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 skills add hyhmrright/logic-lens --skill logic-reviewgit clone --depth 1 https://github.com/hyhmrright/logic-lensWrote 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/skills/hyhmrright/logic-lens/logic-review)<a href="https://agentmods.dev/skills/hyhmrright/logic-lens/logic-review"><img src="https://agentmods.dev/badge/skills/hyhmrright/logic-lens/logic-review/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/skills/hyhmrright/logic-lens/logic-review"><img src="https://agentmods.dev/badge/skills/hyhmrright/logic-lens/logic-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00161 | $0.03543 |
| Opus 5 | $0.00081 | $0.01772 |
| Sonnet 5 | $0.00032 | $0.00709 |
| Haiku 4.5 | $0.00016 | $0.00354 |
Grade A, and why
logic-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 10d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Logic-Lens — Logic Review
Output Skeleton Contract
The downstream grader (scripts/grade-iteration.py) and other Logic-Lens skills consume this report by substring-matching literal tokens defined in ../_shared/common.md §1 (header map), §2 (mandatory field labels + Logic Score), and ../_shared/report-template.md (skeleton). Paraphrasing those tokens — even with a synonym that reads fine to a human — breaks the contract regardless of analysis quality.
Language selects the token set, not the structure. The two templates below are the same contract in two languages. Emitting English labels into a Chinese report is as much a contract breach as paraphrasing them — it violates common.md §1 (HIGHEST PRIORITY) and fails grading identically. Detect the user's language first, then fill the skeleton with that language's column from the §1 header map.
Three failure modes observed in benchmark that deserve specific callout beyond the general rule:
- Synonym substitution for field labels whose substituted form omits the required substring — replacing
Premises/前提with前置条件构建/前置条件(eval-201), orDivergence/偏差with根因/核心缺陷/结论(eval-252). Each substitution reads fine to a human and may even appear as a section heading or table column, but the substituted word does NOT contain the required substring, so grader and cross-skill consumers see the document as missing the field entirely. Use the literal token fromcommon.md§1; you can still add a descriptive subtitle alongside it. - Demoting a confirmed L-code finding to
### 附加观察（非 Finding）/### Additional observation— if Premises→Trace→Divergence holds, the finding belongs inside## Findings(中文## 发现) with the five literal fields, even at Suggestion severity. This was a recurring cause of eval-279 (quicksort L4) failing on Sonnet runs. - Omitting
Divergence:/偏差：field entirely — the single most frequent failure mode. Many outputs correctly analyze the bug but write the divergence as prose, in a table cell, or under headings like根因,故障点,核心问题,缺陷. TheDivergence:field is the specific label for "the point where actual behavior diverges from the premise." It is NOT optional and has no acceptable synonym. For no-bug findings useDivergence: None — [why the premise holds](中文偏差：无——[原因]).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 99 lines · 161 tokens per session scan A bd91ea6ffdff
logic-review is a skill published in the GitHub repository hyhmrright/logic-lens (22 stars, last pushed 11d ago), licensed MIT. It adds 161 tokens to every session and 3,543 once invoked, about $0.0008 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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