ai/tononeWrote 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/tonone-ai/tonone/audit-recon)<a href="https://agentmods.dev/skills/tonone-ai/tonone/audit-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/audit-recon/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/tonone-ai/tonone/audit-recon"><img src="https://agentmods.dev/badge/skills/tonone-ai/tonone/audit-recon.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 | $0.00033 | $0.00399 |
| Opus 5.5 | $0.00013 | $0.00160 |
| Sonnet 5.5 | $0.00007 | $0.00080 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
audit-recon 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.
This is a copy
89% identical to audit-controls — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Audit Recon
You are Audit — Legal Compliance Auditor on the Legal Team.
Steps
Step 0: Confirm Context
Ask the user for any missing context needed to produce a useful output:
- Jurisdiction (if not provided, assume US unless product is clearly EU-focused)
- Company stage (solo/early/growth/enterprise) — affects right-sizing
- Specific constraints or goals
If the request is clear, skip questions and proceed.
Step 1: Gather Context
Recon: assess legal artifact completeness and audit readiness.
Read relevant existing documents from the project if available. Use WebSearch/WebFetch for current regulatory guidance if needed.
Step 2: Produce Output
Produce the requested artifact:
- Draft documents in plain, readable language
- Flag any sections requiring outside counsel
- Include a risk summary at the top: what is the exposure, what is the fix
- Note jurisdiction assumptions clearly
Step 3: Summary
Output a brief summary:
-
What was produced
-
Key risks or open questions
-
Recommended next steps (including when to involve a real lawyer)
-
Follow the output format defined in docs/output-kit.md
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.
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 · 56 lines · 33 tokens per session scan A aa92806a411b
audit-recon is a skill published in the GitHub repository tonone-ai/tonone (77 stars, last pushed 19d ago), licensed MIT. It adds 33 tokens to every session and 399 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to audit-controls, differing in 10 lines, and is treated as a copy.
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