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/thejefflarson/soundcheck/security-reviewnpx skills add thejefflarson/soundcheck --skill security-reviewgit clone --depth 1 https://github.com/thejefflarson/soundcheckWhat 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 | $0.00037 | $0.01142 |
| Opus 5 | $0.00018 | $0.00571 |
| Sonnet 5 | $0.00007 | $0.00228 |
| Haiku 4.5 | $0.00004 | $0.00114 |
Grade A, and why
security-review 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 3d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Security Audit (A01:2025–A10:2025 + LLM01:2025–LLM10:2025)
What this checks
Full repo audit against OWASP Web + LLM Top 10:2025 via a four-stage
pipeline: threat-model → hotspots → review → validate. Main context
dispatches subagents (threat-modeling, hotspot-mapping,
vulnerability-audit, design-review, finding-validate,
attack-chain-analysis) and renders findings; never reads code.
Vulnerable patterns
This skill is the orchestrator. The actual pattern catalog lives in
the per-category auto-invoking skills (injection, csrf, ssrf,
broken-access-control, authentication-failures, etc.) — the
vulnerability-audit subagent picks the right one per hotspot and
applies its Vulnerable patterns section. Skill-list maintenance
is automatic via .claude/skills/ directory contents; no separate
catalog file.
Procedure
Use only the Agent tool in main context. No Read/Grep/Glob/
Bash in main context. Stage prompts live in .claude/agents/:
threat-modeling, hotspot-mapping, design-review,
vulnerability-audit, finding-validate, attack-chain-analysis.
This skill is just the coordinator.
Copy this checklist as you progress:
- [ ] Stage 0 — threat-modeling returned
- [ ] Stage 1 — hotspot-mapping returned (one whole-repo call)
- [ ] Stages 1b+2 — design-review + N vulnerability-audit in ONE message
- [ ] Stage 2.5 — finding-validate returned; refuted findings dropped
- [ ] Stage 3 — attack-chain-analysis returned
- [ ] Stage 4 — findings table rendered with severity legend
- [ ] Stage 5 — suggested /security-cleanup to the user
Stage 0 — Threat model
Dispatch one threat-modeling subagent. It returns JSON with
purpose, deployment, trusted_inputs, untrusted_inputs. Thread this
into every later subagent.
Stage 1 — Hotspot map
Dispatch one hotspot-mapping subagent with the threat model.
Returns a JSON array of {file, lines, name, category, priority, why}
entries — the hotspot list for Stage 2.
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.
- 3d ago First seen · 115 lines · 37 tokens per session scan A f919e36fef73
security-review is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 1,142 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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