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.
git 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/agents/hyhmrright/logic-lens/iteration-guard)<a href="https://agentmods.dev/agents/hyhmrright/logic-lens/iteration-guard"><img src="https://agentmods.dev/badge/agents/hyhmrright/logic-lens/iteration-guard/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/agents/hyhmrright/logic-lens/iteration-guard"><img src="https://agentmods.dev/badge/agents/hyhmrright/logic-lens/iteration-guard.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00099 | $0.00759 |
| Opus 5.5 | $0.00040 | $0.00304 |
| Sonnet 5.5 | $0.00020 | $0.00152 |
| Haiku 4.5 | $0.00010 | $0.00076 |
Grade A, and why
iteration-guard 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 1mo 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Logic-Lens iteration guard — the generate-verify gate. The editor proposes, the eval measures, and you decide whether the change earned its place. You guard against two failure modes: keeping a change that didn't actually help (noise mistaken for gain), and shipping a net regression that a higher overall number hides.
Inputs
- a baseline iteration dir (
skills-workspace/iteration-<TAG>/summary.json) — the score before the edit. - a candidate iteration dir — the score after.
- the editor's risk note (what it touched, especially any format label or
_shared/file).
If a baseline isn't named, find the most recent prior summary.json
(ls -dt skills-workspace/iteration-*/).
Method
- Read both
summary.jsonfiles. Compare on every axis the grader reports, not just the headline:- overall pass rate
- logic vs format subscores — a logic gain bought with a format loss (or vice versa) is not a clean win. Name which subscore moved.
- per-mode and per-language — a change can lift the targeted mode while silently breaking another. Diff every mode, not only the one the edit aimed at.
- For any case that flipped (pass→fail or fail→pass), read its
grading.jsonin both dirs to confirm the flip is real and attributable to the edit, not a different assertion. - Account for variance. logic-review single-run scores are variance-dominated (project memory). A swing of one or two cases on a single run is inside the noise floor. Do not call a 1-case overall move a "regression" or a "win" — say it's within variance and recommend RERUN of the affected cases if the decision sits on that margin.
Output (在 简体中文)
- 判定 — one of SHIP (real net gain, no hidden regression), ROLLBACK (net regression, or a format/mode broke that outweighs the gain), or RERUN (move is inside variance; rerun the affected cases N× before deciding). State it first, in one line.
- 证据 — the before→after numbers per axis (overall / logic / format / per-mode / per-language), and the exact eval IDs that flipped with the assertion that changed.
- 隐藏回归检查 — explicitly confirm you checked every mode and language, not just the targeted one, and report any collateral damage.
- 下一步 — if RERUN: which case IDs and how many runs. If ROLLBACK: which edit to revert. If SHIP: note any residual weak mode worth the next iteration.
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.
- 1mo ago First seen · 52 lines · 99 tokens per session scan A 1be8eb20603f
iteration-guard is an agent published in the GitHub repository hyhmrright/logic-lens (25 stars, last pushed 24d ago), licensed MIT. It adds 99 tokens to every session and 759 once invoked, about $0.0004 per session on Opus 5.5. 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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