validating-openai-api-implementations
Use to review OpenAI API integrations for correctness (endpoints, params, auth, response handling) and robustness.
Find the perfect capability for your agent.
Use to review OpenAI API integrations for correctness (endpoints, params, auth, response handling) and robustness.
Expert prompt engineering for Claude 4 models (Sonnet 4.5). Use when crafting prompts, optimizing AI responses, implementing chain-of-thought, or improving prompt clarity and effectiveness. Specializes in Claude-specific techniques and best practices.
Expert guidance for building AI agents and multi-agent workflows using Microsoft Agent Framework for .NET. Use when (1) creating AI agents with OpenAI or Azure OpenAI, (2) implementing function tools and structured outputs, (3) building multi-turn conversations, (4) designing graph-based workflows with streaming/checkpointing, (5) implementing middleware pipelines, (6) orchestrating multi-agent systems with fan-out/fan-in patterns, (7) adding human-in-the-loop interactions, (8) integrating OpenTelemetry observability, or (9) exposing agents as MCP tools.
Extract and structure personal context from AI chat transcripts into themed markdown files. Use when (1) Processing Claude, Claude Code, or other AI conversation exports, (2) Building personalized AI assistants from chat history, (3) Creating context files for Claude Projects, GPTs, or Gems, (4) Consolidating scattered knowledge from multiple conversations. Optimized for Claude Haiku.
Prevents skill atrophy by periodically halting for user code writing and review. Use when senior engineers want to stay hands-on while leveraging agentic AI.
[51] EXECUTE. Commitment to maximum quality work with 150% coverage. Use when you need the highest quality output for critical tasks, complex problems, important decisions, or when standard work isn't enough. Triggers on "maximum quality", "150% mode", "full quality", "critical task", or when you explicitly want AI to work at its best.
Run thesis experiments from ideation through publication. Use when brainstorming research questions, writing specs, executing experiments, or polishing for thesis.
Managing session continuity across Claude conversations by saving structured context (tasks, files, errors, git state) to JSON and loading in new sessions. Use when user asks how session continuity works, explaining session protocol functionality, understanding session state management, describing what gets saved in sessions, ending a session with unfinished work, starting a session with existing session-protocol.json, or when user mentions 'save progress', 'save session', 'save my work', 'continue later', 'session context', 'pick up where left off', 'load session', 'restore session', or explicitly invokes /save-session-protocol or /load-session-protocol commands.
Generate high-quality synthetic datasets using statistical samplers and Claude's native LLM capabilities. Use when users ask to create synthetic data, generate datasets, create fake/mock data, generate test data, training data, or any data generation task. Supports CSV, JSON, JSONL, Parquet output. Adapted from NVIDIA NeMo DataDesigner (Apache 2.0).
Optimize system prompts for Claude Code agents using proven prompt engineering patterns. Use when users request prompt improvement, optimization, or refinement for agent workflows, tool instructions, or system behaviors.
Defines the three-layer architecture for the "Agentic Framework" - a meta-layer that surrounds an Application Layer with a Skills Layer in between, providing production-grade controls for AI agents. Use this skill when designing, explaining, or implementing agentic systems that require robust orchestration, domain capabilities, and safety controls.
Orchestrate multiple skills together for complex workflows. Triggers include "chain these skills", "use X then Y skill", "multi-skill workflow", "which skills", or when a task requires capabilities from several skills.
Enhanced meta-orchestration for selecting and combining reasoning patterns. Now includes 9 methodologies (ToT, BoT, SRC, HE, AR, DR, AT, RTR, NDF) with weighted multi-dimensional selection, feedback loops, uncertainty propagation, and validated confidence aggregation. Use when facing complex problems requiring optimal reasoning strategy selection.
The master skill for analyzing, designing, and standardizing the creation of new AI skills (Skill 72).
Autonomous development loop patterns for iterative self-improvement. Auto-triggers when implementing features autonomously, fixing bugs in a loop, or running until completion. Based on frankbria/ralph-claude-code.
Integrate with Google's Gemini CLI for delegating tasks to Gemini. Use when you need an alternative AI perspective, Google Search grounding, parallel task execution, or want to leverage Gemini's 1M token context window. Enables "Claude calling Gemini" patterns for diverse AI collaboration.
OpenAI official SDK usage (Python, Node.js). Use when: writing code that calls OpenAI API, implementing chat/embeddings/images/audio features, handling streaming responses, async patterns, error handling with SDK. For raw HTTP/REST calls, see `openai-api` skill.
Build LLM applications using Dify's visual workflow platform. Use when creating AI chatbots, implementing RAG pipelines, developing agents with tools, managing knowledge bases, deploying LLM apps, or building workflows with drag-and-drop. Supports hundreds of LLMs, Docker/Kubernetes deployment.
Recover from crashed, failed, or interrupted Claude Code sessions. Use this skill when: session crashed during multi-agent parallel execution, need to determine what work was completed vs incomplete, want to generate resumption commands for interrupted tasks, recovering from context window exhaustion, or handling session handoffs. Analyzes agent logs, verifies on-disk state, and creates resumption plans with ready-to-execute Task() commands.
Expert Python developer specializing in modern Python 3.11+ with deep expertise in type safety, async programming, testing, and production-grade code. Invoke for Pythonic patterns, type hints, pytest, async/await, dataclasses.