Agent skill

Review Loop

by ntorga in ntorga/agent-starter-kit

Two-mode review loop — single reviewer per epic, three reviewers for full branch.

MITAuto-check passedAI & LLM Engineering

Install Review Loop

skills CLI
$ npx skills add ntorga/agent-starter-kit --skill review-loop -a claude-code

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

GitHub CLI
$ gh skill install ntorga/agent-starter-kit review-loop --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/ntorga/agent-starter-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/review-loop .claude/skills/review-loop && 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
review-loop
GitHub stars
146
Token cost
~1.3k tokens
SKILL.md length
671 words
Files
1
Skills in repo
21
Repo updated
First seen
Licence
MIT

At a glance

Two-mode review loop — single reviewer per epic, three reviewers for full branch.

  • Works in 8 steps: Determine mode. → Identify scope. Determine the changed… → Dispatch. Dispatch the reviewer… → …
  • AI & LLM Engineering work in your project
  • SKILL.md covers Purpose, Procedure and Guardrails
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Loop is an agent skill from ntorga/agent-starter-kit. Two-mode review loop — single reviewer per epic, three reviewers for full branch.

Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering. The repository describes itself as: The scaffold for your multi-model, personalized Natural Language AI Harness (NLAH) . The licence is MIT.

When your agent uses it

  • AI & LLM Engineering work in your project

Example prompts

  • “/review-loop”

Workflow steps

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

  1. Determine mode.
  2. Identify scope. Determine the changed files. Use the command matching the current mode
  3. Dispatch. Dispatch the reviewer (personas/reviewer.md) (follows: skills/dispatch/SKILL.md).
  4. Merge findings. When all dispatched reviewers return
  5. Verify findings. Before acting on any reviewer output, spot-check each blocker and warning against the codebase. Reviewers can hallucinate…
  6. Determine the verdict.
  7. Handle failure. If the verdict is fail
  8. Handle success. If the verdict is pass and the artifact is code from a plan, mark the epic as delivered in impl.md (follows…

What it can do on your machine

Read from SKILL.md and the folder at commit 851e942. 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

Review Loop loads about 1.3k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 671 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~1.3k

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 ntorga/agent-starter-kit at commit 851e942, republished under its MIT licence (© ntorga). 671 words, ~1,333 tokens.

Download SKILL.mdSave it as .claude/skills/review-loop/SKILL.md (or your agent's skills folder).
name
review-loop
description
Two-mode review loop — single reviewer per epic, three reviewers for full branch.
usedBy
maestro
version
0.5.0
lastUpdated
2026-09-12

Purpose

This skill defines how the Maestro reviews sub-agent work before it reaches the user. Two modes serve different needs. Incremental reviews (per epic) use one reviewer. Full branch reviews (at feature completion) dispatch three reviewers for full coverage.

Procedure

  1. Determine mode.

    • Incremental (default) — used for per-epic reviews during plan execution.
    • Full branch — used when all epics in impl.md are marked ✓ and the review covers the entire branch's accumulated changes. Triggered by skills/plan-management/SKILL.md → Tracking Progress.
  2. Identify scope. Determine the changed files. Use the command matching the current mode:

    • Incremental — files changed since the last commit:
      bash
      git diff HEAD --name-only; git ls-files --others --exclude-standard
    • Full branch — files changed against the base branch:
      bash
      git diff "$(git merge-base HEAD main)" --name-only; git ls-files --others --exclude-standard

    If no files changed, skip the review loop.

  3. Dispatch. Dispatch the reviewer (personas/reviewer.md) (follows: skills/dispatch/SKILL.md).

    Incremental mode — single dispatch. Reviewer runs all three lenses.

    Full branch mode — three dispatches with focused <task>:

    • First: coherence focus (follows: skills/code-coherence-review/SKILL.md).
    • Second: quality focus (follows: skills/code-quality-review/SKILL.md).
    • Third: security focus (follows: skills/code-sec-review/SKILL.md).

    For plans and non-code work, use a single dispatch regardless of mode.

    The <task> for every reviewer dispatch must include:

    • What was produced (artifact type and affected scope).
    • The original <task> or acceptance criteria.
    • For code: the list of changed files.
    • The focus area (for Full branch mode).
  4. Merge findings. When all dispatched reviewers return:

    • Union all blockers, warnings, and notes across all reviewers.
    • Deduplicate identical entries — same file, same line, same issue counts once. Dedup before verify is intentional; step 5 verifies each remaining finding.
    • If verdicts conflict, the stricter verdict wins.

    For single-dispatch reviews (incremental mode), use its findings directly.

  5. Verify findings. Before acting on any reviewer output, spot-check each blocker and warning against the codebase. Reviewers can hallucinate — flag false positives (invented violations, misread paths, fabricated rules) and discard them. Only confirmed findings proceed. When confirmed hallucinations appear, classify the cause before re-dispatching:

    • Missing context — the boot payload lacked information the reviewer needed. Fix: enrich the task brief, add memory or skills.
    • Ambiguous input — the task brief had multiple interpretations. Fix: tighten the brief.
    • Design flaw — a skill or persona instruction led the reviewer astray. Fix: patch the framework file and record the fix in long-term memory.
    • Model limitation — the model cannot handle the task at this tier. Fix: switch provider.

    After verifying reported findings, spot-check for missing findings. Pick the first 3 paths in the changed code that involve error handling, authentication, authorization, data mutation, or external I/O, and verify the reviewers addressed them. A reviewer that returns zero findings on complex changes is suspect. A clean bill from a skimmed review is a false pass.

  6. Determine the verdict.

    • pass — zero confirmed blockers and all review steps completed.
    • partial-pass — zero confirmed blockers but one or more review steps were skipped (e.g., external tool unavailable). Surface the gap to the user.
    • fail — one or more confirmed blockers.
  7. Handle failure. If the verdict is fail:

    1. Present the verified findings to the user before re-dispatching.
    2. The user may provide additional input — incorporate it into the re-dispatch.
    3. If the failed artifact is a plan, re-dispatch the Architect (personas/architect.md) (follows: skills/dispatch/SKILL.md) with the confirmed findings. If the failed artifact is code, re-dispatch the Coder (personas/coder.md) (follows: skills/dispatch/SKILL.md) with the findings (blockers, warnings, notes).
    4. When the Coder returns, restart this procedure from step 1 with the new deliverable.
    5. Repeat until the verdict is pass or partial-pass. If the cycle exceeds 2 re-dispatches without reaching a passing verdict, yield to the user with the confirmed findings, conflicting verdicts, or ambiguous trade-offs that could not be resolved.
  8. Handle success. If the verdict is pass and the artifact is code from a plan, mark the epic as delivered in impl.md (follows: skills/plan-management/SKILL.md → Tracking Progress).

Show full SKILL.md (38 more words)Show less

Guardrails

  • Never skip the verify step (step 5). Unverified findings from reviewers must not reach the user or trigger re-dispatches.
  • Never re-dispatch after a hallucination without investigating and fixing the cause first. Blind re-dispatch repeats the same failure.

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

Files

Just SKILL.md in skills/review-loop of ntorga/agent-starter-kit.

Open the folder on GitHubat commit 851e942

Compare with similar skills

Review Loop 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.

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Peft Fine TuningOrchestra-Research/AI-Research-SKILLs13k9 repos~3.1kAutomated safety check: PassMIT
Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs13k9 repos~3.3kAutomated safety check: PassMIT
1passwordtrpc-group/trpc-agent-go1.8k15 repos~656Automated safety check: PassApache-2.0

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Questions about Review Loop

What does Review Loop do?

Two-mode review loop — single reviewer per epic, three reviewers for full branch. Review Loop is an agent skill from ntorga/agent-starter-kit. Two-mode review loop — single reviewer per epic, three reviewers for full branch.

When should I use Review Loop?

Review Loop fits situations like: AI & LLM Engineering work in your project.

How do I install Review Loop in Claude Code?

Run `npx skills add ntorga/agent-starter-kit --skill review-loop -a claude-code`. Or copy the skill folder (skills/review-loop in ntorga/agent-starter-kit) into .claude/skills/review-loop in your project. Claude Code loads it when a task matches its description.

How do I install Review Loop in Codex?

Run `npx skills add ntorga/agent-starter-kit --skill review-loop -a codex`. Or copy the skill folder (skills/review-loop in ntorga/agent-starter-kit) into .agents/skills/review-loop in your project. Codex loads it when a task matches its description.

Can I use Review Loop 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 ntorga/agent-starter-kit --skill review-loop -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-loop, .gemini/skills/review-loop, .github/skills/review-loop and .opencode/skills/review-loop in your project.

What does Review Loop need to run?

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

Does Review Loop 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 Review Loop 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 Review Loop use?

Review Loop is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Loop use?

About 1.3k tokens (SKILL.md is roughly 5.3k 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 Review Loop?

Skills that share tags, products or a category with Review Loop: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Loop?

ntorga (a GitHub user) maintains it in ntorga/agent-starter-kit, which has 146 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on September 12, 2026.

Source: ntorga/agent-starter-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.