Agent skill

Changeset Codereview

by HorizonRobotics in HorizonRobotics/RoboOrchardLab

Review a commit, branch diff, staged diff, working tree diff, patch, or file set for validated high-signal bugs and scoped guidance violations in a local changeset review.

Apache-2.0Auto-check passedDevelopment

Install Changeset Codereview

skills CLI
$ npx skills add HorizonRobotics/RoboOrchardLab --skill changeset-codereview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HorizonRobotics/RoboOrchardLab changeset-codereview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HorizonRobotics/RoboOrchardLab.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/codereview/changeset-codereview .claude/skills/changeset-codereview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
changeset-codereview
GitHub stars
173
Token cost
~1.4k tokens
SKILL.md length
703 words
Files
2
Skills in repo
8
Repo updated
First seen
Licence
Apache-2.0

At a glance

Review a commit, branch diff, staged diff, working tree diff, patch, or file set for validated high-signal bugs and scoped guidance violations in a local changeset review.

  • Works in 8 steps: Determine the reviewed changeset. → Decide whether architecture review is… → Gather and read the relevant repository… → …
  • Development work in your project
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Changeset Codereview is an agent skill from HorizonRobotics/RoboOrchardLab. Review a commit, branch diff, staged diff, working tree diff, patch, or file set for validated high-signal bugs and scoped guidance violations in a local changeset review.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `REPORT_TEMPLATE.md`).

It sits in Development. The licence is Apache-2.0.

When your agent uses it

  • Development work in your project

Example prompts

  • “/changeset-codereview”

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Determine the reviewed changeset.
  2. Decide whether architecture review is also applicable.
  3. Gather and read the relevant repository guidance locally, including
  4. Summarize the reviewed changeset locally before issue discovery.
  5. Launch one strong review subagent for the current round by default. Give it
  6. Validate every candidate locally against the current code, call sites,
  7. Filter and converge.
  8. Output a summary using REPORT_TEMPLATE.md.

What it can do on your machine

Read from SKILL.md and the folder at commit 221ec0e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Changeset Codereview loads about 1.4k tokens when it runs. Until then it costs about 48 tokens; SKILL.md has 703 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~48
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HorizonRobotics/RoboOrchardLab at commit 221ec0e, republished under its Apache-2.0 licence (© HorizonRobotics). 703 words, ~1,421 tokens.

Download SKILL.mdSave it as .claude/skills/changeset-codereview/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
changeset-codereview
description
Review a commit, branch diff, staged diff, working tree diff, patch, or file set for validated high-signal bugs and scoped guidance violations in a local changeset review.

Provide a code review for the given local changeset.

Use this skill after .agents/skills/codereview/SKILL.md routes the task here. Read ../references/triggering-and-signal.md and ../references/review-depth-and-delegation.md first. When composing the final report, also read ../references/report-composition.md.

If the user explicitly asks for architecture review in addition to local changeset review, if an upstream prmr-codereview workflow forwards that requirement, or if the reviewed scope materially changes layering, ownership boundaries, dependency direction, compatibility/public surfaces, or other architecture-review dimensions, apply ../architecture-review/SKILL.md as a paired logical review against the same scope. Keep this skill as the main report owner and summarize the paired architecture result in Related review inputs. The paired review normally shares the same review subagent. Use ../architecture-review/SKILL.md by itself only when architecture is the sole requested review dimension.

Follow ../references/review-depth-and-delegation.md. The main agent owns guidance discovery and candidate validation. Each heavy changeset-review round defaults to one strong review subagent covering all applicable review dimensions.

To do this:

  1. Determine the reviewed changeset.

    • Identify whether the target is a commit, branch diff, staged diff, working tree diff, patch, or explicit file set.
    • Choose the smallest reasonable diff or file scope that satisfies the request.
    • When the change alters public or observable behavior, derive an interface surface before narrowing the review: applicable CLI help/config, public examples, README/docs, runbooks, packaged Agent Skills, and their install/package resources. Inspect related artifacts for drift even when they have no diff; an explicit file list is not by itself an exclusion.
    • For re-review, default to the full current effective changeset unless the user explicitly asks for incremental-only validation.
    • Keep a prior-findings ledger when needed and classify each item as fixed, unresolved, or no longer applicable.
    • If the target is ambiguous, make a reasonable local choice and state it in the report metadata.
  2. Decide whether architecture review is also applicable.

    • Add the architecture-review dimensions when explicitly requested or when the scope materially changes layering, ownership, dependency direction, compatibility/public surfaces, or other architecture boundaries.
    • Keep architecture candidates separate so they can be summarized as a paired review input without launching another subagent by default.
  3. Gather and read the relevant repository guidance locally, including:

    • This repository's root AGENTS.md file, if it exists
    • Any directory-scoped AGENTS.md files that apply to modified files
    • Relevant .agents/instructions/, .agents/references/, or .agents/skills/ files referenced by those AGENTS.md files
    • For a durable documentation claim that depends on externally mutable state, such as a remote job outcome, storage state, or publication status, identify the authoritative source and verify it from fresh live/readback evidence or an inspectable retained snapshot. A local temporary summary alone does not substantiate the claim; if evidence is unavailable, report the claim as unverified rather than confirmed.
  4. Summarize the reviewed changeset locally before issue discovery.

  5. Launch one strong review subagent for the current round by default. Give it the full effective changeset, applicable guidance paths, prior findings, and any paired architecture context. Require one structured candidate list that covers:

    • scoped repository-guidance compliance
    • bugs, correctness, security, and significant performance risk
    • contracts, compatibility, and caller-facing behavior
    • architecture boundaries and minimality when Step 2 applies

    The reviewer must exhaust the scope for high-signal issues rather than stop after the first finding. Keep only candidates with a concrete location, impact, evidence, and confidence. Ignore style nits and speculative issues.

  6. Validate every candidate locally against the current code, call sites, tests, and scoped guidance. Confirm that each cited guidance rule is in scope and actually violated. Keep the current reviewer responsible for verifying fixes to its own findings. After its ledger is resolved, follow the shared review-loop rule and use a fresh reviewer for the next issue-discovery round. Do not launch separate validator agents by default.

  7. Filter and converge.

    • Remove unvalidated issues.
    • De-duplicate overlapping issues and assign final severity.
    • Compare the validated set with reviewer output and any prior-findings ledger before reporting so one pass contains all current findings.
  8. Output a summary using REPORT_TEMPLATE.md.

    • Summarize paired logical review inputs in Related review inputs; do not duplicate their full findings in the main findings sections.
    • If architecture review was paired, include its required summary.
    • If no issues were found, use the exact text: No issues found. Checked for bugs and scoped guidance compliance.
    • If issues were found, include only validated, de-duplicated high-signal findings.

© HorizonRobotics, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/codereview/changeset-codereview of HorizonRobotics/RoboOrchardLab.

  • SKILL.md
  • REPORT_TEMPLATE.md

Open the folder on GitHubat commit 221ec0e

Compare with similar skills

Changeset Codereview next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Changeset Codereview compared with similar skills
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Changeset Codereview this skillHorizonRobotics/RoboOrchardLab173—~1.4kAutomated safety check: PassApache-2.0
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PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT

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Categories

Questions about Changeset Codereview

What does Changeset Codereview do?

Review a commit, branch diff, staged diff, working tree diff, patch, or file set for validated high-signal bugs and scoped guidance violations in a local changeset review. Changeset Codereview is an agent skill from HorizonRobotics/RoboOrchardLab. Review a commit, branch diff, staged diff, working tree diff, patch, or file set for validated high-signal bugs and scoped guidance violations in a local changeset review.

When should I use Changeset Codereview?

Changeset Codereview fits situations like: development work in your project.

How do I install Changeset Codereview in Claude Code?

Run `npx skills add HorizonRobotics/RoboOrchardLab --skill changeset-codereview -a claude-code`. Or copy the skill folder (.agents/skills/codereview/changeset-codereview in HorizonRobotics/RoboOrchardLab) into .claude/skills/changeset-codereview in your project. Claude Code loads it when a task matches its description.

How do I install Changeset Codereview in Codex?

Run `npx skills add HorizonRobotics/RoboOrchardLab --skill changeset-codereview -a codex`. Or copy the skill folder (.agents/skills/codereview/changeset-codereview in HorizonRobotics/RoboOrchardLab) into .agents/skills/changeset-codereview in your project. Codex loads it when a task matches its description.

Can I use Changeset Codereview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HorizonRobotics/RoboOrchardLab --skill changeset-codereview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/changeset-codereview, .gemini/skills/changeset-codereview, .github/skills/changeset-codereview and .opencode/skills/changeset-codereview in your project.

What does Changeset Codereview need to run?

SKILL.md names no scripts, command-line tools or credentials: Changeset Codereview is instructions for the agent only.

Does Changeset Codereview access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Changeset Codereview safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Changeset Codereview use?

Changeset Codereview is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Changeset Codereview use?

About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Changeset Codereview?

Skills that share tags, products or a category with Changeset Codereview: Vercel Composition Patterns (supabase/supabase, 111k stars), Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars) and PR Babysitter (openinterpreter/openinterpreter, 69k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Changeset Codereview?

HorizonRobotics (a GitHub organization) maintains it in HorizonRobotics/RoboOrchardLab, which has 173 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 24, 2026.

Source: HorizonRobotics/RoboOrchardLab on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.