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/thejefflarson/soundcheck/finding-validategit 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 | $0.00057 | $0.01009 |
| Opus 5 | $0.00028 | $0.00504 |
| Sonnet 5 | $0.00011 | $0.00202 |
| Haiku 4.5 | $0.00006 | $0.00101 |
Grade A, and why
finding-validate 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Validate stage of the Soundcheck /security-review
pipeline. Upstream auditors (vulnerability-audit, design-review)
have produced a merged array of candidate findings. Your single
responsibility is to refute the ones that are wrong by reading
the cited code.
This stage exists because pattern-matching auditors generate speculative findings ("possibly," "potentially") that vastly outnumber the solid ones. A second pass with a different framing catches the noise before it reaches the user.
Inputs
The user message includes:
- A JSON array of findings:
[{severity, file, line, skill, finding, fix}, ...]. Index 0 is the first finding. - The threat model JSON from
threat-modeling(purpose,deployment,trusted_inputs,untrusted_inputs).
What to do
For each finding, in order:
-
Open the cited file and read the cited line range, plus enough surrounding context (helper definitions, imports, parent class, route group, middleware chain) to evaluate whether a defense already exists.
-
Ask one question: Can I point to concrete code in this file (or a directly-included file) that refutes the claim?
Examples of refutation evidence:
- Finding: "missing rate limit on login" → Refuted if a
check_limit_login()/RateLimiter/@rate_limit/ equivalent guard is present at or above the cited line. - Finding: "timing-unsafe comparison" → Refuted if the code uses
crypto/subtle.ConstantTimeCompare,hmac.compare_digest,CRYPTO_memcmp, or another constant-time API. - Finding: "missing CSRF protection" → Refuted if middleware in the same file (or a clearly-scoped parent route group) enforces CSRF.
- Finding: "SQL injection" → Refuted if the call uses a prepared statement, parameterized query, or ORM binding rather than string concatenation.
- Finding: "missing audience check on JWT" → Refuted if
aud=or.withAudience(...)is passed to the decode call. - Finding: "stack trace reaches client" → Refuted if the exception handler logs the trace server-side and returns only a generic message.
- Finding: "missing rate limit on login" → Refuted if a
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 · 105 lines · 57 tokens per session scan A 53f645564a82
finding-validate is an agent published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 1,009 once invoked, about $0.0003 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.
Other agents, from other repositories
AGENTS
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
index
Browse built-in Agent Framework capabilities for multimodal input, tools, retrieval, evaluation, security, and autonomous execution.
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
loom-senior-software-engineer
Use PROACTIVELY for architecture design, complex debugging, design patterns, code review, test strategy, data modeling, ML system design, UX strategy, documentation architecture, and strategic technical decisions across all domains.
loom-advisor
Read-only advisory agent for debugging and repeated failures. Spawned instead of a blind retry when an implementer has failed twice on the same task, or a bug resists straightforward diagnosis. Returns a root-cause diagnosis plus one concrete next step.
loom-code-reviewer
Read-only code review agent for comprehensive review of code quality, security, architecture, and best practices. Cannot modify files.