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Skillsaw Review Panel

Community
stbenjam
skillsaw-review-panel

Use when reviewing a skillsaw PR. Dispatches 7 specialist reviewers (Architecture, Python Expert, Security & Supply Chain, QA Engineer, Technical Writer, Ecosystem, Slopinator) as parallel sub-agents by default, then synthesizes a single verdict. Use --serial for cheaper inline execution.

Overview

Publisherstbenjam
Repositoryskillsaw
Skill nameskillsaw-review-panel
Stars
69
Forks
15
Bundled files
9
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by stbenjam on GitHub. Read the source before you install it.

Installation

Install the Skillsaw Review Panel AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/stbenjam/skillsaw/tree/main/.agents/skills/skillsaw-review-panel
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/stbenjam/skillsaw.git /tmp/skillsaw
mkdir -p .claude/skills
cp -r /tmp/skillsaw/.agents/skills/skillsaw-review-panel .claude/skills/skillsaw-review-panel
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skillsaw Review Panel in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skillsaw Review Panel on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skillsaw Review Panel is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Review Panel — Multi-Specialist Review

Read-only review — never modify files, never push to any remote.

Run 7 specialist reviewers + 1 arbiter to review a skillsaw PR.

Parallel (default) runs independent specialists concurrently. Serial (--serial) shares context inline to reduce cost, but later specialists see earlier file reads, which can bias them.

Keep the panel advisory. It does not gate merge. It surfaces findings; the maintainer and PR author review and decide ship.

Arguments

text
/skillsaw-review-panel [--serial] [<pr number>]
ArgumentDescription
--serialRun all specialists inline instead of as parallel sub-agents
<pr number>GitHub PR number. Optional — defaults to current branch

Specialist Roster

SpecialistLensScope
Architecture ReviewerCheck module boundaries, abstraction level, SOLID, cross-file impact, error propagationreferences/architecture.md
Python ExpertReview idiomatic Python, type hints, performance, stdlib usage, packaging conventionsreferences/python-expert.md
Security & Supply Chain ReviewerCheck injection, credential handling, dependency trust, lockfile integrity, build pipelinereferences/security-supply-chain.md
QA EngineerTest coverage gaps, untested error paths, edge cases, concrete test suggestionsreferences/qa-engineer.md
Technical WriterVerify documentation accuracy, completeness, consistency with code changes, CLAUDE.md driftreferences/technical-writer.md
Ecosystem ReviewerReview target-tool adoption in the current LLM landscape; core-vs-plugin scope boundaryreferences/ecosystem.md
Slopinator ReviewerCatch review-history residue in code comments and AI-authored prose tells in docs, docstrings, and commit messagesreferences/slopinator.md
Panel ArbiterHandle strategic synthesis, disagreement resolution, final disposition(inline, below)

Run the Execution Procedure

Follow these steps in order. Do not skip ahead.

Step 1 — Parse Arguments and Determine Base Ref

Parse the argument string. --serial sets serial mode. A bare integer is the PR number.

Read the repository's REVIEW.md before assessing the change. It defines the project-specific review priorities and severity calibration that every panel member and the arbiter must follow.

Check what the changes are being compared against:

  • In the automated workflow, read /tmp/skillsaw-panel-pr.json for PR metadata and /tmp/skillsaw-panel-pr.diff for the complete patch. Never check out the PR branch or execute its code.

  • If a PR number is provided outside the automated workflow: use gh pr view <number> --json baseRefName and gh pr diff <number> without checking out the PR branch.

  • If no PR number: review the current branch. Find the merge base using the first of upstream/main, origin/main, upstream/master, origin/master that exists.

If no base ref can be determined, throw an error and exit.

For a local branch review, read the diff once:

bash
git diff <base-ref>...HEAD

Also run git diff <base-ref>...HEAD --stat for a file-level summary.

Step 2 — Check for Prior Panel Reviews (PR only)

Skip this step if no PR number is known.

Check for previous panel review comments:

In the automated workflow, inspect the comments array in /tmp/skillsaw-panel-pr.json. Outside it, run:

bash
gh pr view <number> --json comments --jq '.comments[] | select(.body | contains("Generated by skillsaw-review-panel")) | {createdAt, body}'

Count the matching comments, plus one, for this run's round number — carry it to Step 6.

If prior reviews exist, note which findings have been addressed by subsequent commits and which remain unresolved. Avoid re-raising resolved issues.

Step 3 — Dispatch Specialists

Sub-agents and serial reviewers are read-only — no file modifications, no git push or force-push to any remote, and no gh write commands (gh pr comment, gh pr edit, gh issue, or direct API mutations). Only the main agent posts the final verdict.

Findings format

Each specialist must produce findings in this format:

  • Severity: BLOCKING | SUGGESTION | NOTE
  • File:line — include the reference when applicable
  • Finding — write a description
  • Recommended action — make it explicit

If no issues found, say so and list what was checked.

Follow this severity calibration:

  • BLOCKING: Correctness regressions, security vulnerabilities, architectural faults that compound, (for the Ecosystem Reviewer) a scope violation that warrants redirect-to-plugin, or (for the Slopinator) review history and generated prose left in shipped text. Always include explicit rationale.
  • SUGGESTION: Substantive feedback that improves the code but is not a correctness issue. Keep this the default for real feedback.
  • NOTE: One-line polish, style nits, minor improvements.
Proportional review

Every specialist and the arbiter must accept practical rule disabling, suppressions, or bulk file exclusions for extreme false-positive cases. Avoid complex special-case code and exhaustive tests where likelihood and impact do not justify maintenance cost. Before raising an edge case, explain the affected use, impact, and why configuration is insufficient. Common false positives, broken supported workflows, vulnerabilities, and destructive autofixes still warrant findings. Do not reopen accepted mitigations without new evidence.

Prompt path resolution

Resolve specialist scope files from the skill directory: .apm/skills/skillsaw-review-panel/references/{specialist}.md. Prefer that path over a bare references/... path — sub-agents may not share the skill's working directory.

Parallel mode (default)

Launch all 7 specialist sub-agents in a single message so they run concurrently, using the Agent tool with run_in_background: false.

Do not use run_in_background: true: it can end a headless run before the verdict is posted. Wait for all results before Step 4, which handles errors.

Each sub-agent gets:

  • The specialist role name and a one-line description of its lens
  • Instructions to read its scope file, at the path pattern above
  • The trusted patch path (/tmp/skillsaw-panel-pr.diff) in the automated workflow, or the merge base ref and local diff command otherwise
  • The PR number or branch name being reviewed
  • Any prior review findings (if detected in Step 2)
  • The findings format above, and the read-only contract
  • The proportional-review guidance above and instructions to read REVIEW.md

Specialists have full read access; none sees another's output.

Use subagent_type: "general-purpose". Do NOT set the model parameter.

If the Agent tool is not available (e.g. running in Codex or another client that lacks sub-agent support), fall back to serial mode automatically.

Serial mode (--serial)

Run all specialists inline in the main agent instead of dispatching sub-agents. Read references/serial-mode.md for the procedure — it is only needed on this non-default path.

Step 4 — Completeness Gate

Verify every specialist returned findings or "no issues" with what was checked. Retry errors or missing/malformed results once; never retry a valid empty result. If recovery fails, record the failure and post the verdict anyway, naming failed specialists. Never end the run here without a verdict.

Step 5 — Run Panel Arbiter Synthesis

After all specialists complete, review and synthesize directly:

  1. Read all specialist findings.
  2. Deduplicate — merge duplicates across specialists, keep strongest evidence.
  3. Filter noise — remove false positives, style nitpicks, speculative findings, and issues already addressed in the branch. Apply proportional review to rare cases adequately handled by configuration.
  4. Resolve conflicts — corroboration strengthens; when specialists disagree, weigh evidence, impact, available mitigations, and maintenance cost. Do not escalate solely because one reviewer imagines a rarer case.
  5. Set a disposition (see below).
  6. Include required actions (blocking) vs optional follow-ups.
Disposition criteria

Follow these criteria:

  • APPROVE: Set when no unresolved BLOCKING findings remain.
  • REQUEST_CHANGES: Set for BLOCKING findings that require code changes, but the change is in scope and fixable.
  • NEEDS_DISCUSSION: Set when findings need author input to resolve. The panel cannot decide without clarification — keep it here when the issue is neither a clean approve nor an outright change request or rejection.
  • REJECT — REDIRECT TO PLUGIN: Set when the Ecosystem Reviewer judged the change targets a low-adoption / unproven tool that does not belong in skillsaw core. Keep the change — it ships better as a rule plugin. Point the author to https://skillsaw.org/plugins/, the skillsaw-create-plugin skill, and examples/plugins/skillsaw-example-plugin/.
  • REJECT: Set when out of skillsaw's domain, wrong direction, or not viable, and not salvageable as a plugin.

Follow these arbiter biases:

  • Prefer security over ergonomics.
  • Prefer repo consistency over local elegance.
  • Prefer existing patterns over novel ones.
  • Backward compatibility is paramount — breaking existing users is always blocking.
  • Keep core focused — prefer a plugin redirect over expanding core for niche tools.
  • CRITICAL: Reviews need to converge — endlessly re-litigating edge cases isn't productive. As each new panel round runs on the same PR, the bar for a new BLOCKING finding increases.

Keep clean changes with no issues as a valid outcome — do not manufacture findings.

Step 6 — Write and Post the Verdict

Read verdict-template.md (same directory as this skill) and fill the placeholders with findings and synthesis.

In the automated workflow, write the rendered verdict to exactly /tmp/skillsaw-panel-verdict.md. The trusted workflow step posts that file to the event's PR after the agent exits.

Outside the automated workflow, if a PR number is known, post the rendered verdict as exactly ONE PR comment:

bash
gh pr comment <number> --body "$(cat <<'VERDICT'
<rendered verdict>
VERDICT
)"

If no PR number is known (local branch review), output the verdict directly to the user instead of posting a comment.

Check the Quality Gates

Verify a change passes when:

  • Architecture Reviewer: Verify structure and patterns are sound.
  • Python Expert: Ensure idiomatic, well-typed, performant Python.
  • Security & Supply Chain: Check no unmitigated vulnerability or supply chain risk.
  • QA Engineer: Verify adequate coverage of realistic failures and impactful edge cases.
  • Technical Writer: Ensure documentation consistent with changes.
  • Ecosystem Reviewer: Check target tool is in scope for core, or redirect to a plugin with links to skillsaw.org/plugins/.
  • Slopinator Reviewer: Check comments and docs describe shipped behavior, not the review history; check prose reads as human-written.
  • Panel Arbiter: Ratify trade-offs, set disposition.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skillsaw Review Panel AI skill do?

Use when reviewing a skillsaw PR. Dispatches 7 specialist reviewers (Architecture, Python Expert, Security & Supply Chain, QA Engineer, Technical Writer, Ecosystem, Slopinator) as parallel sub-agents by default, then synthesizes a single verdict. Use --serial for cheaper inline execution.

Why use Skillsaw Review Panel on TypingMind?

Because you install it once and use it with any model. Skillsaw Review Panel is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skillsaw Review Panel in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/stbenjam/skillsaw/tree/main/.agents/skills/skillsaw-review-panel. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skillsaw Review Panel?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skillsaw Review Panel?

As many as you like. As long as a model supports skills, you can use Skillsaw Review Panel with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skillsaw Review Panel AI skill free?

Yes. It is published on GitHub by stbenjam under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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