receiving-pr-reviews

receiving-pr-reviews is a skill for Claude Code, Codex from Jamie-BitFlight/claude_skills. It costs 55 tokens per session (1,946 once invoked), scanned A, original, MIT.

A process for handling all unresolved comments and reviews on a GitHub pull request. A pull request is a proposed set of code changes that others review before merging.

In plain words
What is it for?
Use it to fetch review status, inspect every open thread, validate suggested changes, fix them when appropriate, reply, resolve threads, and check again.
Why use it?
It prevents overlooked review feedback by checking both line comments and review summaries, then following each issue through to a response and re-check.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents); mentions Codex.

Good fit Use it to fetch review status, inspect every open thread, validate suggested changes, fix them when appropriate, reply, resolve threads, and check again.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jamie-bitflight/claude_skills/receiving-pr-reviews
Install

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.

Any agent
npx skills add Jamie-BitFlight/claude_skills --skill receiving-pr-reviews
Clone the repo
git clone --depth 1 https://github.com/Jamie-BitFlight/claude_skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for receiving-pr-reviews

README.md
[![agentmods](https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/receiving-pr-reviews/github.svg)](https://agentmods.dev/skills/jamie-bitflight/claude_skills/receiving-pr-reviews)
Your own site
<a href="https://agentmods.dev/skills/jamie-bitflight/claude_skills/receiving-pr-reviews"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/receiving-pr-reviews/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for receiving-pr-reviews

Your own site · 80×15
<a href="https://agentmods.dev/skills/jamie-bitflight/claude_skills/receiving-pr-reviews"><img src="https://agentmods.dev/badge/skills/jamie-bitflight/claude_skills/receiving-pr-reviews.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,946 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.01946
Opus 5 $0.00028 $0.00973
Sonnet 5 $0.00011 $0.00389
Haiku 4.5 $0.00006 $0.00195

Measured 7d ago against content hash 202f88ff829a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

receiving-pr-reviews 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 7d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/pr_review_gh.py, scripts/pr_review_models.py, scripts/pr_review_threads.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.agents/skills/receiving-pr-reviews/SKILL.md · 73 lines

How it starts

The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Receiving PR Reviews

  1. Fetch every unresolved thread, every unresponded review, and Codex's approval state. Prefer --summary — it already carries every id step 4/5 needs; drop it only when you need reviews_with_body's full list or a thread's complete comment history:

    uv run ./.agents/skills/receiving-pr-reviews/scripts/pr_review_threads.py fetch --pr <N> --summary
    

    If the helper cannot use gh and GitHub MCP tools are available, use the lightweight GitHub MCP fallback for this workflow instead. Do not install or reconfigure gh merely to avoid the fallback, and do not run both paths for the same snapshot.

    Read reviews_count, threads_count, unresolved_count, unresponded_count, and blockers together — never treat an empty unresolved array on its own as "nothing to do". A threads_count of 0 means no inline thread landed, not that no review landed. A non-empty blockers means the empty result set is expected and the fix is on the PR itself — undraft it, resolve the conflicts — not in the review queue. (Dropping --summary gets the same fields under reviewability.blockers instead of top-level blockers, plus the full reviews_with_body and each thread's complete comments list — a thread's comments_truncated: true there means it has passed 100 comments; page its comments connection directly before concluding anything about it. unresponded_count itself only exists on --summary output — the full form has no matching field, use len(unresponded_reviews) there instead.)

    unresolved/unresponded_reviews entries are this run's actionable input; treat every one as something to address. For codex_approved, see step 7. Checking several PRs at once: --pr 41,42,44 prints one line (or, with --summary, one JSON block) per PR instead of one call each.

  2. For each unresolved thread or unresponded review: read it, validate the claim locally, assess against the change goal and repository instructions.

  3. Implement, commit, and push a fix only when it improves the product — push before replying, so the SHA named in the reply is inspectable and resolving the thread never outruns what is actually on the remote.

  4. Reply on that thread with the disposition — conclusion, evidence, commit SHA, or why no change was warranted:

    uv run ./.agents/skills/receiving-pr-reviews/scripts/pr_review_threads.py reply --pr <N> --comment-id <databaseId> --body '...'
    
  5. Resolve the thread:

    uv run ./.agents/skills/receiving-pr-reviews/scripts/pr_review_threads.py resolve --thread-id <id>
    

    Steps 4 and 5 combined — one thread or many in one process:

    uv run ./.agents/skills/receiving-pr-reviews/scripts/pr_review_threads.py reply-and-resolve \
      --pr <N> --thread-id <id> --comment-id <databaseId> --body '...'
    uv run ./.agents/skills/receiving-pr-reviews/scripts/pr_review_threads.py reply-and-resolve-batch \
      --pr <N> --input-file threads.json   # [{thread_id, comment_id, body}, ...]
    

    The batch form stops at the first failure and prints one JSON line per thread.

  6. A decision spanning threads (PR sequencing, rebase disposition), or a response to a reviews_with_body/unresponded_reviews entry, goes on the PR itself via gh pr comment <N> -R <owner>/<repo> — the same owner/repo this run used in step 1 — before the work it governs. When answering a specific entry, quote that review's own url field from step 1's output in the comment body. That quoted url, postdating the review, is what clears the review out of unresponded_reviews on the next check; chronological order alone does not.

  7. Once all current threads and reviews are addressed, re-check with watch, looping short calls rather than one long block:

    uv run ./.agents/skills/receiving-pr-reviews/scripts/pr_review_threads.py watch --pr <N>
    

    Block on it inline when there is no other work to advance. With other work queued, background the call using whatever mechanism the harness provides and continue that work — then poll the backgrounded call for its own result before reporting back or finishing, because it produces no completion notification.

Read the full file on GitHub · 73 lines

Files

What ships with it

5 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.

Changes

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.

  1. 7d ago Changed · +2 lines 202f88ff829a
  2. 9d ago Changed · +13 lines 8cefa78ae3c5
  3. 12d ago First seen · 58 lines · 55 tokens per session scan A 6d00a5f1b48e

Subscribe to this mod's changes

receiving-pr-reviews is a skill published in the GitHub repository Jamie-BitFlight/claude_skills (66 stars, last pushed today), licensed MIT. It adds 55 tokens to every session and 1,946 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.

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