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/cobusgreyling/loop-engineering/loop-verifiernpx skills add cobusgreyling/loop-engineering --skill loop-verifiergit clone --depth 1 https://github.com/cobusgreyling/loop-engineeringWhat 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 | $0.00047 | $0.00371 |
| Opus 5 | $0.00023 | $0.00186 |
| Sonnet 5 | $0.00009 | $0.00074 |
| Haiku 4.5 | $0.00005 | $0.00037 |
Grade A, and why
loop-verifier 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 yesterday.
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.
What it actually says
Loop Verifier Skill
You are the checker in a maker/checker split. Your job is to reject unless evidence is strong.
Inputs
- Implementer's proposal summary and diff
- Original issue / CI failure / comment being addressed
- Project test/lint commands
- Allowed file scope (if specified by the loop)
Checklist (all must pass for APPROVE)
- Scope: Only relevant files changed; no denylist paths; no unrelated edits.
- Intent: Change clearly addresses the stated target — not a different problem.
- Tests: You ran tests (or equivalent) and report pass/fail with output snippet.
- No cheating: No disabled tests, skipped assertions, or commented-out checks.
- Risk: For medium+ risk, recommend human review even if tests pass.
Output
## Verdict: APPROVE | REJECT | ESCALATE_HUMAN
### Evidence
- Tests: (command + result)
- Scope check: (pass/fail + notes)
### If REJECT
- Reasons: (numbered, specific)
- Suggested next step for implementer
Rules
- Default stance: REJECT until proven otherwise.
- Do not trust implementer's claim that tests passed — run them.
- If you cannot run tests (env issue) → ESCALATE_HUMAN.
- Be concise. The loop and human read this under time pressure.
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.
- yesterday First seen · 48 lines · 47 tokens per session scan A d8a06f7cb07d
loop-verifier is a skill published in the GitHub repository cobusgreyling/loop-engineering (10,738 stars, last pushed 2d ago), licensed MIT. It adds 47 tokens to every session and 371 once invoked, about $0.0002 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.
Other skills, from other repositories
aw-author
Author, validate, and improve GitHub Agentic Workflow (gh-aw) markdown files. Use when the user wants to create a new workflow, validate an existing workflow, improve a workflow, or debug workflow issues. Triggers on: "aw-author", "agentic workflow", "gh-aw workflow", "workflow markdown", "workflow frontmatter"…
aw-daily
Fully autonomous daily pipeline for the aw-author plugin. Executes intelligence research (web search + GitHub activity queries), posts to Discussions, performs gap analysis against reference files, creates issues, implements changes on develop branch, creates PR, requests review, and auto-merges. Designed for…
gh-aw-report
Daily intelligence reporting for the GitHub Agentic Workflows (gh-aw) ecosystem. Executes 8+ targeted web searches, synthesizes findings into a structured Markdown report, updates the persistent knowledge base, and optionally posts to GitHub Discussions. Triggers on: "aw-report", "gh-aw report", "intelligence sweep"…
hive-maintainer
Discipline for developing Hive itself — PR sizing, review handling, merge discipline, release workflow, delegation, and cleanup hygiene. Use this skill when working on Hive repo changes that span multiple PRs or review cycles.
hive-coordination
Coordinate work across multiple agents and projects in Hive. Covers task claims, blockers, handoffs, campaigns, portfolio management, briefs, and shared memory.
hive-essentials
Hive mental model and orientation. Read this first before using any other Hive skill. Covers the entity hierarchy, observe-and-steer pattern, drivers, sandboxes, console vs CLI, and workspace conventions.