Llm

regex-vs-llm-structured-text skill for choosing regex or LLM in structured text extraction. Start with deterministic parsing, add LLM validation for low-confidence edge cases, and use a cheaper, more reliable pipeline for documents, forms, invoices, and data analysis.

llm-trading-agent-security is a practical guide for securing autonomous trading agents with wallet authority. It covers prompt injection, spend limits, pre-send simulation, circuit breakers, MEV-aware execution, and key isolation to reduce financial-loss risk in a Security Audit.

foundation-models-on-device helps you build Apple FoundationModels features on iOS 26+ with on-device text generation, guided output with @Generable, tool calling, snapshot streaming, and availability checks for privacy-first apps.

cost-aware-llm-pipeline helps you build LLM workflows that control API spend with model routing, immutable cost tracking, retry handling, and prompt caching. Ideal for batch jobs, document pipelines, and Workflow Automation where output volume and quality tradeoffs need clear rules.

fact-checker is a prompt-driven skill for structured claim verification, source evaluation, and clear verdicts with confidence and context. Install it from Shubhamsaboo/awesome-llm-apps to fact check statements, rumors, statistics, and misleading claims with a repeatable workflow.

deep-research is a lightweight agent skill for structured web research. It helps clarify scope, gather multiple sources, evaluate credibility, and synthesize cited findings from a single SKILL.md workflow.

cso is a Chief Security Officer–style security audit skill for agents. It helps review codebases and workflows for secrets exposure, dependency and supply-chain risk, CI/CD security, and LLM/AI security using OWASP Top 10 and STRIDE. Use cso for structured Security Audit reviews with confidence gates, active verification, and trend tracking.

langsmith-fetch is a debugging skill for LangChain and LangGraph agents. It guides assistants to install the CLI, set LangSmith credentials, fetch recent traces, and analyze errors, tool calls, memory activity, latency, and token usage with trace evidence.

humanloop-automation is a Claude skill for automating Humanloop workflows through Composio Rube MCP. Install it from ComposioHQ/awesome-claude-skills, configure https://rube.app/mcp, verify RUBE_SEARCH_TOOLS, connect Humanloop, and discover current tool schemas before execution.

GroqCloud Automation is a Composio MCP skill for GroqCloud model discovery, chat completions, audio translation, and TTS voice selection through GROQCLOUD_* tools.

prompt-engineering-patterns is a practical skill for production prompt design, covering install context, reusable templates, few-shot examples, structured outputs, and prompt optimization workflows for Context Engineering.

The evaluation-methodology skill explains PluginEval scoring for Model Evaluation, including layers, rubrics, composite scoring, badge thresholds, and practical guidance for interpreting results and improving weak dimensions.

rag-implementation is a practical skill for planning RAG systems with vector databases, embeddings, retrieval patterns, and grounded-answer workflows. Use it to compare stack options, shape architecture decisions, and guide install and usage for document Q&A, knowledge assistants, and semantic search.

similarity-search-patterns helps you choose distance metrics, index types, and hybrid retrieval patterns for semantic search and RAG workflows. Use it to plan production vector search tradeoffs around recall, latency, and scale.

The hybrid-search-implementation skill shows how to combine vector and keyword retrieval with RRF, linear fusion, reranking, and cascade patterns for RAG and search systems.

langchain-architecture is a design guide for building LangChain 1.x and LangGraph applications. Use it to choose between chains, agents, retrieval, memory, and stateful orchestration patterns before implementation.

Use the llm-evaluation skill to design repeatable evaluation plans for LLM apps, prompts, RAG systems, and model changes with metrics, human review, benchmarking, and regression checks.

embedding-strategies helps you choose and optimize embedding models for semantic search and RAG workflows, with practical guidance on chunking, model tradeoffs, multilingual content, and retrieval evaluation.

ai-prompt-engineering-safety-review is a prompt audit skill for reviewing LLM prompts for safety, bias, security weaknesses, and output quality before production, evaluation, or customer-facing use.

agentic-eval is a GitHub Copilot skill that shows how to build evaluation loops for AI outputs using reflection, rubric-based critique, and evaluator-optimizer patterns.

develop-ai-functions-example helps you create or modify runnable AI SDK examples in vercel/ai under examples/ai-functions/src/. Use it to choose the right category, match repo conventions, and build minimal examples for provider validation, demos, or fixtures.

agent-workflow-designer helps plan production multi-agent workflows with pattern selection, handoff contracts, retries, timeouts, context limits, and quality gates. Use it to design sequential, parallel, router, orchestrator, or evaluator flows and generate starter skeletons with scripts/workflow_scaffolder.py.

prompt-governance is a Claude skill for managing production prompts as versioned, reviewed, tested assets. Use it to plan prompt registries, regression tests, A/B experiments, eval pipelines, release approvals, and rollback workflows for AI features.

senior-ml-engineer helps agents plan production ML systems: model deployment, MLOps pipelines, monitoring, drift detection, RAG architecture, and LLM integration. Includes reference guides and starter scripts for deployment, monitoring, and RAG that teams should adapt before production.