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/iamk77/skill/gaugenpx skills add IamK77/Skill --skill gaugegit clone --depth 1 https://github.com/IamK77/SkillWrote 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/iamk77/skill/gauge)<a href="https://agentmods.dev/skills/iamk77/skill/gauge"><img src="https://agentmods.dev/badge/skills/iamk77/skill/gauge.svg" alt="Measured on agentmods" 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 | $0.00184 | $0.03531 |
| Opus 5 | $0.00092 | $0.01766 |
| Sonnet 5 | $0.00037 | $0.00706 |
| Haiku 4.5 | $0.00018 | $0.00353 |
Grade A, and why
gauge 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
gauge
!checklist init ${CLAUDE_SKILL_DIR} --force
An agent works in a loop: act → observe a signal → correct. Its effectiveness is bounded by the quality of the signal at each step — it has no intuition about the system, no memory between sessions, and no "feel" that something is subtly wrong. It knows only what the code, the checker, the test, and the logs tell it. So the highest-leverage thing you can do for an agent is make the codebase's feedback clear.
The goal is not to copy a static language's syntax — it is to reproduce the property a good static language delivers: clear feedback. Decompose "clear" into the dimensions you actually engineer for:
- Fast — it returns inside the agent's working loop (on save, on one command), not minutes later and not only in production.
- Local — it points at the exact site (file, line, sub-expression), not "something failed downstream".
- Attributed — it says why (expected X, got Y; this constraint failed), not just "error".
- Deterministic — same input, same signal; a flaky signal is no signal, because the agent can't attribute it.
- Trustworthy — green means good, and absence of a signal means "fine", not "this path was never checked".
- Un-fakeable — a signal the agent can flip green by hand (delete the assertion,
as any, widen the timeout) is noise, not feedback.
No single tool delivers all of this. You assemble clear feedback from several sources, and for each way the code can fail you pick the source that gives the clearest signal cheapest, and push it as far left (early) as it will go. This skill walks a five-stage flight plan and will not fly past a GATE until it is cleared.
Discipline: finish every GATE before the next stage. The checklist tool enforces the order; let it. Commands address stages by name. Recon mode: when this runs as a solo, read-only assessment with no user present (no one to consult at a gate, nothing built yet), the GATEs become a self-check that each stage's failure modes are characterized, and the deliverable is a findings ledger — the six-dimension scoring plus a ranked, scoped fix list — not a built feedback surface. verify <stage> then means "I have diagnosed and ranked this stage's gap," not "I stood the source up."
Read references/agent-feedback-shifts.md first — why feedback quality is the lever for an agent specifically, and why un-fakeable is not optional (an agent games a signal the way it games any gate). Match effort to risk — richly instrumenting a trivial helper is the same waste as leaving a payments path opaque.
Match the reader's fluency. Much of this skill's vocabulary is unavoidably technical (Any, a discriminated union, boundary parsing, mutation testing), and with a fluent engineer that register is correct. But the strictness-by-risk dial and the recon findings ledger are set with whoever owns the code, and not every owner is fluent — read it from how they talk, and gloss a term on first use when they are not. A dial nobody but you understood is a strictness budget one party set; its whole point is a shared call on where rigor is worth its friction.
What ships with it
9 files 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.
- 3d ago First seen · 141 lines · 184 tokens per session scan A 77c686d7b111
gauge is a skill published in the GitHub repository IamK77/Skill (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 184 tokens to every session and 3,531 once invoked, about $0.0009 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 2026-08-31.
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