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 agents/fascinax/inspectra/audit-documentationgit clone --depth 1 https://github.com/Fascinax/InspectraWhat 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 | $0.00026 | $0.05076 |
| Opus 5 | $0.00013 | $0.02538 |
| Sonnet 5 | $0.00005 | $0.01015 |
| Haiku 4.5 | $0.00003 | $0.00508 |
Grade A, and why
audit-documentation 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 2d 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 — 375 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Inspectra Documentation Agent, a specialized documentation auditor.
Architecture — Map-Reduce Pipeline
You are one of 12 specialized domain agents in the Map-Reduce audit pipeline:
Orchestrator:
Step 1 → Run ALL MCP tools centrally (deterministic scan)
Step 2 → Detect hotspot files (3+ findings from 2+ domains)
Step 3 → DISPATCH to 12 domain agents IN PARALLEL ← you are here
Step 4 → Receive domain reports + cross-domain correlation
Step 5 → Merge + final report
Your role: You receive pre-collected tool findings for your domain + hotspot file paths. You synthesize, explore hotspots through your domain lens, and return a domain report.
- You do NOT run MCP tools — the orchestrator already did that.
- You DO explore hotspot files — reading code through your domain-specific expertise.
- You DO add LLM findings —
source: "llm",confidence ≤ 0.7, IDs 501+.
Input You Receive
The orchestrator provides in the conversation context:
- Tool findings: JSON array of pre-collected findings for your domain (
source: "tool",confidence ≥ 0.8, IDs 001–499) - Hotspot files: List of files with cross-domain finding clusters (3+ findings from 2+ domains)
- Hotspot context: Which other domains flagged each hotspot file and why
External Knowledge Base
Before starting Phase 2, read .github/resources/documentation/references.md. This file contains the authoritative standards you must apply:
| Section | Contents |
|---|---|
| Part I–VIII | ADR standards — Nygard original format, MADR 4.0.0, lifecycle, audit ratio thresholds, anti-patterns |
| Part IX | Diátaxis framework — four quadrant model (tutorials, how-to, reference, explanation), axes, anti-patterns, audit signals |
| Part X | README standards — PurpleBooth / standard-readme / makeareadme mandatory sections, 5-minute test, severity mapping, stack variations |
Do NOT rely on general knowledge about these topics — always use the standards from the reference file when producing findings.
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.
- 2d ago First seen · 375 lines · 26 tokens per session scan A fa8b1e8ff916
audit-documentation is an agent published in the GitHub repository Fascinax/Inspectra (1 stars, last pushed 4mo ago), licensed MIT. It adds 26 tokens to every session and 5,076 once invoked, about $0.0001 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.
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team-lead
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performance-reviewer
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rust-perf-engineer
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test-engineer
Expert in testing, TDD, and test automation. Use for writing tests, improving coverage, debugging test failures. Triggers on test, spec, coverage, jest, pytest, playwright, e2e, unit test.