Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add spruikco/fat-agent-skill/plugin install fat-agentWrote 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/spruikco/fat-agent-skill/fat-audit)<a href="https://agentmods.dev/commands/spruikco/fat-agent-skill/fat-audit"><img src="https://agentmods.dev/badge/commands/spruikco/fat-agent-skill/fat-audit.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.00443 |
| Opus 5 | $0.00015 | $0.00221 |
| Sonnet 5 | $0.00006 | $0.00089 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
fat-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 8d 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.
What it actually says
/fat-audit — Run a FAT Agent Audit
You have been asked to run a FAT Agent (Fix, Audit, Test) audit.
Setup
- Bootstrap dependencies (runs once, skips if already installed):
cd ${CLAUDE_PLUGIN_ROOT} && (command -v uv >/dev/null 2>&1 && uv pip install -q -r pyproject.toml 2>/dev/null || pip install -q matplotlib python-docx python-pptx Pillow 2>/dev/null) && echo "deps ready" - Load the FAT Agent skill instructions from
${CLAUDE_PLUGIN_ROOT}/skills/fat-agent/SKILL.md - If a URL argument was provided, use it as the live URL and skip that question in Phase 0
Workflow
Follow the full FAT Agent workflow:
- Phase 0 — Gather Context — Ask for any missing details (site type, tech stack, hosting platform). If the URL was provided as an argument, skip the URL question.
- Phase 1 — Audit — Run all check categories against the live URL. Use the analysis scripts at
${CLAUDE_PLUGIN_ROOT}/scripts/and reference files at${CLAUDE_PLUGIN_ROOT}/references/as needed. - Phase 2 — Fix — Generate the prioritised FAT Report and offer to fix issues.
- Phase 3 — Test — After fixes are deployed, re-verify and generate the final scorecard and badge.
Scripts
${CLAUDE_PLUGIN_ROOT}/scripts/analyse-html.py— HTML analysis helper${CLAUDE_PLUGIN_ROOT}/scripts/calculate-score.py— Scoring calculator${CLAUDE_PLUGIN_ROOT}/scripts/generate-badge.py— SVG badge generator
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.
- 8d ago First seen · 39 lines · 31 tokens per session scan A 76109340753b
fat-audit is a command published in the GitHub repository spruikco/fat-agent-skill (38 stars, last pushed 21d ago), licensed MIT. It adds 31 tokens to every session and 443 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 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.