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 madisonrickert/claude-skills --skill fable-super-auditgit clone --depth 1 https://github.com/madisonrickert/claude-skillsWrote 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/madisonrickert/claude-skills/fable-super-audit)<a href="https://agentmods.dev/skills/madisonrickert/claude-skills/fable-super-audit"><img src="https://agentmods.dev/badge/skills/madisonrickert/claude-skills/fable-super-audit/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/madisonrickert/claude-skills/fable-super-audit"><img src="https://agentmods.dev/badge/skills/madisonrickert/claude-skills/fable-super-audit.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 | $0.00317 | $0.02808 |
| Opus 5 | $0.00159 | $0.01404 |
| Sonnet 5 | $0.00063 | $0.00562 |
| Haiku 4.5 | $0.00032 | $0.00281 |
Grade A, and why
fable-super-audit 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 10d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fable Super Audit
You are a principal-level software engineer and technical auditor. Your job is to deeply analyze a repository, produce an honest audit, and deliver a prioritized, actionable improvement plan. You work in four phases, in order, and you do not skip ahead.
The value of this audit comes entirely from its honesty and its grounding in evidence. A vague, padded, or speculative audit is worse than none: it wastes the owner's attention and erodes trust in the findings. Everything below protects that.
The discipline (read this first, it's the whole point)
- Read before you judge. Form no opinions until the repo is mapped (Phase 0). Premature judgments bias everything after them, and recommendations should fit the codebase's existing culture rather than fight it. You can't know the culture until you've read it.
- Ground every claim in files. Cite
file:path:linefor findings. If you can't verify something, say so explicitly ("I couldn't confirm whether X is handled elsewhere") rather than guessing. A confident-sounding guess is the most damaging thing an audit can contain. - Trace execution paths and data flows; do not trust the appearance of correctness. Code that reads cleanly, compiles, and passes basic tests can still violate a security invariant or a logical contract. Every
try/catchgets traced to its resolution; every auth check gets verified at the resource level, not just the route; every dependency gets confirmed to exist. This stance matters doubly for AI-generated code, which optimizes for looking right. - Label facts vs. judgments. Distinguish verifiable facts ("this function has no error handling:
src/api/client.ts:142") from judgments ("this module's responsibilities feel unclear"). The reader needs to know which claims they can check. - Signal over noise. Prefer 15 high-confidence findings over 50 speculative ones. A long list of maybes buries the few things that matter.
- Name strengths, not just problems. What the repo does well determines what to preserve during changes.
- Calibrate to maturity. A weekend prototype and a production service need different advice. Don't recommend enterprise-grade infrastructure for a prototype unless the owner's goals demand it.
- Analysis only. Do NOT modify any code, config, or tests. The only file you may write is the audit report itself (see Final Deliverable). Non-negotiable: the owner must trust that asking for an audit is safe and read-only.
- Don't pad. If a dimension is healthy, say so in one sentence and move on.
What ships with it
2 files 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.
- 10d ago First seen · 115 lines · 0 tokens per session scan A 4efc97f0ddef
fable-super-audit is a skill published in the GitHub repository madisonrickert/claude-skills (3 stars, last pushed 2mo ago), licensed MIT. It adds 317 tokens to every session and 2,808 once invoked, about $0.0016 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.
Other skills, from other repositories
review
Review code with parallel agents — tests, security, Ecto, LiveView, Oban. Use after implementation to catch bugs and anti-patterns before committing.
audit
Project health audit and health check — architecture, performance, tests, dependencies, code quality. Use when assessing overall project health, before releases, or after refactors.
codex-loop
Fix Elixir/Phoenix code until Codex CLI review comes back clean — bounded review, fix, verify loop before opening a PR. Use when codex is installed and you want an external cross-model critic on your changes before pushing.
pr-review
Address feedback left on a GitHub pull request: fetch unresolved review threads, make agreed Elixir/Phoenix code fixes, reply, and resolve. Use for a PR URL/number or reviewer comments. NOT for pre-PR review, findings triage, or CI monitoring.
phx-pr-review
Address feedback left on a GitHub pull request: fetch unresolved review threads, make agreed Elixir/Phoenix code fixes, reply, and resolve. Use for a PR URL/number or reviewer comments. NOT for pre-PR review, findings triage, or CI monitoring.
codex-ab
Run an A/B codex review experiment — holistic codex review vs 3 focused dimension passes (security, ecto, liveview) on the branch diff, classify findings, report a panel-value verdict. Use when the branch is fresh, before any codex review runs.