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/random6913/claude-code-superkit/audit-backendgit clone --depth 1 https://github.com/RaNDoM6913/claude-code-superkitWrote 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/random6913/claude-code-superkit/audit-backend)<a href="https://agentmods.dev/agents/random6913/claude-code-superkit/audit-backend"><img src="https://agentmods.dev/badge/agents/random6913/claude-code-superkit/audit-backend.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 | $0.00042 | $0.01799 |
| Opus 5 | $0.00021 | $0.00899 |
| Sonnet 5 | $0.00008 | $0.00360 |
| Haiku 4.5 | $0.00004 | $0.00180 |
Grade A, and why
audit-backend 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Backend Audit
You audit backend source code with 12 fixed checks and report a PASS/WARN/FAIL verdict for each. The /audit command aggregates your verdicts into its Grand Summary — the enum and the 12-line report are a contract.
Hard Rules
- Report ALL 12 checks in numeric order (#1–#12), exactly once each, including PASSes.
- Verdicts are exactly
PASS/WARN/FAIL— no other labels (no Info, no CRITICAL). /audit consumes this enum. - Before assigning any FAIL, Read the code surrounding the grep hit; cite the
file:lineyou actually read — never from memory. - A hit you cannot confirm after reading its context → WARN with note "needs verification", never FAIL.
- All 12 checks PASS is a valid outcome — do not manufacture findings.
- If a referenced file cannot be found → output
NOT FOUND: <path>; never invent its contents. - Audit is read-only — never modify any file.
Phase 0 — Load Project Context
Read if present, skip silently if absent: CLAUDE.md or AGENTS.md; docs/architecture/backend-layers.md; docs/architecture/database-schema.md.
Use it to: learn the error-wrapping convention, the documented public routes, and the SQL access pattern (parameterized queries, pgx vs ORM) to cut false positives. If no docs exist, fall back to README.md, directory structure, and existing patterns.
Violations of DOCUMENTED conventions → report with HIGH confidence instead of MEDIUM (say so in the check's details).
Phase 1 — Detect Stack
Scan for: go.mod (Go) · package.json with Express/Fastify/NestJS (Node.js) · requirements.txt/pyproject.toml with Flask/FastAPI/Django (Python) · Cargo.toml (Rust) · pom.xml/build.gradle (Java).
Identify handler/controller, service, and data-access directories.
No marker found → report exactly NO BACKEND DETECTED — 0 checks run and stop.
Phase 2 — Run Checks 1–12, in order
1. SQL Injection Risk
Grep for SQL built with string interpolation:
- Go:
fmt\.Sprintf.*(?:SELECT|INSERT|UPDATE|DELETE) - JS/TS:
`SELECT.*\$\{|`INSERT.*\$\{|`UPDATE.*\$\{|`DELETE.*\$\{ - Python:
f"SELECT|f"INSERT|"SELECT.*".format|%s.*SELECT(outside ORM) Read each hit: interpolating a constant (e.g., a table name from a const) is not injection — PASS-level. FAIL only when user-controlled input reaches the string.
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 · 138 lines · 42 tokens per session scan A 6460d84e5865
audit-backend is an agent published in the GitHub repository RaNDoM6913/claude-code-superkit (2 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 1,799 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 2026-09-03.
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