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/openhands/extensions/github-pr-reviewernpx skills add OpenHands/extensions --skill github-pr-reviewergit clone --depth 1 https://github.com/OpenHands/extensionsWhat 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 | $0.00059 | $0.03273 |
| Opus 5 | $0.00030 | $0.01636 |
| Sonnet 5 | $0.00012 | $0.00655 |
| Haiku 4.5 | $0.00006 | $0.00327 |
Grade A, and why
github-pr-reviewer scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://api.github.com/user \ How it starts
The opening of the file, as written. The whole thing — 316 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub PR Reviewer Automation
Create a cron automation that watches one or more GitHub repositories for pull
requests with a review trigger label, starts an OpenHands review conversation
once per label event, and publishes the AI review to GitHub.
Windows PowerShell equivalents for the setup, packaging, upload, and API-check shell snippets are in references/windows.md.
The automation script is deterministic: PR discovery, label-event tracking, state persistence, stale-result suppression, the repository checkout, and its removal are all handled in Python. The LLM is invoked only for the review itself.
The script prepares each review's workspace before the agent starts: the pull request's head commit is downloaded as a tarball and extracted to a directory of its own, which becomes the conversation's working directory. The agent is told not to clone, fetch, check out, or delete anything, and the script removes the checkout once the conversation has stopped. Nothing accumulates between runs.
Prerequisites
Required secret
Verify that the following secret is set in OpenHands Settings -> Secrets:
| Secret name | Token type | Minimum permissions |
|---|---|---|
GITHUB_PERSONAL_ACCESS_TOKEN |
Classic PAT | repo for private repos or public_repo for public repos |
GITHUB_PERSONAL_ACCESS_TOKEN |
Fine-grained PAT | Contents: Read, Metadata: Read, Pull requests: Read and Write, Issues: Read and Write |
Pull-request write access is required because the agent publishes a pull request review, not just an issue comment. A token with only Pull requests: Read will poll happily and then fail at the point of publishing.
When several repositories are monitored, the token must cover all of them.
Check with:
curl -s https://api.github.com/user \
-H "Authorization: Bearer $GITHUB_PERSONAL_ACCESS_TOKEN" \
| python3 -c "import json,sys; d=json.load(sys.stdin); print(d.get('login') or d.get('message'))"
If the token is missing or invalid, inform the user and stop.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 316 lines · 59 tokens per session scan A 73d743013473
github-pr-reviewer is a skill published in the GitHub repository OpenHands/extensions (137 stars, last pushed 4d ago), licensed MIT. It adds 59 tokens to every session and 3,273 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…