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/eai-org/agent-toolkit/fetch-pr-reviewnpx skills add eai-org/agent-toolkit --skill fetch-pr-reviewgit clone --depth 1 https://github.com/eai-org/agent-toolkitWrote 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/eai-org/agent-toolkit/fetch-pr-review)<a href="https://agentmods.dev/skills/eai-org/agent-toolkit/fetch-pr-review"><img src="https://agentmods.dev/badge/skills/eai-org/agent-toolkit/fetch-pr-review.svg" alt="Measured on agentmods" 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 | $0.00051 | $0.01442 |
| Opus 5 | $0.00026 | $0.00721 |
| Sonnet 5 | $0.00010 | $0.00288 |
| Haiku 4.5 | $0.00005 | $0.00144 |
Grade A, and why
fetch-pr-review 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 5d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR review fetcher
Fetch only — capture the review feedback left on a pull request; never fix code, reply, or
judge the comments. Output is a self-contained .PR-REVIEW.md a fresh session can pick up and act
on (e.g. via /refine-pr-review).
Source & access
Identify the platform from the PR URL (host shape) and fetch through the matching MCP server or
CLI — e.g. GitHub MCP / gh for GitHub PRs, Azure DevOps MCP for ADO pull requests. Use
whichever equivalent tools are connected; tool name prefixes vary by config. If the input is
ambiguous, or no matching MCP/CLI is available, ask the user / stop — don't guess.
Golden rule: never assume — ask
Every uncertainty is confirmed with the user before proceeding: a thread's resolved status, the planning directory, the slug, anything ambiguous in between. A plausible guess is a question, not an answer.
Your task
- Resolve the input. Accept a full PR URL; extract repo/project and PR id. If unrecognizable, ask.
- Fetch the PR metadata — title, description, source/target branch, state, author, linked
ticket/work item — and all feedback:
- inline review threads (file, line, code context, full reply chain);
- top-level review verdicts (approve / request changes / …) with their summary text;
- general conversation comments;
- bot comments (CI, linters, coverage, …) — captured too, but grouped separately from human feedback.
- Determine each thread's status (see Status flags).
- Decide the output directory — the planning directory of the task the PR belongs to,
following the project's/user's convention for where plans live (default:
.agents/plans/). Guess the task's existing home from PR context (linked ticket id, branch name, PR title) — its<id>-<slug>/subdirectory, or the shared parent/group directory holding its<id>-<slug>.TICKET.mdwhen the ticket lives flat there — and confirm the guess with the user; when not sure, always ask. If no matching exists, propose a new<id>-<slug>(ticket id prefix when bound to one, kebab-case slug from the PR title), confirm, and create it. - Pick the file name —
<slug>.PR-REVIEW.md, where<slug>is the planning directory name — in a shared directory, the ticket's own<id>-<slug>instead (e.g.1234-some-task.PR-REVIEW.md). If it already exists and this is a new review round, write<slug>.PR-REVIEW-2.md,-3, … — never overwrite; history per round is kept on purpose. - Write the document (see structure below).
- Print the result — project-relative paths and the next-step line.
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.
- 5d ago First seen · 119 lines · 51 tokens per session scan A c97f2a3312be
fetch-pr-review is a skill published in the GitHub repository eai-org/agent-toolkit (43 stars, last pushed 2d ago), licensed MIT. It adds 51 tokens to every session and 1,442 once invoked, about $0.0003 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
persona-author
Author or improve an ADD-method persona file (a .add/personas/ slug.md) — the project-fit requirements LENS the ADD engine validates and the design/build/verify/advisor surfaces load. Use when adding a domain expert to the ADD roster, when the add-worker persona mode must DRAFT a persona because none fits the task…
wikispec-wiki
This skills instructs the agent how to maintain the knowledge base in the wiki. Use it when you want to ingest documents or query the wiki.
wikispec-propose
Create specs and designs for a new change. Use this when you need to specify a new change or continue to work on the specification of an existing change.
wikispec-apply
Use the task list for the change to implement the actual change.
wikispec-archive
Drive the agent through the full archive step for a wikispec change. You will use the wikispec tool which is a binary in the path, no need to install anything.
wikispec-plan
Create a tasks list to prepare for the implementation of a change. Use this before implementation to understand what needs to be done.