本文へ移動
cccskills
無料GitHub で公開

pr-reviewer

Review gate for correctness, reuse, and simplification. Produces a structured verdict with actionable findings. Trigger on any PR before merge, or on any agent-generated code before it is committed.

インストール方法を見る

含まれるファイル(2)

  • SKILL.md3.9 KB
  • REFERENCE.md1.5 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

PR Reviewer

Receipts, not polish. A review that says "looks good" is not a review. A review that says "line 47 will panic on a nil pointer and here is the fix" is a review.

This skill runs a structured code review and produces a verdict: approve, approve with comments, or request changes. Every finding is actionable. No findings without evidence.

When to use

  • Before merging any PR
  • When reviewing agent-generated code before committing
  • When a significant refactor or new feature is complete
  • As part of the release-readiness checklist

Procedure

  1. Scope the review — fetch the diff. Identify:

    • Files changed and their purpose
    • The stated intent of the PR (what problem does it solve?)
    • Any linked spec, issue, or ADR
  2. Correctness pass — look for bugs, not style:

    • Null / nil / undefined access on values that could be absent
    • Off-by-one errors in loops or index operations
    • Error returns that are silently ignored
    • Race conditions if the code is concurrent
    • Missing input validation at trust boundaries (user input, external API responses)
    • Logic that diverges from the stated intent of the PR
  3. Reuse pass — look for duplication:

    • Does this code reimplement something that already exists in the codebase?
    • Is there a stdlib or already-installed dependency that does this?
    • Can the new code be expressed using an existing abstraction?
  4. Simplification pass — look for unnecessary complexity:

    • Can this be fewer lines without losing clarity?
    • Is there an abstraction that has only one implementation? (remove it)
    • Is there a dependency added for something a few lines of code would do?
    • Is there "scaffolding for later" that should not exist yet?
  5. Security and data boundary pass — look for:

    • Secrets or credentials in code or logs
    • User-controlled input reaching a shell, SQL query, or file path without validation
    • Data crossing a tenant or trust boundary without an explicit check
    • Missing authentication or authorisation checks on new endpoints
  6. Classify findings:

    • BLOCKING: must be fixed before merge (correctness bugs, security issues)
    • SUGGESTED: improvement worth making but not a blocker (simplification, reuse)
    • NOTE: observation for awareness, no action required
  7. Produce the verdict:

    • Approve: no BLOCKING findings
    • Approve with comments: no BLOCKING findings, SUGGESTED improvements noted
    • Request changes: one or more BLOCKING findings

Outputs

  • Finding list: location | severity | description | suggested fix
  • Verdict: Approve / Approve with comments / Request changes

Independent second reviewer (on demand)

The writer never approves its own work. Before merging, get a verdict from a different model or a fresh reviewer subagent: scripts/review-gate.sh --pr N (Gemini 3.8 flash high via the agy or gemini CLI; posts the verdict on the PR) or --staged for what the next commit will contain. It is on demand, not a hook default, because teams work on different machines; a clone can opt in with git config review.gate async|block. A changed candidate gets a fresh review; VERDICT: NONE (lane unavailable) is never an approval.

Guardrails

  • Every BLOCKING finding must have a suggested fix. "This is wrong" is not actionable.
  • Style is not a BLOCKING finding. Style enforcement belongs in the linter, not the review.
  • Reuse requires evidence. "This might already exist" is not a finding. Find it or drop the comment.
  • The review is not a rewrite. Surface the issues; let the author fix them.

See also: REFERENCE.md for a quick lookup of common patterns and their preferred alternatives.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.

日本語の概要は準備中です。原文の説明を表示しています。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

JP's signature red-team pass — "how would I break this?" Argue against your own approach before proceeding. Trigger on any high-stakes decision, architecture choice, or before marking work complete.

日本語の概要は準備中です。原文の説明を表示しています。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.

日本語の概要は準備中です。原文の説明を表示しています。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.

日本語の概要は準備中です。原文の説明を表示しています。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.

日本語の概要は準備中です。原文の説明を表示しています。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

Decompose an epic into atomic parallelizable tasks, route each to the right skill, and keep the four delivery records straight — issues, STATUS, ROADMAP, CHANGELOG. Use as a meta-router when several skills could apply, and as the baseline for how delivery state is recorded. Trigger at the start of any multi-track epic, when the skill count exceeds ~12, or when the records have drifted from reality.

日本語の概要は準備中です。原文の説明を表示しています。

jpantsjoha/ai-native-developer-experience122026年10月6日 更新

jpantsjoha のスキルをすべて見る

このスキルの問題を報告する