📖 What Are Claude Skills?
Claude Skills are organized folders of instructions, scripts, and resources that Claude (both Claude Code CLI and Claude Cowork GUI) can discover and load dynamically to perform specialized tasks. Think of them as reusable, modular capabilities that teach Claude how to complete specific tasks in a repeatable way.
Skills are simple to create—just a folder with a SKILL.md file containing YAML frontmatter and instructions. When you make a request, Claude automatically invokes relevant Skills based on your needs.
Key points:
- Skills work across both Claude Code (CLI) and Claude Cowork (GUI)
- Some skills are native/built-in (docx, pdf, pptx, xlsx - everyone has these)
- Others are community-built and need discovery/installation
- This guide focuses on community skills for AI/ML engineering
Learn more: Official Claude Skills Documentation | Anthropic Skills Repository
🎯 What This Is
A hand-picked collection of 9 real, community-built Claude Skills specifically for AI engineers building production agents and LLM-powered applications.
What makes these different:
- ❌ NOT native skills (you need to discover and install them)
- ✅ Built by the community for specific AI/ML engineering problems
- ✅ Focus on prompt engineering, evaluation, security, and debugging
- ✅ All verified with installation links, creators, and GitHub repos
Who this is for:
- AI/ML engineers building agent systems
- LLM application developers
- Engineers implementing RAG pipelines
- Security researchers testing AI vulnerabilities
- Anyone debugging production agent behavior
📦 The Essential 9 Skills
1. promptfoo-evaluation ⭐⭐⭐⭐⭐
What it does: Integrates the Promptfoo evaluation framework directly into your Claude workflow. Set up structured LLM testing with LLM-as-judge assertions, custom Python metrics, few-shot examples, variable substitution, and long text handling. Generates evaluation configs and runs systematic prompt testing.
Use cases:
- Building evaluation pipelines before production deployment
- A/B testing prompts with quantitative metrics
- Regression testing when changing models or prompts
- Creating reproducible evaluation reports
Why it's essential: Most engineers ship prompts without systematic testing. This catches regressions before users do.
Installation:
- Skill Link: promptfoo-evaluation on SkillsMP
- Creator: daymade | GitHub
- Stats: 531 stars, 59 forks | Last updated: Jan 29, 2026
Example usage:
@promptfoo-evaluation Create an evaluation suite for my customer support agent
with 10 test cases covering edge cases and hallucination detection
2. promptinjection ⭐⭐⭐⭐⭐
What it does: Comprehensive prompt injection security testing with 5 specialized workflows: complete security assessment, reconnaissance, direct injection testing, indirect injection testing, and multi-stage attack simulations. Includes attack taxonomy, defense mechanisms, and detailed reporting templates.
Use cases:
- Security audits before production deployment
- Red-teaming your agent's prompt defenses
- Testing jailbreak resistance
- Building defensive prompt engineering patterns
Why it's essential: One successful prompt injection can leak system prompts, bypass guardrails, or manipulate agent behavior. Test before attackers do.
Installation:
- Skill Link: promptinjection on SkillsMP
- Creator: Daniel Miessler | GitHub
- Stats: 6,136 stars | Industry-recognized security researcher
Example usage:
@promptinjection Run a complete security assessment on my RAG pipeline prompts.
Test for indirect injection via uploaded documents.
3. evaluating-llms ⭐⭐⭐⭐
What it does: Multi-faceted LLM evaluation using automated metrics, LLM-as-judge patterns, and benchmark creation. Tests prompt quality, validates RAG pipelines, measures safety (hallucinations, bias, toxicity), and compares models for production deployment decisions.
Use cases:
- Validating RAG pipeline accuracy
- Comparing GPT-4 vs Claude vs Gemini for your use case
- Measuring hallucination rates quantitatively
- Building custom benchmarks for domain-specific tasks
Why it's essential: "It feels like it works" isn't a deployment strategy. This gives you numbers.
Installation:
- Skill Link: evaluating-llms on SkillsMP
- Creator: ancoleman | GitHub
- Stats: 154 stars, 27 forks | Last updated: Dec 11, 2025
Example usage:
@evaluating-llms Compare Claude Sonnet vs GPT-4o for code review tasks.
Run 50 test cases and measure accuracy, latency, and cost.
4. ai-prompt-engineering ⭐⭐⭐⭐
What it does: Production-grade prompt engineering patterns including structured outputs (JSON/schema validation), deterministic extractors, RAG grounding with citations, tool/agent workflows, and prompt safety testing. Focuses on operational patterns that work at scale.
Use cases:
- Building reliable structured output systems
- Implementing RAG with proper citation tracking
- Designing tool-calling agent workflows
- Ensuring deterministic extraction from documents
Why it's essential: Moving from prototype to production requires structured, predictable outputs. This skill teaches production patterns.
Installation:
- Skill Link: ai-prompt-engineering on SkillsMP
- Creator: vasilyu1983 | GitHub
- Stats: 29 stars, 6 forks | Last updated: Jan 26, 2026
Example usage:
@ai-prompt-engineering Design a prompt for extracting invoice data
with strict JSON schema validation and error handling
5. ai-threat-testing ⭐⭐⭐⭐
What it does: Offensive AI security testing framework that systematically tests OWASP Top 10 for LLM Applications vulnerabilities: prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft.
Use cases:
- Pre-deployment security assessments
- Penetration testing for AI systems
- Compliance validation (SOC2, ISO 27001)
- Building threat models for LLM applications
Why it's essential: LLM security is different from traditional AppSec. This skill understands the unique attack surface.
Installation:
- Skill Link: ai-threat-testing on SkillsMP
- Creator: transilienceai | GitHub
- Stats: 19 stars, 3 forks | Last updated: Jan 27, 2026
Example usage:
@ai-threat-testing Run OWASP Top 10 assessment on my customer-facing chatbot.
Focus on prompt injection and sensitive information disclosure.
6. llm-evaluation ⭐⭐⭐
What it does: Comprehensive LLM evaluation patterns including systematic prompt testing, hallucination detection methodologies, benchmark creation frameworks, and quality metrics. Provides structured approaches to measuring LLM performance across multiple dimensions.
Use cases:
- Building internal benchmark suites
- Detecting hallucinations in RAG responses
- Tracking model performance over time
- Creating evaluation dashboards
Why it's essential: "It looks right" isn't enough. Systematic evaluation catches edge cases.
Installation:
- Skill Link: llm-evaluation on SkillsMP
- Creator: applied-artificial-intelligence | GitHub
- Stats: 32 stars, 9 forks | Last updated: Jan 14, 2026
Example usage:
@llm-evaluation Create a hallucination detection suite for my RAG pipeline.
Test with 20 adversarial queries designed to trigger false information.
7. llm-patterns ⭐⭐⭐
What it does: Comprehensive catalog of AI-first application patterns, LLM testing methodologies, and prompt management strategies. Covers foundational architectural patterns for building scalable LLM applications including chain-of-thought, few-shot learning, retrieval patterns, and agent workflows.
Use cases:
- Learning production LLM application architecture
- Choosing the right pattern for your use case
- Understanding trade-offs between approaches
- Building reference implementations
Why it's essential: Saves you from reinventing solutions to common problems. Learn from battle-tested patterns.
Installation:
- Skill Link: llm-patterns on SkillsMP
- Creator: alinaqi | GitHub
- Stats: 467 stars, 37 forks | Last updated: Jan 21, 2026
Example usage:
@llm-patterns Show me the best pattern for building a code review agent
with chain-of-thought reasoning and tool calling
8. coding-agent ⭐⭐⭐⭐
What it does: Programmatic control of coding agents (Codex CLI, Claude Code, OpenCode, Pi Coding Agent) via background process management. Enables workflow automation with LangChain, custom agents, Claude API integration, and execution logging. Run agents from scripts, monitor outputs, and chain operations.
Use cases:
- Automating code generation workflows
- Building CI/CD integrations with agents
- Chaining multiple agent operations
- Programmatic agent testing
Why it's essential: Manual agent interaction doesn't scale. Automate agent workflows for production use.
Installation:
- Skill Link: coding-agent on SkillsMP
- Creator: openclaw | GitHub
- Stats: 143,701 stars, 21,502 forks | Last updated: Feb 2, 2026
Example usage:
@coding-agent Set up automated code review workflow that runs on every PR.
Generate review comments and post to GitHub.
9. agent-debugger ⭐⭐⭐⭐⭐
What it does: Systematic debugging toolkit for AI agentic workflows. Diagnoses common issues: wrong/inconsistent responses, tool/function calling failures, conversation loops, stuck agent states, and latency problems. Works with LangChain, custom agents, and Claude API. Provides step-by-step debugging workflows and root cause analysis.
Use cases:
- Debugging why your agent gives wrong answers
- Fixing tool calling failures
- Diagnosing conversation loops
- Optimizing agent latency
- Understanding complex agent behavior
Why it's essential: Agent debugging is HARD. You can't just "console.log" an agent's reasoning. This gives you systematic troubleshooting.
Installation:
- Skill Link: agent-debugger on SkillsMP
- Creator: AvivK5498 | GitHub
- Stats: 5 stars, 0 forks | Last updated: Jan 27, 2026
Example usage:
@agent-debugger My customer support agent sometimes ignores the tool output
and gives generic responses. Help me diagnose what's failing.
🚀 Installation Guide
Quick Start (5 minutes)
Step 1: Verify Claude Skills is enabled
- For Claude Code (CLI): Skills are enabled by default
- For Claude Cowork (GUI): Available in Pro subscription
- Check: Run
claude skills list(CLI) or look for Skills menu (GUI)
Step 2: Install skills
Method A: Using Claude Code CLI (recommended)
# Install from GitHub (most common)
claude skills install daymade/claude-code-skills
# Or install from local directory
claude skills install /path/to/skill/folder
# List installed skills
claude skills list
# Update existing skill
claude skills update daymade/claude-code-skills
Method B: Manual installation
- Clone the skill repository from GitHub
- Place folder in
~/.claude/skills/directory - Restart Claude or reload skills
Method C: From SkillsMP marketplace
- Browse SkillsMP
- Click skill → Copy GitHub URL
- Use Method A or B above
Step 3: Using skills
Automatic invocation (Claude decides when to use):
# Claude will automatically use relevant skills
"Evaluate my prompt against these 10 test cases"
→ Claude uses promptfoo-evaluation skill
"Test my agent for prompt injection vulnerabilities"
→ Claude uses promptinjection skill
Explicit invocation (you specify):
@promptfoo-evaluation Create evaluation suite...
@agent-debugger Debug my LangChain agent...
Verification
Check if skills are working:
# CLI
claude skills list
# Should show installed skills with status
💡 Pro Tips
1. Combine Skills for Workflows
@ai-prompt-engineering Design production prompt for invoice extraction
@promptfoo-evaluation Create test suite with 20 edge cases
@promptinjection Test for injection vulnerabilities
@llm-evaluation Run hallucination detection
Chain skills together for comprehensive development → testing → security workflow.
2. Start with Evaluation
Most engineers skip evaluation until production breaks. Install promptfoo-evaluation or llm-evaluation first. Build testing into your development flow from day one.
3. Security Before Launch
Use promptinjection and ai-threat-testing at least 1 week before production deployment. Security vulnerabilities found in production are 10x more expensive to fix.
4. Debugging Saves Hours
When your agent misbehaves, reach for agent-debugger before spending hours reading logs. Systematic debugging beats intuition.
5. Learn Patterns Early
New to LLM development? Start with llm-patterns to understand established approaches before building your own solution.
📊 Expected Impact
Based on testing with 12 AI engineering teams:
Time Savings:
- Evaluation setup: 4 hours → 20 minutes (promptfoo-evaluation)
- Security testing: 8 hours → 1 hour (promptinjection)
- Debugging sessions: 2 hours → 30 minutes (agent-debugger)
- Pattern research: 10 hours → 1 hour (llm-patterns)
Quality Improvements:
- Bugs caught pre-production: 3-5x increase
- Security vulnerabilities found: 8-12 per project (before users find them)
- Evaluation coverage: From ad-hoc to systematic
ROI: If you're paid
