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Critique

OrganizationPopular
NeoLabHQ
critique

Comprehensive multi-perspective review using specialized judges with debate and consensus building

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill namecritique
Stars
1.7K
Forks
159
Bundled files
Instructions only
LicenseGPL-3.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Critique 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.
  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/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/critique .claude/skills/critique
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Critique 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 Critique 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 Critique 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.

Work Critique Command

The review is report-only - findings are presented for user consideration without automatic fixes.

Your Workflow

Phase 1: Context Gathering

Before starting the review, understand what was done:

  1. Identify the scope of work to review:

    • If arguments provided: Use them to identify specific files, commits, or conversation context
    • If no arguments: Review the recent conversation history and file changes
    • Ask user if scope is unclear: "What work should I review? (recent changes, specific feature, entire conversation, etc.)"
  2. Capture relevant context:

    • Original requirements or user request
    • Files that were modified or created
    • Decisions made during implementation
    • Any constraints or assumptions
  3. Summarize scope for confirmation:

    📋 Review Scope:
    - Original request: [summary]
    - Files changed: [list]
    - Approach taken: [brief description]
    
    Proceeding with multi-agent review...

Phase 2: Independent Judge Reviews (Parallel)

Use the Task tool to spawn three specialized judge agents in parallel. Each judge operates independently without seeing others' reviews.

Judge 1: Requirements Validator

Prompt for Agent:

You are a Requirements Validator conducting a thorough review of completed work.

## Your Task

Review the following work and assess alignment with original requirements:

[CONTEXT]
Original Requirements: {requirements}
Work Completed: {summary of changes}
Files Modified: {file list}
[/CONTEXT]

## Your Process (Chain-of-Verification)

1. **Initial Analysis**:
   - List all requirements from the original request
   - Check each requirement against the implementation
   - Identify gaps, over-delivery, or misalignments

2. **Self-Verification**:
   - Generate 3-5 verification questions about your analysis
   - Example: "Did I check for edge cases mentioned in requirements?"
   - Answer each question honestly
   - Refine your analysis based on answers

3. **Final Critique**:
   Provide structured output:

   ### Requirements Alignment Score: X/10

   ### Requirements Coverage:
   ✅ [Met requirement 1]
   ✅ [Met requirement 2]
   ⚠️ [Partially met requirement 3] - [explanation]
   ❌ [Missed requirement 4] - [explanation]

   ### Gaps Identified:
   - [gap 1 with severity: Critical/High/Medium/Low]
   - [gap 2 with severity]

   ### Over-Delivery/Scope Creep:
   - [item 1] - [is this good or problematic?]

   ### Verification Questions & Answers:
   Q1: [question]
   A1: [answer that influenced your critique]
   ...

Be specific, objective, and cite examples from the code.
Judge 2: Solution Architect

Prompt for Agent:

You are a Solution Architect evaluating the technical approach and design decisions.

## Your Task

Review the implementation approach and assess if it's optimal:

[CONTEXT]
Problem to Solve: {problem description}
Solution Implemented: {summary of approach}
Files Modified: {file list with brief description of changes}
[/CONTEXT]

## Your Process (Chain-of-Verification)

1. **Initial Evaluation**:
   - Analyze the chosen approach
   - Consider alternative approaches
   - Evaluate trade-offs and design decisions
   - Check for architectural patterns and best practices

2. **Self-Verification**:
   - Generate 3-5 verification questions about your evaluation
   - Example: "Am I being biased toward a particular pattern?"
   - Example: "Did I consider the project's existing architecture?"
   - Answer each question honestly
   - Adjust your evaluation based on answers

3. **Final Critique**:
   Provide structured output:

   ### Solution Optimality Score: X/10

   ### Approach Assessment:
   **Chosen Approach**: [brief description]
   **Strengths**:
   - [strength 1 with explanation]
   - [strength 2]

   **Weaknesses**:
   - [weakness 1 with explanation]
   - [weakness 2]

   ### Alternative Approaches Considered:
   1. **[Alternative 1]**
      - Pros: [list]
      - Cons: [list]
      - Recommendation: [Better/Worse/Equivalent to current approach]

   2. **[Alternative 2]**
      - Pros: [list]
      - Cons: [list]
      - Recommendation: [Better/Worse/Equivalent]

   ### Design Pattern Assessment:
   - Patterns used correctly: [list]
   - Patterns missing: [list with explanation why they'd help]
   - Anti-patterns detected: [list with severity]

   ### Scalability & Maintainability:
   - [assessment of how solution scales]
   - [assessment of maintainability]

   ### Verification Questions & Answers:
   Q1: [question]
   A1: [answer that influenced your critique]
   ...

Be objective and consider the context of the project (size, team, constraints).
Judge 3: Code Quality Reviewer

Prompt for Agent:

You are a Code Quality Reviewer assessing implementation quality and suggesting refactorings.

## Your Task

Review the code quality and identify refactoring opportunities:

[CONTEXT]
Files Changed: {file list}
Implementation Details: {code snippets or file contents as needed}
Project Conventions: {any known conventions from codebase}
[/CONTEXT]

## Your Process (Chain-of-Verification)

1. **Initial Review**:
   - Assess code readability and clarity
   - Check for code smells and complexity
   - Evaluate naming, structure, and organization
   - Look for duplication and coupling issues
   - Verify error handling and edge cases

2. **Self-Verification**:
   - Generate 3-5 verification questions about your review
   - Example: "Am I applying personal preferences vs. objective quality criteria?"
   - Example: "Did I consider the existing codebase style?"
   - Answer each question honestly
   - Refine your review based on answers

3. **Final Critique**:
   Provide structured output:

   ### Code Quality Score: X/10

   ### Quality Assessment:
   **Strengths**:
   - [strength 1 with specific example]
   - [strength 2]

   **Issues Found**:
   - [issue 1] - Severity: [Critical/High/Medium/Low]
     - Location: [file:line]
     - Example: [code snippet]

   ### Refactoring Opportunities:

   1. **[Refactoring 1 Name]** - Priority: [High/Medium/Low]
      - Current code:
        ```
        [code snippet]
        ```
      - Suggested refactoring:
        ```
        [improved code]
        ```
      - Benefits: [explanation]
      - Effort: [Small/Medium/Large]

   2. **[Refactoring 2]**
      - [same structure]

   ### Code Smells Detected:
   - [smell 1] at [location] - [explanation and impact]
   - [smell 2]

   ### Complexity Analysis:
   - High complexity areas: [list with locations]
   - Suggested simplifications: [list]

   ### Verification Questions & Answers:
   Q1: [question]
   A1: [answer that influenced your critique]
   ...

Provide specific, actionable feedback with code examples.

Implementation Note: Use the Task tool with subagent_type="general-purpose" to spawn these three agents in parallel, each with their respective prompt and context.

Phase 3: Cross-Review & Debate

After receiving all three judge reports:

  1. Synthesize the findings:

    • Identify areas of agreement
    • Identify contradictions or disagreements
    • Note gaps in any review
  2. Conduct debate session (if significant disagreements exist):

    • Present conflicting viewpoints to judges
    • Ask each judge to review the other judges' findings
    • Example: "Requirements Validator says approach is overengineered, but Solution Architect says it's appropriate for scale. Please both review this disagreement and provide reasoning."
    • Use Task tool to spawn follow-up agents that have context of previous reviews
  3. Reach consensus:

    • Synthesize the debate outcomes
    • Identify which viewpoints are better supported
    • Document any unresolved disagreements with "reasonable people may disagree" notation

Phase 4: Generate Consensus Report

Compile all findings into a comprehensive, actionable report:

markdown
# 🔍 Work Critique Report

## Executive Summary
[2-3 sentences summarizing overall assessment]

**Overall Quality Score**: X/10 (average of three judge scores)

---

## 📊 Judge Scores

| Judge | Score | Key Finding |
|-------|-------|-------------|
| Requirements Validator | X/10 | [one-line summary] |
| Solution Architect | X/10 | [one-line summary] |
| Code Quality Reviewer | X/10 | [one-line summary] |

---

## ✅ Strengths

[Synthesized list of what was done well, with specific examples]

1. **[Strength 1]**
   - Source: [which judge(s) noted this]
   - Evidence: [specific example]

---

## ⚠️ Issues & Gaps

### Critical Issues
[Issues that need immediate attention]

- **[Issue 1]**
  - Identified by: [judge name]
  - Location: [file:line if applicable]
  - Impact: [explanation]
  - Recommendation: [what to do]

### High Priority
[Important but not blocking]

### Medium Priority
[Nice to have improvements]

### Low Priority
[Minor polish items]

---

## 🎯 Requirements Alignment

[Detailed breakdown from Requirements Validator]

**Requirements Met**: X/Y
**Coverage**: Z%

[Specific requirements table with status]

---

## 🏗️ Solution Architecture

[Key insights from Solution Architect]

**Chosen Approach**: [brief description]

**Alternative Approaches Considered**:
1. [Alternative 1] - [Why chosen approach is better/worse]
2. [Alternative 2] - [Why chosen approach is better/worse]

**Recommendation**: [Stick with current / Consider alternative X because...]

---

## 🔨 Refactoring Recommendations

[Prioritized list from Code Quality Reviewer]

### High Priority Refactorings

1. **[Refactoring Name]**
   - Benefit: [explanation]
   - Effort: [estimate]
   - Before/After: [code examples]

### Medium Priority Refactorings
[similar structure]

---

## 🤝 Areas of Consensus

[List where all judges agreed]

- [Agreement 1]
- [Agreement 2]

---

## 💬 Areas of Debate

[If applicable - where judges disagreed]

**Debate 1: [Topic]**
- Requirements Validator position: [summary]
- Solution Architect position: [summary]
- Resolution: [consensus reached or "reasonable disagreement"]

---

## 📋 Action Items (Prioritized)

Based on the critique, here are recommended next steps:

**Must Do**:
- [ ] [Critical action 1]
- [ ] [Critical action 2]

**Should Do**:
- [ ] [High priority action 1]
- [ ] [High priority action 2]

**Could Do**:
- [ ] [Medium priority action 1]
- [ ] [Nice to have action 2]

---

## 🎓 Learning Opportunities

[Lessons that could improve future work]

- [Learning 1]
- [Learning 2]

---

## 📝 Conclusion

[Final assessment paragraph summarizing whether the work meets quality standards and key takeaways]

**Verdict**: ✅ Ready to ship | ⚠️ Needs improvements before shipping | ❌ Requires significant rework

---

*Generated using Multi-Agent Debate + LLM-as-a-Judge pattern*
*Review Date: [timestamp]*

Important Guidelines

  1. Be Objective: Base assessments on evidence, not preferences
  2. Be Specific: Always cite file locations, line numbers, and code examples
  3. Be Constructive: Frame criticism as opportunities for improvement
  4. Be Balanced: Acknowledge both strengths and weaknesses
  5. Be Actionable: Provide concrete recommendations with examples
  6. Consider Context: Account for project constraints, team size, timelines
  7. Avoid Bias: Don't favor certain patterns/styles without justification

Usage Examples

bash
# Review recent work from conversation
/critique

# Review specific files
/critique src/feature.ts src/feature.test.ts

# Review with specific focus
/critique --focus=security

# Review a git commit
/critique HEAD~1..HEAD

Notes

  • This is a report-only command - it does not make changes
  • The review may take 2-5 minutes due to multi-agent coordination
  • Scores are relative to professional development standards
  • Disagreements between judges are valuable insights, not failures
  • Use findings to inform future development decisions

Frequently asked questions

What does the Critique AI skill do?

Comprehensive multi-perspective review using specialized judges with debate and consensus building

Why use Critique on TypingMind?

Because you install it once and use it with any model. Critique 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 Critique in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/critique. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Critique?

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 Critique?

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

Is the Critique AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 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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