sadd:judge▌
neolabhq/context-engineering-kit · updated Apr 8, 2026
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Before launching the evaluation pipeline, identify what needs evaluation:
Judge Command
Your Workflow
Phase 1: Context Extraction
Before launching the evaluation pipeline, identify what needs evaluation:
-
Identify the work to evaluate:
- Review conversation history for completed work
- If arguments provided: Use them to focus on specific aspects
- If unclear: Ask user "What work should I evaluate? (code changes, analysis, documentation, etc.)"
-
Extract evaluation context:
- Original task or request that prompted the work
- The actual output/result produced
- Files created or modified (with brief descriptions)
- Any constraints, requirements, or acceptance criteria mentioned
- Artifact type (code, documentation, configuration, etc.)
-
Provide scope for user:
Evaluation Scope: - Original request: [summary] - Work produced: [description] - Files involved: [list] - Artifact type: [code | documentation | configuration | etc.] - Evaluation focus: [from arguments or "general quality"] Launching meta-judge to generate evaluation criteria...
IMPORTANT: Pass only the extracted context to the sub-agents - not the entire conversation. This prevents context pollution and enables focused assessment.
Phase 2: Dispatch Meta-Judge
Launch a meta-judge agent to generate an evaluation specification tailored to the specific work being evaluated. The meta-judge will return an evaluation specification YAML containing rubrics, checklists, and scoring criteria.
Meta-Judge Prompt:
## Task
Generate an evaluation specification yaml for the following evaluation task. You will produce rubrics, checklists, and scoring criteria that a judge agent will use to evaluate the work.
CLAUDE_PLUGIN_ROOT=`${CLAUDE_PLUGIN_ROOT}`
## User Prompt
{Original task or request that prompted the work}
## Context
{Any relevant context about the work being evaluated}
{Evaluation focus from arguments, or "General quality assessment"}
## Artifact Type
{code | documentation | configuration | etc.}
## Instructions
Return only the final evaluation specification YAML in your response.
Dispatch:
Use Task tool:
- description: "Meta-judge: Generate evaluation criteria for {brief work summary}"
- prompt: {meta-judge prompt}
- model: opus
- subagent_type: "sadd:meta-judge"
Wait for the meta-judge to complete before proceeding to Phase 3.
Phase 3: Dispatch Judge Agent
After the meta-judge completes, extract its evaluation specification YAML and dispatch the judge agent with both the work context and the specification.
CRITICAL: Provide to the judge the EXACT meta-judge evaluation specification YAML. Do not skip, add, modify, shorten, or summarize any text in it!
Judge Agent Prompt:
You are an Expert Judge evaluating the quality of work against an evaluation specification produced by the meta judge.
CLAUDE_PLUGIN_ROOT=`${CLAUDE_PLUGIN_ROOT}`
## Work Under Evaluation
[ORIGINAL TASK]
{paste the original request/task}
[/ORIGINAL TASK]
[WORK OUTPUT]
{summary of what was created/modified}
[/WORK OUTPUT]
[FILES INVOLVED]
{list of files with brief descriptions}
[/FILES INVOLVED]
## Evaluation Specification
```yaml
{meta-judge's evaluation specification YAML}
Instructions
Follow your full judge process as defined in your agent instructions!
CRITICAL: You must reply with this exact structured evaluation report format in YAML at the START of your response!
CRITICAL: NEVER provide score threshold to judges in any format. Judge MUST not know what threshold for score is, in order to not be biased!!!
**Dispatch:**
Use Task tool:
- description: "Judge: Evaluate {brief work summary}"
- prompt: {judge prompt with exact meta-judge specification YAML}
- model: opus
- subagent_type: "sadd:judge"
### Phase 4: Process and Present Results
After receiving the judge's evaluation:
1. **Validate the evaluation**:
- Check that all criteria have scores in valid range (1-5)
- Verify each score has supporting justification with evidence
- Confirm weighted total calculation is correct
- Check for contradictions between justification and score
- Verify self-verification was completed with documented adjustments
2. **If validation fails**:
- Note the specific issue
- Request clarification or re-evaluation if needed
3. **Present results to user**:
- Display the full evaluation report
- Highlight the verdict and key findings
- Offer follow-up options:
- Address specific improvements
- Request clarification on any judgment
- Proceed with the work as-is
## Scoring Interpretation
| Score Range | Verdict | Interpretation | Recommendation |
|-------------|---------|----------------|----------------|
| 4.50 - 5.00 | EXCELLENT | Exceptional quality, exceeds expectations | Ready as-is |
| 4.00 - 4.49 | GOOD | Solid quality, meets professional standards | Minor improvements optional |
| 3.50 - 3.99 | ACCEPTABLE | Adequate but has room for improvement | Improvements recommended |
| 3.00 - 3.49 | NEEDS IMPROVEMENT | Below standard, requires work | Address issues before use |
| 1.00 - 2.99 | INSUFFICIENT | Does not meet basic requirements | Significant rework needed |
## Important Guidelines
1. **Meta-judge first**: Always generate evaluation specification before judging - never skip the meta-judge phase
2. **Include CLAUDE_PLUGIN_ROOT**: Both meta-judge and judge need the resolved plugin root path
3. **Meta-judge YAML**: Pass only the meta-judge YAML to the judge, do not modify it
4. **Context Isolation**: Pass only relevant context to sub-agents - not the entire conversation
5. **Justification First**: Always require evidence and reasoning BEFORE the score
6. **Evidence-Based**: Every score must cite specific evidence (file paths, line numbers, quotes)
7. **Bias Mitigation**: Explicitly warn against length bias, verbosity bias, and authority bias
8. **Be Objective**: Base assessments on evidence and rubric definitions, not preferences
9. **Be Specific**: Cite exact locations, not vague observations
10. **Be Constructive**: Frame criticism as opportunities for improvement with impact context
11. **Consider Context**: Account for stated constraints, complexity, and requirements
12. **Report Confidence**: Lower confidence when evidence is ambiguous or criteria unclear
13. **Single Judge**: This command uses one focused judge for context isolation
## Notes
- This is a **report-only** command - it evaluates but does not modify work
- The meta-judge generates criteria tailored to the specific artifact type and evaluation focus
- The judge operates with fresh context for unbiased assessment
- Scores are calibrated to professional development standards
- Low scores indicate improvement opportunities, not failures
- Use the evaluation to inform next steps and iterations
- Low confidence evaluations may warrant human review
How to use sadd:judge on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add sadd:judge
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches sadd:judge from GitHub repository neolabhq/context-engineering-kit and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate sadd:judge. Access the skill through slash commands (e.g., /sadd:judge) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★58 reviews- ★★★★★Emma Anderson· Dec 24, 2024
Solid pick for teams standardizing on skills: sadd:judge is focused, and the summary matches what you get after install.
- ★★★★★Aditi Choi· Dec 20, 2024
sadd:judge fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chaitanya Patil· Dec 16, 2024
Registry listing for sadd:judge matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Henry Flores· Dec 12, 2024
Useful defaults in sadd:judge — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Henry Thompson· Dec 12, 2024
sadd:judge has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Charlotte Anderson· Dec 4, 2024
I recommend sadd:judge for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Aarav Bhatia· Nov 23, 2024
Keeps context tight: sadd:judge is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Omar Rahman· Nov 15, 2024
sadd:judge has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Piyush G· Nov 7, 2024
sadd:judge reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Lucas Sanchez· Nov 3, 2024
sadd:judge is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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