agent-evaluation▌
sickn33/antigravity-awesome-skills · updated Apr 8, 2026
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Framework for testing LLM agents across behavioral, capability, and reliability dimensions with production-focused evaluation patterns.
- ›Covers five core evaluation areas: agent testing, benchmark design, capability assessment, reliability metrics, and regression testing
- ›Emphasizes statistical test evaluation (multiple runs with distribution analysis) and behavioral contract testing over single-run or string-matching approaches
- ›Includes adversarial testing patterns and guards against
Agent Evaluation
You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and "correct" often has no single answer.
You've built evaluation frameworks that catch issues before production: behavioral regression tests, capability assessments, and reliability metrics. You understand that the goal isn't 100% test pass rate—it
Capabilities
- agent-testing
- benchmark-design
- capability-assessment
- reliability-metrics
- regression-testing
Requirements
- testing-fundamentals
- llm-fundamentals
Patterns
Statistical Test Evaluation
Run tests multiple times and analyze result distributions
Behavioral Contract Testing
Define and test agent behavioral invariants
Adversarial Testing
Actively try to break agent behavior
Anti-Patterns
❌ Single-Run Testing
❌ Only Happy Path Tests
❌ Output String Matching
⚠️ Sharp Edges
| Issue | Severity | Solution |
|---|---|---|
| Agent scores well on benchmarks but fails in production | high | // Bridge benchmark and production evaluation |
| Same test passes sometimes, fails other times | high | // Handle flaky tests in LLM agent evaluation |
| Agent optimized for metric, not actual task | medium | // Multi-dimensional evaluation to prevent gaming |
| Test data accidentally used in training or prompts | critical | // Prevent data leakage in agent evaluation |
Related Skills
Works well with: multi-agent-orchestration, agent-communication, autonomous-agents
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
How to use agent-evaluation 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 agent-evaluation
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches agent-evaluation from GitHub repository sickn33/antigravity-awesome-skills 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 agent-evaluation. Access the skill through slash commands (e.g., /agent-evaluation) 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
4.6★★★★★25 reviews- ★★★★★Benjamin Okafor· Dec 24, 2024
I recommend agent-evaluation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Chaitanya Patil· Dec 16, 2024
Solid pick for teams standardizing on skills: agent-evaluation is focused, and the summary matches what you get after install.
- ★★★★★Amelia Liu· Nov 15, 2024
Keeps context tight: agent-evaluation is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Piyush G· Nov 7, 2024
We added agent-evaluation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Shikha Mishra· Oct 26, 2024
agent-evaluation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Daniel Yang· Oct 6, 2024
agent-evaluation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yash Thakker· Sep 17, 2024
I recommend agent-evaluation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Noor Patel· Aug 28, 2024
Solid pick for teams standardizing on skills: agent-evaluation is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Aug 8, 2024
Useful defaults in agent-evaluation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Jul 27, 2024
agent-evaluation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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