agentsclimarketplace

Pr review

Skill jsirish/workflow-skills/skills/pr-review

Cross-project agent workflow skills — session onboarding, handoffs, PR review, and merge workflows

Install
npx -y skills add jsirish/workflow-skills --skill pr-review

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What its author says it does

Copied from the file, not written here

Request and manage pull request reviews using pr-agent (local CLI) with optional Claude second-pass. Use when needing to review a PR, check review comments, address feedback, or iterate on fixes. Triggers on "review pr", "request review", "check review comments", "review this", or "re-review".

SKILL.md

5.9 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it

PR Review

Primary reviewer: pr-agent (Qodo Merge), installed locally via pipx. Optional second-pass for code PRs: code-review:code-review skill (Claude-based).

Environment

pr-agent is configured entirely through environment variables — there is no user-global config file for the CLI (see Notes). Put this block in your shell rc (~/.zshrc) once, substituting your provider's endpoint, key var, and model:

export OPENAI__KEY="$YOUR_PROVIDER_KEY"
export OPENAI__API_BASE="https://your-endpoint/v1"   # your real endpoint URL — no angle brackets
export CONFIG__MODEL="<provider/model>"            # e.g. openai/<model-name>
export CONFIG__FALLBACK_MODELS='["<provider/fallback-model>"]'
export CONFIG__CUSTOM_MODEL_MAX_TOKENS=<tokens>    # required if model isn't in pr-agent's built-in list
export GITHUB__USER_TOKEN="$GITHUB_TOKEN"
  • SECTION__KEY double-underscore is dynaconf's env convention (pr-agent sets envvar_prefix=False, so no prefix). These vars are read only by pr-agent — they don't collide with other tools' OPENAI_API_KEY/OPENAI_API_BASE. OPENAI__* here means the OpenAI-compatible interface, not the OpenAI service specifically — it works with any provider that exposes an OpenAI-compatible endpoint.
  • CONFIG__MODEL overrides the default (gpt-*); prefix with the provider (openai/, anthropic/, …) to route a custom model to your OPENAI__API_BASE.
  • Setting OPENAI__API_BASE pins all openai-provider calls to your endpoint, so a stray real OPENAI_API_KEY in the environment can never reach api.openai.com via pr-agent — no unset needed.

Project-level config lives in .pr_agent.toml (if present).

Commands

All commands write to the PR under the user's GitHub identity. Confirm before running review, improve, or describe (which rewrites the PR title and body) on a PR the user didn't author.

In an interactive terminal the env block is already loaded, so just call pr-agent. Non-interactive callers (e.g. an agent's shell tool) that don't load your rc should prepend source ~/.zshrc;.

# Review — posts a top-level review comment (correctness, security, completeness)
pr-agent --pr_url <pr-url> review

# Improve — posts inline code suggestions
pr-agent --pr_url <pr-url> improve

# Describe — rewrites the PR title and description
pr-agent --pr_url <pr-url> describe

# Ask — ask a specific question about the PR
pr-agent --pr_url <pr-url> ask "<question>"

Workflow

  1. Identify the PR: gh pr view <number> to confirm scope, then grab the URL.
  2. Request pr-agent review:
    source ~/.zshrc; pr-agent --pr_url <pr-url> review
    
    For inline suggestions, also run improve. (Env vars from Environment must be loaded; the source covers non-interactive shells.)
  3. Optional Claude second-pass (code PRs): invoke code-review:code-review. Useful for a second-model perspective — pr-agent uses a different model by default, so Claude catches different patterns.
  4. Read the posted feedback:
    gh pr view <number> --json reviews
    gh api repos/OWNER/REPO/pulls/<number>/comments
    
  5. Address feedback — fix code, commit, push.
  6. Reply to each thread explaining what was fixed:
    gh api repos/OWNER/REPO/pulls/<PR_NUMBER>/comments/<COMMENT_ID>/replies \
      -X POST -f body="Fixed in <commit-sha>"
    
  7. Re-review — repeat from step 2. Aim for at least 3 rounds when the reviewer is providing legitimate feedback. Stop only when two consecutive rounds produce no new actionable comments.

Round count guidance

  • < 3 rounds: only acceptable when the very first review finds nothing actionable (clean pass). If any round surfaces real findings, keep going.
  • 3 rounds: default target. Covers the common pattern of a fix introducing a new edge case the reviewer catches on re-pass.
  • 4+ rounds: expected for security issues, logic bugs, or when an earlier skip was later reversed.

Deciding What to Address

  • Address immediately: Security issues, bugs, missing error handling, secret leaks
  • Create issue for later: Style preferences, nice-to-have refactors, doc suggestions
  • Skip with explanation: Comments that don't apply to the project's context — reply on the thread so the rationale is on record. Be prepared for the reviewer to re-flag if the skip was wrong.

Checking Review Status

# List open PRs
gh pr list

# View PR details + diff
gh pr view <number>
gh pr diff <number>

# Check existing reviews
gh pr view <number> --json reviews --jq '.reviews[] | {author: .author.login, state, submittedAt}'

# Check inline review comments
gh api repos/OWNER/REPO/pulls/<number>/comments \
  --jq '.[] | {id, path, line, user: .user.login, body}'

Notes

  • pr-agent is a local CLI only — there is no GitHub Action workflow firing on push.
  • Install via pipx install pr-agent. Do not edit the bundled .secrets.toml inside the pipx venvpipx upgrade/reinstall rebuilds the venv and wipes it. Configure via the env vars above instead (they live outside the venv and survive upgrades).
  • There is no user-global config file for the CLI (no ~/.pr_agent.toml). pr-agent loads settings only from package-internal paths and actively blocks dynaconf's include/external-file mechanisms. The "global configuration" in the docs is the org-level pr-agent-settings GitHub repo, which applies only to the hosted GitHub App — not the CLI. Environment variables are the only global mechanism.
  • Project-specific guidance for pr-agent (when relevant) lives in the project's .claude/CLAUDE.md or .agent/pr-agent.md.

Gives 0 of the 12 instructions most context ai engineering skills give in ~1.4k tokens

Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-06

  • dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
  • dispatch final reviewer after all tasksin 37 of 1193, across 11 files
  • provide full task text to the subagentin 31 of 1193, across 10 files
  • review spec compliance before code qualityin 27 of 1193, across 10 files
  • make the hook script executablein 26 of 1193, across 8 files
  • re-snapshot after navigation or DOM changesin 25 of 1193, across 17 files
  • answer subagent questions before proceedingin 22 of 1193, across 7 files
  • mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
  • merge hook into existing settingsin 21 of 1193, across 3 files
  • read files before editing themin 21 of 1193, across 9 files
  • ask if installation is global or projectin 20 of 1193, across 2 files
  • copy the hook script to target locationin 20 of 1193, across 2 files

Said here and by no other author read

  • confirm before reviewing unauthored PRs
  • load environment variables before running pr-agent
  • identify the PR before requesting review
  • request pr-agent review for the PR
  • run improve for inline suggestions
  • read posted feedback after requesting review

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.