review Skill
Pre-landing PR review. Analyzes diff against main for SQL safety, LLM trust
Want an agent-native computer in the browser? Try HappyCapy.
Cloud sandbox for AI agents · No setup · Run autonomous workflows from your browser
Affiliate link — we may earn a commission at no extra cost to you.
Why use this skill
review is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Codex Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in eclipse-rdf4j/rdf4j, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
--- name: review version: 1.0.0 description: | Pre-landing PR review. Analyzes diff against main for SQL safety, LLM trust boundary violations, conditional side effects, and other structural issues. allowed-tools: - Bash - Read - Edit - Write - Grep - Glob - AskUserQuestion --- # Pre-Landing PR Review You are running the `/review` workflow. Analyze the current branch's diff against main for structural issues that tests don't catch. --- ## Step 1: Check branch 1. Run `git branch --show-current` to get the current branch. 2. If on `main`, output: **"Nothing to review — you're on main or have no changes against main."** and stop. 3. Run `git fetch origin main --quiet && git diff origin/main --stat` to check if there's a diff. If no diff, output the same message and stop. --- ## Step 2: Read the checklist Read `checklist.md`. **If the file cannot be read, STOP and report the error.** Do not proceed without the checklist. --- ## Step 3: Get the diff Fetch the latest main to avoid false positives from a stale local main: ```bash git fetch origin main --quiet ``` Run `git diff origin/main` to get the full diff. This includes both committed and uncommitted changes against the latest main. --- ## Step 4: Two-pass review Apply the checklist against the diff in two passes: 1. **Pass 1 (CRITICAL):** SQL & Data Safety, LLM Output Trust Boundary 2. **Pass 2 (INFORMATIONAL):** Conditional Side Effects, Magic Numbers & String Coupling, Dead Code & Consistency, LLM Prompt Issues, Test Gaps, View/Frontend Follow the output format specified in the checklis ...