Borrowing it
Nothing to install: this file belongs to stellar-experimental/stellar-raven. 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/stellar-experimental/stellar-raven/main/.agents/skills/audit-reviewability/SKILL.mdgit clone --depth 1 https://github.com/stellar-experimental/stellar-ravenWrote 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/stellar-experimental/stellar-raven/audit-reviewability)<a href="https://agentmods.dev/skills/stellar-experimental/stellar-raven/audit-reviewability"><img src="https://agentmods.dev/badge/skills/stellar-experimental/stellar-raven/audit-reviewability/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/stellar-experimental/stellar-raven/audit-reviewability"><img src="https://agentmods.dev/badge/skills/stellar-experimental/stellar-raven/audit-reviewability.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 | $0.00076 | $0.01443 |
| Opus 5.5 | $0.00030 | $0.00577 |
| Sonnet 5.5 | $0.00015 | $0.00289 |
| Haiku 4.5 | $0.00008 | $0.00144 |
Grade A, and why
audit-reviewability 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 28d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit reviewability
Protect the human attention budget. Judge each artifact by truth, durable value, and review cost. Judge the artifact without guessing whether a human or model wrote it.
Read references/audit-rubric.md completely before classifying
candidates. Its last section explains why each rule exists; read it for disputed rules or skill
maintenance.
Choose the mode and scope
Infer the mode from the request:
- Audit mode: Inspect and report. Keep the repository unchanged.
- Repair mode: Audit, edit, and verify. Use this mode when the user asks to clean, fix, prune, simplify, or repair.
Resolve the narrowest complete scope from the request:
- Use the supplied fixed point for a change review.
- Otherwise, use the supplied files or directories.
- Otherwise, audit the repository when the request names the codebase.
State the selected mode, scope, and fixed point. Ask only when different choices would materially change the result.
Establish the baseline
- Read the applicable repository instructions and the nearest directory instructions.
- Inspect the worktree before editing. Record existing changes and preserve unrelated work.
- Read the product scope, architecture, style guides, generated-file rules, and compatibility policy that govern the target.
- Identify the repository's validation commands and current test baseline.
- Re-read a file immediately before editing it. Assume another human or agent can change it between turns.
Complete this step when the audit has an explicit truth source, compatibility posture, dirty-tree boundary, and verification plan.
Build the candidate inventory
Inspect every in-scope surface that can consume reviewer attention:
- implementation code, names, types, branches, abstractions, and compatibility paths;
- inline comments, docblocks, TODOs, and references from code;
- READMEs, guides, plans, ADRs, changelogs, generated documents, and agent instructions;
- tests, fixtures, snapshots, and assertions;
- the change description and review replies when the user supplies them.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 28d ago Changed 8596c644c895
- 1mo ago First seen · 145 lines · 76 tokens per session scan A 885d3763586e
audit-reviewability is a skill published in the GitHub repository stellar-experimental/stellar-raven (13 stars, last pushed yesterday), licensed Apache-2.0. It adds 76 tokens to every session and 1,443 once invoked, about $0.0003 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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