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 HuntsDesk/ve-kit --skill review-rulesgit clone --depth 1 https://github.com/HuntsDesk/ve-kitWrote 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/huntsdesk/ve-kit/review-rules)<a href="https://agentmods.dev/skills/huntsdesk/ve-kit/review-rules"><img src="https://agentmods.dev/badge/skills/huntsdesk/ve-kit/review-rules/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/huntsdesk/ve-kit/review-rules"><img src="https://agentmods.dev/badge/skills/huntsdesk/ve-kit/review-rules.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 | $0.00071 | $0.01325 |
| Opus 5.5 | $0.00028 | $0.00530 |
| Sonnet 5.5 | $0.00014 | $0.00265 |
| Haiku 4.5 | $0.00007 | $0.00133 |
Grade A, and why
review-rules 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 27d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rules & Core Instructions Review Protocol
Reviews the always-loaded instruction layer that governs every Claude Code session in this repo. Scope:
CLAUDE.md(project root).claude/rules/*.md.claude/settings.jsonhooks config (only to verify rule-hook alignment)
Part of the review-* skill family. Cites ../_shared/review-checklist.md and ../_shared/anthropic-configuration-guide.md.
Owner: docs-manager agent.
When to invoke
- CLAUDE.md approaching 150 lines (lean limit) or > 200 lines
- After significant process changes (new hooks, new RIPER modes, new deployment patterns)
- Quarterly audit
- When new team members or agents repeatedly make mistakes the rules should prevent
Output contract
Board tasks only. Lean summary to main agent.
Step 1 — Inventory
- Read
CLAUDE.md. Record line count, section list, referenced rules/skills/docs/agents. - Read every
.claude/rules/*.md. Record line count, topic, referenced files. - Read
.claude/settings.jsonhooks block. Record which hooks exist and what they enforce.
Step 2 — Size discipline
| Artifact | Target | Hard limit |
|---|---|---|
| CLAUDE.md | < 150 lines | 200 lines |
| Individual rule | < 200 lines | 300 lines |
Flag bloat. Recommend:
- CLAUDE.md bloat → push detail into a new rule or existing rule
- Rule bloat → split by sub-topic or move deep reference to a skill / doc
Step 3 — Staleness
For each rule and for CLAUDE.md, check:
- Deprecated infrastructure references (Supabase Edge Functions, old table names, retired services)
- Domain names and URLs still current ( variants, MIG host IDs, LB IP
<YOUR_LB_IP>) - Model IDs match current Anthropic lineup (Opus 5.5, Fable 5.1, Sonnet 5, Haiku 4.5 —
claude-opus-5-5,claude-fable-5-1); Opus is the agent default - Command references valid (
Enter C -push-dev,Enter C -push,Enter C -all) - Stripe API version current (
2026-04-22.dahliaper CLAUDE.md) - Referenced docs exist (every
[link](path)resolves) - Referenced agents exist in
.claude/agents/ - Referenced skills exist in
.claude/skills/
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.
- 27d ago Changed 7e3a15726440
- 1mo ago Changed 69fd5925a103
- 1mo ago Changed · +14 tokens per session 986b7213932a
- 1mo ago First seen · 132 lines · 57 tokens per session scan A 9fa65d754ec4
review-rules is a skill published in the GitHub repository HuntsDesk/ve-kit (3 stars, last pushed 10d ago), licensed MIT. It adds 71 tokens to every session and 1,325 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
pr-review
Review open PRs two ways - default is a per-PR deep review with severity-tagged findings, inline comments, and a verdict; --survey runs a risk-tiered triage digest of what's safe to merge first.
pr-triage
First-touch triage for external pull requests - verdict, label, and a welcoming comment within minutes of open.
feedback-builder
Point Aeon at a service's /feedback endpoint - pull what agents reported (bugs, missing features), triage and cluster it, then build the best accepted request as a PR for a human to approve.
[REPLACE: SKILL_NAME]
First-touch review of newly opened PRs on [REPLACE: WATCHEDREPO] — verdict + welcoming comment + label.
code-review
Systematic code review for bugs, security, style, and performance.
code-review
Review code for security, quality, and maintainability with structured feedback.