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 suyoumo/ClawProBench --skill review-checklistgit clone --depth 1 https://github.com/suyoumo/ClawProBenchWrote 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/suyoumo/clawprobench/review-checklist)<a href="https://agentmods.dev/skills/suyoumo/clawprobench/review-checklist"><img src="https://agentmods.dev/badge/skills/suyoumo/clawprobench/review-checklist.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 31 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00016 | $0.00627 |
| Opus 5 | $0.00008 | $0.00313 |
| Sonnet 5 | $0.00003 | $0.00125 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
review-checklist 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 8d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Merge Review Checklist
Before merging, verify these items. They represent the most common issues caught by automated code reviewers (Copilot, Gemini) on IronClaw PRs.
Database Operations
- Multi-step DB operations are wrapped in transactions (INSERT+INSERT, UPDATE+DELETE, read-modify-write)
- Both postgres AND libsql backends updated for any new Database trait methods
- Migrations are atomic (SQL execution + version recording in same transaction)
Security & Data Safety
- Tool parameters are redacted via
redact_params()before logging or SSE/WebSocket broadcast - URL validation resolves DNS before checking for private/loopback IPs (anti-SSRF via DNS rebinding)
- Destructive tools have
requires_approval()returningAlwaysorUnlessAutoApproved - Data from worker containers is treated as untrusted (tool domain checks, server-side nesting depth)
- No secrets or credentials in error messages, logs, or SSE events
String Safety
- No byte-index slicing (
&s[..n]) on external/user strings -- useis_char_boundary()orchar_indices() - File extension and media type comparisons are case-insensitive (
.to_ascii_lowercase()before matching) - Path comparisons are case-insensitive where needed (macOS/Windows filesystems)
Trait Wrappers & Decorator Chain
- New
LlmProvidertrait methods are delegated in ALL wrapper types (grepimpl LlmProvider for) - New trait methods are tested through the full decorator/provider chain, not just the base impl
- Default trait method implementations are intentional -- wrappers that silently return defaults are bugs
Tests
- Temporary files/dirs use
tempfilecrate, no hardcoded/tmp/paths - Tests don't mutate global statics without synchronization (use per-test state or
serial_test) - Tests don't make real network requests (use mocks, stubs, or RFC 5737 TEST-NET IPs like 192.0.2.1)
- Test names and comments match actual test behavior and assertions
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.
- 8d ago First seen · 55 lines · 16 tokens per session scan A 7163b398e875
review-checklist is a skill published in the GitHub repository suyoumo/ClawProBench (823 stars, last pushed 13d ago), licensed Apache-2.0. It adds 16 tokens to every session and 627 once invoked, about $0.0001 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
Evaluation
Frames model, prompt, and system evaluation as a reproducible experiment with baselines, datasets, and explicit metrics.
open-code-review-delegate
Delegation mode for open-code-review (OCR). Instead of OCR calling an LLM endpoint, this skill instructs the host agent to perform the code review itself, using OCR only for deterministic engineering: file selection and rule resolution. Use when the host agent should drive the review with its own LLM capabilities.
open-code-review
Performs AI-powered code review on Git changes using the ocr CLI from alibaba/open-code-review. Use when the user asks to review code, review a pull request, review staged/unstaged changes, review a commit, or compare branches for code quality issues. Produces line-level review comments and can automatically apply…
review-prs
Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…
bootstrap-repo-analysis
First-time analysis of a repository with no prior reviewer outcomes. Crawl historical merged-PR review feedback with the gh CLI (plus any preloaded samples), extract the team's review norms, and synthesize the initial per-repo review-style prompt. Use this for a cold-start repo; use continual-learning instead once the…
harness-creator
Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is…