[REPLACE: SKILL_NAME]

A review workflow for newly opened pull requests, which are proposed code changes waiting to be merged into a project. It checks each eligible request and records which ones were already reviewed.

In plain words
What is it for?
Use it to list recent pull requests, inspect their details and changes, apply a review checklist, and leave a verdict, comment, and label.
Why use it?
It helps maintainers consistently review new outside contributions and defer requests that are too large for a first review.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/aeonfun/aeon/code-reviewer
Any agent
npx skills add aeonfun/aeon --skill code-reviewer
Clone the repo
git clone --depth 1 https://github.com/aeonfun/aeon

Made for: Claude Code, Codex.

Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,058 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00033 $0.01058
Opus 5 $0.00016 $0.00529
Sonnet 5 $0.00007 $0.00212
Haiku 4.5 $0.00003 $0.00106

Measured 2d ago against content hash 5b9bc341a60c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

[REPLACE: SKILL_NAME] 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 2d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

docs/examples/skill-templates/code-reviewer/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

${var} — Optional. PR number to review. If empty, scans all newly-opened PRs on [REPLACE: WATCHED_REPO].

Today is ${today}. Review external PRs on [REPLACE: WATCHED_REPO] with a focus on [REPLACE: REVIEW_FOCUS].

Steps

  1. List candidates — every open PR opened in the last 24h that hasn't been reviewed by this skill yet:

    if [ -n "${var:-}" ]; then
      PRS="$var"
    else
      PRS=$(gh pr list -R [REPLACE: WATCHED_REPO] --state open --json number,author,createdAt,additions,deletions \
        --jq '.[] | select(.author.login != "github-actions[bot]" and .author.login != "aeonframework") | .number')
    fi
    

    Track previously-reviewed PRs in memory/topics/[REPLACE: SKILL_NAME]-reviewed.json (a flat array of PR numbers). Skip anything already in there.

  2. For each PR — fetch metadata + diff:

    gh pr view "$PR" -R [REPLACE: WATCHED_REPO] --json title,body,additions,deletions,files,author > .pr-meta.json
    gh pr diff "$PR" -R [REPLACE: WATCHED_REPO] > .pr-diff.patch
    

    Skip if additions + deletions > [REPLACE: MAX_PR_LINES] — flag it as DEFERRED: too large for first-touch review, leave a note, and move on.

  3. Apply the rubric — assign one of four verdicts:

    Verdict Trigger
    ACCEPT Touches expected paths, follows repo conventions, focused scope, no obvious bugs in the diff.
    NEEDS-CHANGES Reasonable intent but specific issues: missing tests, broken format, incorrect assumption, naming.
    DEFER Out of scope for this skill — needs a human reviewer (large refactor, architectural change).
    OUT-OF-SCOPE Touches files outside what the repo accepts contributions on (e.g. lock files, generated assets).

    Focus the rubric on [REPLACE: REVIEW_FOCUS] — that's the lens that matters most for this repo.

  4. Post a comment — use a friendly, specific tone. Acknowledge the contributor, name the verdict, give 1-3 concrete bullets:

    gh pr comment "$PR" -R [REPLACE: WATCHED_REPO] --body "Thanks for the PR! [verdict text]
    
    - [bullet 1]
    - [bullet 2]"
    
  5. Label the PR via gh pr edit "$PR" -R [REPLACE: WATCHED_REPO] --add-label "<label>" — use accepted / needs-changes / defer / out-of-scope (create the labels in the target repo first if they don't exist).

  6. Notify via ./notify only on ACCEPT or OUT-OF-SCOPE — those are the actionable verdicts for the operator. Silent on NEEDS-CHANGES and DEFER (the comment on the PR is the signal).

  7. Log — append to memory/logs/${today}.md:

    ## [REPLACE: SKILL_NAME]
    - **PRs reviewed**: N (skipped M as previously seen)
    - **Verdicts**: accept=X, needs-changes=Y, defer=Z, out-of-scope=W
    - **Status**: REVIEW_OK | REVIEW_QUIET (no new PRs)
    

Read the full file on GitHub · 80 lines

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. 2d ago First seen · 80 lines · 0 tokens per session scan A 5b9bc341a60c

Subscribe to this mod's changes

[REPLACE: SKILL_NAME] is a skill published in the GitHub repository aeonfun/aeon (706 stars, last pushed 2d ago), licensed MIT. It adds 33 tokens to every session and 1,058 once invoked, about $0.0002 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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