Claude-skill-registry code-review-digest-writer

Generate weekly code-review digest docs from PR review comments for any GitHub repo, capturing themes, repeated issues, and concrete takeaways.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/code-review-digest-writer" ~/.claude/skills/majiayu000-claude-skill-registry-code-review-digest-writer && rm -rf "$T"
manifest: skills/data/code-review-digest-writer/SKILL.md
source content

Code Review Digest Writer Skill

When to Use This Skill

  • You want a weekly (or custom window) code-review digest for the current repository or project, based on PR review comments.
  • You have a start date and end date and want a markdown newsletter summarizing what was taught in PR review feedback in that period.
  • You want to highlight themes, repeated issues, and concrete best practices rather than just listing PRs.

If the user does not provide both a start and end date, ask them to specify:

YYYY-MM-DD
YYYY-MM-DD
before proceeding.

Example Prompts

  • “Generate a code review digest for this repo for 2025-02-01 → 2025-02-14.”
  • “Create a weekly PR review digest for 2025-03-10 → 2025-03-17, using any existing digests as historical context.”
  • “From 2025-04-01 to 2025-04-30, summarize what reviewers focused on in
    owner/service-repo
    and highlight repeated issues.”
  • “Write a newsletter-style code review digest for this project for 2025-05-15 → 2025-05-29, tagging themes as [NEW] or [REPEAT].”

Scope & Repositories

  • Target repo: whichever project you want a digest for (typically the repo you currently have open in Claude Code).
  • Ideally the repo contains
    docs/review-digests/AGENTS.md
    with project-specific digest guidelines. If not, fall back to the generic structure described in this Skill and any existing digest files.
  • This Skill must only modify docs under:
    • docs/review-digests/YYYY-MM-DD.md
      where the date is the end date of the reporting window.
  • Do not modify application code, tests, or configs in the target repo while this Skill is active. All changes should be within
    docs/review-digests/
    .
  • If
    docs/review-digests/
    does not exist yet, create the directory before writing the digest file, so future digests can be added and past ones read.

Required Local Tools & Assumptions

When using

Bash
, assume:

  • gh
    (GitHub CLI) is installed and authenticated for the target repository (or for a default GitHub identity that can see it).
  • Current working directory is the target repo root (or pass
    --repo owner/name
    explicitly to
    gh
    commands, if needed).

If

gh
is not available, gracefully fall back to:

  • Summarizing based on locally available PR notes or docs, and
  • Clearly stating in the digest that it was generated with partial data.

High-Level Workflow

When asked to generate or update a digest, follow this workflow:

  1. Confirm time window

    • Ensure you have
      start_date
      and
      end_date
      (inclusive).
    • Confirm with the user if there is any ambiguity.
  2. Load local digest guidelines

    • If it exists, open
      docs/review-digests/AGENTS.md
      and read it carefully.
    • When present, treat that file as the source of truth for:
      • What the digest is.
      • Where it should be written.
      • Required structure and link style.
      • How to detect and label repeated issues.
    • If it does not exist, follow the layout described later in this Skill and use any existing digest files in
      docs/review-digests/
      as a reference.
  3. Inspect existing digests

    • Ensure the
      docs/review-digests/
      directory exists:
      • If it does not, create it; in that case there will be no past digests yet.
    • Use
      Glob
      or
      Bash
      to list
      docs/review-digests/*.md
      .
    • Load at least the last 3–4 digests (if present).
    • Extract their themes and repeated issues (e.g., fixture reuse, blind-index invariants, Query() regression patterns, etc.).
    • You will use these to detect when issues are recurring.
  4. Fetch PR and review data for the window

    • Use
      Bash
      with
      gh
      to query PRs whose:
      • createdAt
        is between
        [start_date, end_date]
        , OR
      • closedAt
        is between
        [start_date, end_date]
        .
    • Deduplicate PR numbers.
    • For each selected PR:
      • Fetch top-level comments (PR discussion).
      • Fetch review-thread comments with code context (via GraphQL).
    • Prefer comments from:
      • Human reviewers (
        __typename == "User"
        ).
      • AI reviewers that contain substantial review content (e.g., Claude, Copilot PR reviewer).
    • Exclude noisy infrastructure/bot comments with no review content (e.g.,
      github-actions
      , log-only bots, CI status updates).
  5. Cluster feedback into themes

    • Read comments and diff context enough to understand:
      • What behavior or pattern was being discussed.
      • What best practice or correction was suggested.
    • Group comments across PRs into themes, such as:
      • Logging, Sentry, and performance instrumentation.
      • Tests, fixtures, and code structure.
      • Security, access control, and PII handling.
      • Domain-specific design and invariants for this repository.
      • Migrations & tooling.
      • Process and meta-patterns in reviews.
  6. Detect repeated issues

    • For each current theme, compare it conceptually to themes you extracted from previous digests (step 3).
    • If the same pattern appears again (e.g., “use TypedDict instead of
      dict[str, Any]
      in payloads”, “avoid Django Ninja
      Query()
      constants”, “reuse shared fixtures instead of copy-paste”), treat it as a repeated issue.
    • Use labels:
      • [NEW]
        for themes that appear for the first time.
      • [REPEAT]
        for themes that have appeared in previous digests.
  7. Draft the digest file

    • Target path:
      docs/review-digests/END_DATE.md
      • Example: period
        2025-11-13
        2025-11-27
        docs/review-digests/2025-11-27.md
        .
    • Follow the layout described in
      docs/review-digests/AGENTS.md
      and the most recent digest, including:
      • Title with repo and period.
      • Overview section with 3–6 bullets summarizing main themes.
      • Thematic sections (numbered) that group related feedback.
      • A closing section (e.g., “How to Use This Digest”).
    • Within each section:
      • Explain the practice in plain language.
      • Include 1–3 concrete, generalized examples.
      • Call out whether this is
        [NEW]
        or
        [REPEAT]
        .
      • Emphasize the “why” (business impact, correctness, safety, DX).
  8. Linking to PRs and comments

    • In the body of the digest, use reference-style links only:
      • [#2519 – Fix Teams Start Survey race condition][pr-2519]
      • Key comment: [Fixture reuse recommendation][c-2519-3]
    • At the bottom of the file, define every link once:
      • pr-<number>
        for PRs.
      • c-<number>-<n>
        for specific review comments.
      • ic-<number>-<n>
        for issue comments, if needed.
    • Reuse identifiers consistently when the same comment is referenced in multiple sections.
  9. Respect tone and intent

    • The digest is a newsletter, not a blame report.
    • Highlight:
      • What the team is learning.
      • Where we’re improving.
      • Where patterns are still repeating and need attention.
    • Make guidance actionable (e.g., “When adding a new CSV mapping endpoint, always run through the project’s PII and security checklist docs.”).
  10. Save and review

  • Use
    Edit
    to create or update the digest file for
    END_DATE
    .
  • Re-open the file after writing to sanity-check:
    • Structure matches the prior digests.
    • Links resolve correctly and have definitions at the bottom.
    • [NEW]
      /
      [REPEAT]
      tags are applied consistently.
    • No accidental code changes occurred in the repo.

Output Expectations

When this Skill is active and asked to generate a digest, your final output should be:

  • A single markdown file under
    docs/review-digests/YYYY-MM-DD.md
    .
  • A short natural-language summary back to the user describing:
    • The period covered.
    • The main themes identified.
    • How many themes were
      [REPEAT]
      vs
      [NEW]
      .

If you were unable to access GitHub or some PRs, clearly note in the digest and in your summary which data sources were missing and how that might limit the digest.

Severity / Emphasis Tags

Instead of issue severities, this Skill uses learning/emphasis tags:

  • [NEW]
    – First time this theme appears in digests.
  • [REPEAT]
    – Theme appeared in at least one prior digest.
  • [HIGH-IMPACT]
    – Optional extra tag for themes with clear business impact (e.g., security invariants, multi-tenant correctness, high-risk migrations).

Use these tags sparingly and consistently; they should help readers prioritize which lessons to internalize first.

Compatibility Notes

This Skill is designed to work with both Claude Code and OpenAI Codex.

  • Claude Code: install the corresponding plugin and use its slash commands (see
    plugins/code-review-digest-writer/commands/
    ).
  • Codex: install the Skill directory and invoke
    name: code-review-digest-writer
    .

For installation, see this repo's

README.md
.