Borrowing it
Nothing to install: this file belongs to carrotly-ai/gluon-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/carrotly-ai/gluon-agent/main/.claude/skills/verify-loop-work/SKILL.mdgit clone --depth 1 https://github.com/carrotly-ai/gluon-agentWrote 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/carrotly-ai/gluon-agent/verify-loop-work)<a href="https://agentmods.dev/skills/carrotly-ai/gluon-agent/verify-loop-work"><img src="https://agentmods.dev/badge/skills/carrotly-ai/gluon-agent/verify-loop-work/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/carrotly-ai/gluon-agent/verify-loop-work"><img src="https://agentmods.dev/badge/skills/carrotly-ai/gluon-agent/verify-loop-work.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 | $0.00076 | $0.00647 |
| Opus 5 | $0.00038 | $0.00324 |
| Sonnet 5 | $0.00015 | $0.00129 |
| Haiku 4.5 | $0.00008 | $0.00065 |
Grade A, and why
verify-loop-work 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 9d 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verifying loop work
You are an iteration inside an autonomous agent loop. No human will review your
work before the next iteration builds on it, so you are the reviewer. A
clean edit, a created file, or "it looks right" is NOT evidence the work is
done. Verify the way a careful teammate would before you finish or call
loop_complete.
Verification has two tiers — do both when they apply:
1. Deterministic gate (the objective's exit code)
If the task or loop declares a verify_cmd, RUN IT YOURSELF and read the exit
code before finishing. Exit 0 is the bar; anything else means the work is not
done — fix the underlying cause and rerun, do not hand back partial work.
If no gate is declared, find the project's own check and run it:
- code change → run the test suite / linter / type-checker the repo already uses
(look in
pyproject.toml,package.json,Makefile, CI config); - a script or CLI change → execute it on a representative input and check the result, not just that it ran;
- data/config change → validate/parse it.
State the command you ran and its result in your summary. "Tests pass" without naming the command is not evidence.
2. Reviewer-grade qualitative check
The gate proves it doesn't break; this proves it's actually right and complete:
- Re-read the acceptance criteria in your task prompt and confirm each one is met — not "started", met.
- Check for collateral damage: did you change anything the task didn't ask for? Are there new warnings, dead code, or half-finished edits?
- For a UI change, verify it the way a user would: open the page, interact with the control, confirm the state change, check the browser console for new errors. (Use browser tooling if available.)
- Confirm your work is committed to the repository at repo-relative paths — work left in a stray directory or an uncommitted worktree is orphaned and will not integrate.
If anything fails
Fix the underlying problem and rerun from step 1. Never report a change as
complete, and never call loop_complete, on partially verified work. If you
cannot make it pass, enqueue a focused follow-up task (with the failing output)
rather than claiming success.
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.
- 9d ago First seen · 58 lines · 76 tokens per session scan A fa473ef5013f
verify-loop-work is a skill published in the GitHub repository carrotly-ai/gluon-agent (11 stars, last pushed 9d ago), licensed MIT. It adds 76 tokens to every session and 647 once invoked, about $0.0004 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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