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 agentmods add skills/hatmanstack/ragstack-lambda/auditnpx skills add HatmanStack/RAGStack-Lambda --skill auditgit clone --depth 1 https://github.com/HatmanStack/RAGStack-LambdaWhat 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 | $0.00031 | $0.03126 |
| Opus 5 | $0.00015 | $0.01563 |
| Sonnet 5 | $0.00006 | $0.00625 |
| Haiku 4.5 | $0.00003 | $0.00313 |
Grade A, and why
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 yesterday.
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 — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit
You coordinate one or more codebase audits. Ask scoping questions one at a time, then run all agents in parallel without further user interaction.
Input
$ARGUMENTS is optional context — specific concerns, repo path, or which audits to run.
Process
Step 1: Select Audits
Ask the user which audits to run. This is always the first and only question in the first message.
Which audits should I run?
A) All three (health + eval + docs)
B) Code evaluation — 12-pillar scoring across 3 lenses
C) Technical debt — audit across 4 vectors
D) Documentation — drift detection across 6 phases
If $ARGUMENTS already specifies which audits (e.g., "/audit all"), skip this question and proceed to Step 2.
Wait for the user's answer before continuing.
Step 2: Ask Follow-Up Questions One at a Time
Based on which audits were selected, ask the relevant scoping questions one per message. Wait for each answer before asking the next.
Start with the universal question, then ask audit-specific questions.
Universal (always ask first):
- Known pain points — gives all auditors a starting hypothesis instead of scanning cold.
Are there parts of the codebase you already know are problematic?
Things that keep breaking, areas you dread touching, modules that slow down every PR.
A) Yes (tell me which areas and what's wrong)
B) No — scan everything with fresh eyes
If eval selected (B or A):
The code evaluation runs 3 evaluator agents in parallel, each scoring 4 pillars (12 total). The scores calibrate to the role level you select.
- Role level — sets the scoring bar. A "Senior" evaluation expects production-hardened patterns; a "Junior" evaluation focuses on fundamentals.
What role level should I evaluate this codebase against?
A) Junior Developer — fundamentals: readability, basic error handling, test presence
B) Mid-Level Developer — patterns: separation of concerns, consistent conventions, test coverage
C) Senior Developer — production: defensive coding, observability, performance awareness, type rigor
D) Staff+ / Principal — systems: architectural coherence, scalability, operational excellence
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.
- yesterday First seen · 363 lines · 31 tokens per session scan A bad42cfd1cb2
audit is a skill published in the GitHub repository HatmanStack/RAGStack-Lambda (25 stars, last pushed 4d ago), licensed Apache-2.0. It adds 31 tokens to every session and 3,126 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.
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