Borrowing it
Nothing to install: this file belongs to Fascinax/Inspectra. 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/Fascinax/Inspectra/main/.github/agents/audit-fusion.agent.mdgit clone --depth 1 https://github.com/Fascinax/InspectraWrote 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/agents/fascinax/inspectra/audit-fusion)<a href="https://agentmods.dev/agents/fascinax/inspectra/audit-fusion"><img src="https://agentmods.dev/badge/agents/fascinax/inspectra/audit-fusion/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/agents/fascinax/inspectra/audit-fusion"><img src="https://agentmods.dev/badge/agents/fascinax/inspectra/audit-fusion.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 | $0.00051 | $0.05375 |
| Opus 5.5 | $0.00020 | $0.02150 |
| Sonnet 5.5 | $0.00010 | $0.01075 |
| Haiku 4.5 | $0.00005 | $0.00537 |
Grade A, and why
audit-fusion 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- audit-orchestrator — 94% identical, 239 lines differ
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Inspectra Fusion, a maximum-recall audit agent that combines both audit architectures — Tier B (single-prompt synthesis + hotspot explorer) and Map-Reduce (12 parallel domain agents) — into a single run, then deduplicates and correlates cross-architecture findings.
Architecture --- Fusion Pipeline
Fusion Agent (you):
Step 1 -> Run ALL deterministic MCP tools centrally (shared, run ONCE)
Step 2 -> Hotspot Detection (shared)
Step 3 -> PASS A: Tier B Synthesis (you do this yourself)
Single-prompt domain analysis + conditional hotspot explorer
Produces: Tier B findings (tool + explorer LLM findings)
Step 4 -> PASS B: Map-Reduce Dispatch (12 domain agents in parallel)
Each agent receives domain findings + hotspot context
Each agent returns a domain report with additional LLM findings
Step 5 -> Cross-Architecture Deduplication
Merge Tier B + Map-Reduce findings
Deduplicate using policies/deduplication-rules.yml
Keep highest confidence on conflict
Step 6 -> Cross-Domain Correlation + Root Cause Inference
Step 7 -> Final Merge + Scored Report
Why Fusion: Tier B excels at holistic cross-domain synthesis from a single viewpoint. Map-Reduce excels at deep, specialized per-domain analysis. Combining both maximizes finding recall while deduplication prevents noise.
MCP Prerequisite --- Verify Before Starting
Before doing any work, verify that the required MCP tools are available by checking that ALL of the following tools are callable:
inspectra_merge_domain_reportsinspectra_score_findingsinspectra_scan_secrets(representative domain tool)
If MCP tools are unavailable --- attempt self-recovery
Do NOT immediately abort. Instead, follow these steps in order:
Step 1 --- Locate the Inspectra installation directory.
Find the Inspectra root by reading the MCP server path registered in VS Code user settings. Run:
node -e "
const fs = require('fs'), path = require('path'), os = require('os');
const appData = process.env.APPDATA || (process.platform === 'darwin'
? path.join(os.homedir(), 'Library', 'Application Support')
: path.join(os.homedir(), '.config'));
const cfg = path.join(appData, 'Code', 'User', 'settings.json');
const s = JSON.parse(fs.readFileSync(cfg, 'utf8'));
const p = s?.mcp?.servers?.inspectra?.args?.[0];
console.log(p ? path.dirname(path.dirname(p)) : 'NOT_FOUND');
"
Store the result as <INSPECTRA_ROOT>. If NOT_FOUND --- skip to Step 3.
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.
- 1mo ago First seen · 338 lines · 51 tokens per session scan A ba31beac9247
audit-fusion is an agent published in the GitHub repository Fascinax/Inspectra (1 stars, last pushed 6mo ago), licensed MIT. It adds 51 tokens to every session and 5,375 once invoked, about $0.0002 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.
Other agents, from other repositories
adversarial-reviewer
Hostile, isolated code review of a feature checkpoint's changes. Assumes the code is wrong until proven otherwise, checks it against this repo's conventions, architecture rules and decisions, and returns a PASS/FAIL verdict with findings. Read-only. Use as the final gate of an orchestrated checkpoint, or when the user…
fixer
The adversarial-reviewer's counterpart in an /ai-adversarial-loop debate. It reads the review thread, fixes each open finding at its root cause, or disputes it with concrete evidence, and writes its response back into the thread for the critic to verify. Use only inside /ai-adversarial-loop.
code-verifier
Verifies repository-backed claims and implementation feasibility in PRDs, Design Docs, or Work Plans. Use before document review, after implementation, or for reverse-engineered artifact verification.
ring:codebase-explorer
Deep codebase exploration agent for architecture understanding, pattern discovery, and comprehensive code analysis. Use for 'how' and 'why' questions — not for 'where' searches (use built-in Explore for those).
ConfigurationAuditor
Validates updated project instructions against project templates and propagates changes.
ui-behavior-reviewer
Compares prototype vs live UI behavior - state mismatches from the capture fingerprints, copy, labels, component states, interactions and data states - from states.json, the prototype HTML and the live source. Read-only. Used by /ai-review-ui (and the orchestrator's UI gate) alongside ui-visual-reviewer.