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/overreliancenpx skills add thejefflarson/soundcheck --skill overreliancegit 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 | $0.00060 | $0.00807 |
| Opus 5 | $0.00030 | $0.00404 |
| Sonnet 5 | $0.00012 | $0.00161 |
| Haiku 4.5 | $0.00006 | $0.00081 |
Grade A, and why
overreliance 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overreliance on LLM Output (OWASP LLM09:2025)
What this checks
Prevents systems from treating LLM output as ground truth. LLMs hallucinate, produce confident-sounding errors, and lack real-time knowledge. Acting on unverified output in medical, legal, financial, or deployment contexts can cause serious harm.
Vulnerable patterns
- LLM diagnosis, legal advice, or financial guidance displayed in the UI with no caveat or disclaimer.
- Automated pipeline that merges, deploys, or publishes based solely on an LLM approval signal.
- Confidence threshold defined as a constant but never used to branch behavior — every non-null response is accepted.
- High-stakes domain list defined but never compared against the current request before action.
- No alternate path when the LLM output fails a sanity check or confidence threshold.
Fix immediately
Flag the vulnerable code and explain the risk. Then suggest a fix that establishes these properties. Translate each property into the audited file's language and framework — apply the principles with whatever conditional, logging, and routing primitives the host stack provides.
- Gate on confidence and domain, and the gate must branch. Defining a confidence threshold or a high-stakes domain set without a conditional that actually diverges behavior (review queue versus direct return, proceed versus halt) is the exact bug this skill prevents. The failing branch routes to human review; the passing branch attaches a disclaimer and returns.
- No raw model output reaches the caller. Every return site wraps the content with an "AI-generated — verify before acting" disclaimer or equivalent marker.
- Irreversible actions (merge, deploy, payment, publish) require a human trigger — they are never invoked from the function that consumes the LLM result.
- The audit log captures enough context to reconstruct the decision: the inputs the LLM saw, the output it produced, and the confidence signal. Metadata alone (request id, timestamp, domain) is insufficient — a reviewer cannot second-guess a decision they cannot re-read.
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 · 64 lines · 60 tokens per session scan A 41bdb5c1c50a
overreliance is a skill published in the GitHub repository thejefflarson/soundcheck (20 stars, last pushed 1mo ago), licensed MIT. It adds 60 tokens to every session and 807 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.
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