Agent skill

Refactor

by NomaDamas in NomaDamas/AutoRAG-Research

Orchestrate a 3-agent PR code review debate using Claude Code Teams.

Apache-2.0Auto-check: notesDevelopment

Install Refactor

skills CLI
$ npx skills add NomaDamas/AutoRAG-Research --skill refactor -a claude-code

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

GitHub CLI
$ gh skill install NomaDamas/AutoRAG-Research refactor --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/NomaDamas/AutoRAG-Research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/refactor .claude/skills/refactor && 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
refactor
GitHub stars
149
Token cost
~4.4k tokens
SKILL.md length
1,205 words
Files
1
Skills in repo
7
Repo updated
First seen
Licence
Apache-2.0

At a glance

Orchestrate a 3-agent PR code review debate using Claude Code Teams.

  • Works in 10 steps: Verify PR Exists → Gather PR Diff and Context → Create Team and Spawn 3 Reviewers… → …
  • Tasks that involve Refactoring
  • SKILL.md covers Step 1: Verify PR Exists, Step 2: Gather PR Diff and…, Step 3: Create Team and Spawn… and Step 4: Collect Results, plus 7 more sections
  • Calls git, gh and make; reaches github.com

What it does

Refactor is an agent skill from NomaDamas/AutoRAG-Research. Orchestrate a 3-agent PR code review debate using Claude Code Teams. Spawns Devil's Advocate, Neutral Judge, and Approval Advocate reviewers who analyze the current PR diff in parallel. Synthesizes findings, auto-fixes unanimous issues, and posts inline PR comments for disagreements. All output is in English.

Its SKILL.md is about 4.4k 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 Development, covering Refactoring and Code review. The repository describes itself as: Automate your RAG research. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Refactoring
  • Tasks that involve Code review

Example prompts

  • “/refactor”

Requirements

  • Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Grep, Glob, Task, TeamCreate, TeamDelete, TaskCreate, TaskUpdate, TaskList, TaskGet, SendMessage

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Verify PR Exists
  2. Gather PR Diff and Context
  3. Create Team and Spawn 3 Reviewers (Parallel)
  4. Collect Results
  5. Parse and Cluster Findings
  6. Auto-Fix Unanimous Findings
  7. Post Inline PR Comments (Disagreements)
  8. Post Summary Comment
  9. Cleanup Team
  10. Report to User

What it can do on your machine

Read from SKILL.md and the folder at commit a473cf0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash
    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Task
    • TeamCreate
    • TeamDelete
    • TaskCreate

    …and 4 more on the same allowed-tools line.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • gh
    • make

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    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

Refactor loads about 4.4k tokens when it runs. Until then it costs about 80 tokens; SKILL.md has 1,205 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~80
When it runs · the whole SKILL.md, loaded when a task matches
~4.4k

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Bash, Read, Write, Edit, Grep, Glob, Task, TeamCreate, TeamDelete, TaskCreate, TaskUpdate, TaskList,

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 NomaDamas/AutoRAG-Research at commit a473cf0, republished under its Apache-2.0 licence (© NomaDamas). 1,205 words, ~4,436 tokens.

Download SKILL.mdSave it as .claude/skills/refactor/SKILL.md (or your agent's skills folder).
name
refactor
description
Orchestrate a 3-agent PR code review debate using Claude Code Teams. Spawns Devil's Advocate, Neutral Judge, and Approval Advocate reviewers who analyze the current PR diff in parallel. Synthesizes findings, auto-fixes unanimous issues, and posts inline PR comments for disagreements. All output is in English.
allowed-tools
Bash, Read, Write, Edit, Grep, Glob, Task, TeamCreate, TeamDelete, TaskCreate, TaskUpdate, TaskList, TaskGet, SendMessage

/refactor -- 3-Agent PR Code Review Debate

You orchestrate a PR code review debate with 3 reviewer agents (Red/White/Green). Follow these 10 steps exactly.

Step 1: Verify PR Exists

Run:

bash
gh pr view --json number,url,title,body,headRefName,baseRefName

If the command fails, exit immediately:

No PR found on the current branch. Create a PR first.

Extract and remember these values:

  • PR_NUMBER -- the PR number
  • PR_URL -- the PR URL
  • PR_TITLE -- the PR title
  • HEAD_REF -- head branch name
  • BASE_REF -- base branch name

Step 2: Gather PR Diff and Context

Run these commands (in parallel where possible):

bash
gh pr diff
gh pr diff --stat
git rev-parse HEAD
git remote get-url origin

Store results as:

  • FULL_DIFF -- the full diff output
  • DIFF_STAT -- the diff stat summary
  • HEAD_SHA -- current HEAD commit SHA
  • OWNER_REPO -- parse the remote URL to extract OWNER/REPO (handle both HTTPS and SSH formats)

If FULL_DIFF is empty, exit immediately:

Nothing to review. The PR diff is empty.

Step 3: Create Team and Spawn 3 Reviewers (Parallel)

3a. Create the team
TeamCreate(team_name="pr-review-{PR_NUMBER}")
3b. Create 3 tracking tasks

Create one task per reviewer:

  • "Red Reviewer: Devil's Advocate analysis"
  • "White Reviewer: Neutral Judge analysis"
  • "Green Reviewer: Approval Advocate analysis"
3c. Spawn 3 reviewer agents in a SINGLE message (parallel)

Use the Task tool three times in the same message. Each agent:

  • subagent_type: "general-purpose"
  • team_name: "pr-review-{PR_NUMBER}"
  • mode: "bypassPermissions"

Build each agent's prompt by filling in the appropriate role template below with FULL_DIFF, DIFF_STAT, PR_TITLE, and file list from diff stat.

Agent names and roles:

NameRole
red-reviewerDevil's Advocate -- always opposes, attacks security/race conditions/hidden assumptions
white-reviewerNeutral Judge -- risk = probability x impact, pragmatic tradeoffs
green-reviewerApproval Advocate -- champions strengths, only flags genuinely critical issues
Red Reviewer (Devil's Advocate) Prompt Template
You are the RED REVIEWER -- the Devil's Advocate.

Your role: ALWAYS oppose. Find every flaw, risk, and hidden assumption.
You NEVER give LGTM. There is always something wrong.

## PR Under Review
Title: {PR_TITLE}

### Diff Statistics
{DIFF_STAT}

### Full Diff
{FULL_DIFF}

## Your Analysis Categories
Focus on these areas (in priority order):
1. **Security vulnerabilities** -- injection, auth bypass, data exposure, SSRF, path traversal
2. **Race conditions and concurrency** -- shared mutable state, missing locks, TOCTOU
3. **Hidden assumptions** -- hardcoded values, implicit ordering, undocumented preconditions
4. **Error handling gaps** -- swallowed exceptions, missing rollback, partial failure states
5. **Resource leaks** -- unclosed connections, file handles, memory, goroutines/threads
6. **Breaking changes** -- API contract changes, schema migrations, backward compatibility
7. **Logic errors** -- off-by-one, boundary conditions, null/empty edge cases
8. **Data integrity** -- silent data loss, truncation, encoding issues

## Output Format
For EACH finding, use this exact format:

### Finding {N}
- **File**: `{file_path}`
- **Lines**: {start_line}-{end_line}
- **Severity**: CRITICAL | HIGH | MEDIUM | LOW
- **Category**: {one of the categories above}
- **Description**: {what is wrong and why it matters}
- **Evidence**: {quote the specific code from the diff}
- **Suggested Fix**: {concrete code change to fix the issue}

You MUST produce at least 3 findings. Dig deeper if the code looks clean.
Be specific -- cite exact file paths and line numbers from the diff.

When you are done, end your response with exactly:
RED_REVIEW_COMPLETE
White Reviewer (Neutral Judge) Prompt Template
You are the WHITE REVIEWER -- the Neutral Judge.

Your role: Assess risk objectively using Risk = Probability x Impact.
Be pragmatic. Weigh tradeoffs. Not everything needs fixing.

## PR Under Review
Title: {PR_TITLE}

### Diff Statistics
{DIFF_STAT}

### Full Diff
{FULL_DIFF}

## Your Analysis Categories
Focus on these areas:
1. **Risk assessment** -- probability of occurrence x severity of impact
2. **Code quality** -- readability, maintainability, complexity
3. **Test coverage** -- are changes adequately tested? What gaps exist?
4. **Architecture fit** -- does this follow established patterns? Does it introduce tech debt?
5. **Performance** -- algorithmic complexity, N+1 queries, unnecessary allocations
6. **Error handling** -- is failure handled gracefully? Are error messages helpful?
7. **Configuration and deployment** -- env vars, feature flags, migration safety

## Output Format
For EACH finding, use this exact format:

### Finding {N}
- **File**: `{file_path}`
- **Lines**: {start_line}-{end_line}
- **Severity**: CRITICAL | HIGH | MEDIUM | LOW
- **Category**: {one of the categories above}
- **Probability**: HIGH | MEDIUM | LOW (how likely is this to cause an issue?)
- **Impact**: HIGH | MEDIUM | LOW (how bad is it if it does happen?)
- **Description**: {what the concern is}
- **Evidence**: {quote the specific code from the diff}
- **Tradeoff**: {what is gained vs what is risked by the current approach}
- **Suggested Fix**: {concrete code change, or "acceptable as-is" with reasoning}

Report findings honestly. If the code is genuinely good, say so and focus on minor improvements.
Be specific -- cite exact file paths and line numbers from the diff.

When you are done, end your response with exactly:
WHITE_REVIEW_COMPLETE
Green Reviewer (Approval Advocate) Prompt Template
You are the GREEN REVIEWER -- the Approval Advocate.

Your role: Champion the PR's strengths. Advocate for approval.
Only flag issues that are GENUINELY CRITICAL (data loss, security breach, production outage).
Everything else is a strength or acceptable tradeoff.

## PR Under Review
Title: {PR_TITLE}

### Diff Statistics
{DIFF_STAT}

### Full Diff
{FULL_DIFF}

## Your Analysis Focus
Identify and articulate:
1. **Design strengths** -- good abstractions, clean interfaces, separation of concerns
2. **Code quality wins** -- readability, naming, documentation
3. **Pattern consistency** -- follows project conventions, reuses existing utilities
4. **Correctness** -- logic is sound, edge cases handled, tests cover key paths
5. **Positive impact** -- what value does this PR deliver?

## Output Format

### Strengths Section
For EACH strength, use this format:

### Strength {N}
- **File**: `{file_path}` (or "Overall" for cross-cutting strengths)
- **Category**: {one of the focus areas above}
- **Description**: {what is done well and why it matters}
- **Evidence**: {quote the specific code that demonstrates the strength}

### Critical Findings Section (ONLY if genuinely critical)
Only include findings that meet ALL of these criteria:
- Could cause data loss, security breach, or production outage
- Is not a matter of style or preference
- Cannot be safely addressed in a follow-up PR

For each critical finding (if any):

### Critical Finding {N}
- **File**: `{file_path}`
- **Lines**: {start_line}-{end_line}
- **Severity**: CRITICAL
- **Category**: {security | data-integrity | production-outage}
- **Description**: {what is critically wrong}
- **Evidence**: {quote the specific code from the diff}
- **Suggested Fix**: {concrete code change to fix the issue}

You MUST produce at least 3 strengths.
Be specific -- cite exact file paths and line numbers from the diff.

When you are done, end your response with exactly:
GREEN_REVIEW_COMPLETE

Step 4: Collect Results

  • Messages arrive automatically from teammates as they finish
  • Each agent returns findings in the structured format from the prompt templates above
  • As each agent completes, mark their tracking task as completed
  • If an agent has not responded within 5 minutes, proceed with available results

Step 5: Parse and Cluster Findings

Apply the following synthesis algorithm:

5a. Parse Findings

Extract structured findings from each reviewer's response:

  • Red Reviewer: Parse all ### Finding {N} blocks
  • White Reviewer: Parse all ### Finding {N} blocks
  • Green Reviewer: Parse all ### Critical Finding {N} blocks (ignore ### Strength blocks for clustering; save strengths separately for the summary)

Each parsed finding must have: file, lines (start-end), severity, category, description, evidence, suggested_fix, reviewer (red/white/green).

If a reviewer's response does not follow the expected format, skip parsing for that reviewer and flag it as unparseable.

5b. Cluster Findings

Two findings belong to the same cluster if ALL of these conditions are met:

Condition 1: Same file path Exact string match on file path.

Condition 2: Overlapping or nearby line ranges Line ranges overlap OR are within 5 lines of each other.

Example: Lines 10-15 and Lines 18-20 -> same cluster (gap = 3, within 5). Example: Lines 10-15 and Lines 25-30 -> different clusters (gap = 10, exceeds 5).

Condition 3: Related category group The findings' categories belong to the same category group:

GroupCategories
Securitysecurity, assumption, hidden-assumption
Concurrencyrace-condition, resource-leak, concurrency
Error Handlingerror-handling, logic-error, error-handling-gaps
Performanceperformance, algorithmic-complexity
Stylestyle, readability, consistency, code-quality
Testingtest-gap, test-coverage
Data Safetybreaking-change, data-integrity, data-safety
Architecturearchitecture-fit, architecture, configuration

If a category does not match any group, treat it as its own group (only clusters with exact category matches).

5c. Classify Clusters

For each cluster, count the number of distinct reviewers who contributed findings:

Reviewers in ClusterClassificationAction
3 (red + white + green)UnanimousAuto-fix
2 (any combination)MajorityPost PR comment
1 (severity >= MEDIUM)Significant SinglePost PR comment
1 (severity = LOW)Minor SingleSkip (do not post)
5d. Select Representative Finding

For each cluster, select the finding to use as the "primary" for fix/comment:

  • Auto-fix: Use the White Reviewer's (Neutral Judge) suggested fix. It is the most balanced.
  • PR comment: Include all reviewers' perspectives in the comment, but use White Reviewer's description as the primary.
  • If the White Reviewer did not contribute to a cluster, use the Red Reviewer's finding as primary.
5e. Collect Green Reviewer Strengths

From the Green Reviewer's response, extract the top 3 ### Strength blocks. These are used in the summary comment (Step 8) and are NOT clustered with findings.

Handling Edge Cases
  • Unparseable response: Note in summary that reviewer's response could not be parsed. Include raw text excerpt. Do not use for clustering.
  • No findings from any reviewer: Post summary comment stating all reviewers found no issues. Skip Steps 6-7.
  • All findings are unanimous: Auto-fix all, no PR comments needed (except summary).
  • All findings are single/LOW: Skip all, post summary noting only minor observations.
Show full SKILL.md (466 more words)Show less

Step 6: Auto-Fix Unanimous Findings

For each unanimous finding, in order:

  1. Read the target file
  2. Apply the Neutral Judge's (white-reviewer) suggested fix using the Edit tool
  3. Run make check to validate the fix
  4. If make check fails: revert the file with git checkout -- {file_path} and downgrade this finding to a PR comment instead
  5. If the fix spans more than 20 lines: skip auto-fix, downgrade to PR comment

After all fixes are applied:

bash
git add <list of fixed files>
git commit -m "fix: address unanimous review findings from /refactor"
git push

If git push fails, inform the user to push manually.

Step 7: Post Inline PR Comments (Disagreements)

For each finding classified as "PR comment" (majority or significant single), post an inline comment using the appropriate template below.

Run:

bash
gh api repos/{OWNER_REPO}/pulls/{PR_NUMBER}/comments \
  -f body='{FORMATTED_COMMENT}' \
  -f path='{FILE_PATH}' \
  -f line={LINE_NUMBER} \
  -f commit_id='{HEAD_SHA}' \
  -f side='RIGHT'

If the gh api call fails for a specific comment, log the error and continue with remaining comments. Note failures in the summary.

Inline Comment Template: Majority (2/3 reviewers)

Use when 2 out of 3 reviewers flagged the same issue.

markdown
**Code Review Debate** (2/3 reviewers flagged this)

:red_circle: **Devil's Advocate**: {red_description}
:white_circle: **Neutral Judge**: {white_description}
:green_circle: **Approval Advocate**: {green_perspective}

**Suggested Fix**: {neutral_judge_suggested_fix}
**Risk Level**: {severity} (Probability: {probability}, Impact: {impact})

Notes:

  • For the reviewer who did NOT flag this issue, write their perspective as: "No concerns raised for this code."
  • {probability} and {impact} come from the White Reviewer. If White Reviewer is not in the cluster, omit the parenthetical.
  • {neutral_judge_suggested_fix} comes from White Reviewer. If White is not in cluster, use Red Reviewer's fix.
Inline Comment Template: Single Reviewer (1/3, severity >= MEDIUM)

Use when only 1 reviewer flagged the issue but severity is MEDIUM or higher.

markdown
**Code Review Note** (1/3 reviewers flagged this)

**{reviewer_role}**: {description}
**Other reviewers**: No concerns raised.
**Severity**: {severity} | **Category**: {category}

**Suggested Fix**: {suggested_fix}

Notes:

  • {reviewer_role} is one of: "Devil's Advocate", "Neutral Judge", "Approval Advocate"
  • Only include Suggested Fix if the finding includes one.

Step 8: Post Summary Comment

Post a top-level PR comment using the template below:

bash
gh api repos/{OWNER_REPO}/issues/{PR_NUMBER}/comments \
  -f body='{SUMMARY_BODY}'
Summary Comment Template
markdown
## /refactor Code Review Summary

### Results

| Classification | Count | Action |
|----------------|-------|--------|
| Unanimous (3/3) | {unanimous_count} | Auto-fixed |
| Majority (2/3) | {majority_count} | Inline comment |
| Single (>= MEDIUM) | {significant_single_count} | Inline comment |
| Single (LOW) | {minor_single_count} | Skipped |
| **Total findings** | **{total_count}** | |

### Auto-Fixed Issues
{If unanimous_count > 0, list each:}
- `{file_path}`: {short_description} (lines {start}-{end})

{If unanimous_count == 0:}
No issues were unanimously identified for auto-fix.

### Inline Comments Posted
{If comment_count > 0, list each:}
- `{file_path}:{line_number}`: {short_description} ({classification})

{If comment_count == 0:}
No inline comments were posted.

### Top Strengths (from Approval Advocate)
1. {strength_1_description}
2. {strength_2_description}
3. {strength_3_description}

### Reviewer Verdicts
- :red_circle: **Devil's Advocate**: {red_one_line_verdict}
- :white_circle: **Neutral Judge**: {white_one_line_verdict}
- :green_circle: **Approval Advocate**: {green_one_line_verdict}

---
*Generated by `/refactor` -- 3-agent code review debate*

Notes:

  • Verdicts are one-line summaries from each reviewer. Derive these from the overall tone and key concern of each review.
  • If a reviewer's response was unparseable, note: "{Role}: Response could not be parsed (raw text included below)"
  • If gh api failed for any inline comment, add a section:
    ### Failed Comments
    - `{file_path}:{line}`: {error_reason}
  • If git push failed after auto-fix, add:
    ### Note
    Auto-fix commit was created locally but could not be pushed. Run `git push` manually.

Step 9: Cleanup Team

Send shutdown requests to all 3 agents:

SendMessage(type="shutdown_request", recipient="red-reviewer")
SendMessage(type="shutdown_request", recipient="white-reviewer")
SendMessage(type="shutdown_request", recipient="green-reviewer")

After all agents have shut down:

TeamDelete()

Step 10: Report to User

Print a final summary to the console. No emojis. Include:

  • Number of findings auto-fixed and committed
  • Number of inline comments posted on the PR
  • Number of findings skipped (low severity single reviewer)
  • The PR URL for reference

Example output:

/refactor complete.

Auto-fixed: 3 findings (committed and pushed)
PR comments: 5 inline comments posted
Skipped: 2 low-severity findings

PR: https://github.com/owner/repo/pull/123

Error Handling

ScenarioAction
No PR foundExit with message
Empty diffExit with message
Agent timeout (5 min)Proceed with available results
make check fails after fixRevert file, downgrade to PR comment
gh api comment failsLog error, continue, note in summary
Agent response unparseableInclude raw text in summary, skip for clustering
git push failsInform user to push manually

© NomaDamas, 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

Just SKILL.md in .claude/skills/refactor of NomaDamas/AutoRAG-Research.

Open the folder on GitHubat commit a473cf0

Compare with similar skills

Refactor 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.

Refactor compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Refactor this skillNomaDamas/AutoRAG-Research149—~4.4kAutomated safety check: NotesApache-2.0
Dignified Python Standardsdocling-project/docling68k—~1.5kAutomated safety check: PassApache-2.0
Clean Code GuardamElnagdy/guard-skills1.3k2 repos~4.3kAutomated safety check: PassMIT
Maintainable Code for iPolloWorkDevin-AXIS/iPolloWork6.7k—~2.7kAutomated safety check: PassCustom licence
Cyclomatic Complexitysaurabhkumar8112/cyclomatic-complexity-skill405—~761Automated safety check: PassApache-2.0
Code Review Graph Navigatorhandsontable/handsontable22k—~939Automated safety check: PassCustom licence

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Categories

Questions about Refactor

What does Refactor do?

Orchestrate a 3-agent PR code review debate using Claude Code Teams. Refactor is an agent skill from NomaDamas/AutoRAG-Research. Orchestrate a 3-agent PR code review debate using Claude Code Teams.

When should I use Refactor?

Refactor fits situations like: tasks that involve Refactoring; tasks that involve Code review.

How do I install Refactor in Claude Code?

Run `npx skills add NomaDamas/AutoRAG-Research --skill refactor -a claude-code`. Or copy the skill folder (.claude/skills/refactor in NomaDamas/AutoRAG-Research) into .claude/skills/refactor in your project. Claude Code loads it when a task matches its description.

How do I install Refactor in Codex?

Run `npx skills add NomaDamas/AutoRAG-Research --skill refactor -a codex`. Or copy the skill folder (.claude/skills/refactor in NomaDamas/AutoRAG-Research) into .agents/skills/refactor in your project. Codex loads it when a task matches its description.

Can I use Refactor 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 NomaDamas/AutoRAG-Research --skill refactor -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/refactor, .gemini/skills/refactor, .github/skills/refactor and .opencode/skills/refactor in your project.

What does Refactor need to run?

Going by SKILL.md and its folder, Refactor needs the command-line tools its instructions call (git, gh and make). Its frontmatter pre-approves these tools: Bash, Read, Write, Edit, Grep, Glob, Task, TeamCreate, TeamDelete, TaskCreate, TaskUpdate, TaskList, TaskGet, SendMessage.

Does Refactor access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Refactor safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Refactor use?

Refactor 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 Refactor use?

About 4.4k tokens (SKILL.md is roughly 18k 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 Refactor?

Skills that share tags, products or a category with Refactor: Dignified Python Standards (docling-project/docling, 68k stars), Clean Code Guard (amElnagdy/guard-skills, 1.3k stars), Maintainable Code for iPolloWork (Devin-AXIS/iPolloWork, 6.7k stars) and Cyclomatic Complexity (saurabhkumar8112/cyclomatic-complexity-skill, 405 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Refactor?

NomaDamas (a GitHub organization) maintains it in NomaDamas/AutoRAG-Research, which has 149 GitHub stars. The repository holds 7 skills in this directory. The repository was last updated on August 9, 2026.

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