Prompt Engineering Agent Skills

Converts narrative technical documents into structured mathematical problem specifications with variables, constraints, objectives, and uncertainty.

Diagnose and mitigate context degradation patterns including lost-in-middle failures, context poisoning, distraction, confusion, and clash in AI agent systems.

Design n8n AI agents with best practices for node selection, sub-node wiring, tool design, structured output, and memory management.

Drafts structured goal-loop prompts for long-running agent work with verifiable stop conditions, validation commands, and documentation requirements.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents with vector search, multimodal AI, and enterprise integrations.

Run Fireworks-hosted open-weight model councils that compare responses and synthesize a final answer.

Reference for building LLM-powered apps with Claude: model selection, SDK usage, streaming, tool use, and migration guidance.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Analyzes draft prompts, identifies intent and gaps, matches ECC components, and outputs an optimized prompt for the user to paste and run. Advisory only, never executes the task.

Bypasses safety filters on API-served LLMs using jailbreak templates, input obfuscation, and multi-model racing.

Produces auditable, schema-valid JSON decision records with assumptions, risks, recommendations, and review gating for high-stakes choices.

Generates or executes a remediation prompt for a book flagged YELLOW or RED by /book-audit, writing a fix-<book-slug>.md that enumerates defects with verbatim PDF source text and verification commands, then optionally executes the fixes to bring the book to GREEN.

Reference for building LLM-powered applications with the Claude API and Anthropic SDKs, covering model selection, parameters, streaming, tool use, and migration.

Generates YouTube videos from prompts, creates vertical Shorts from text, and converts long videos into Shorts using free tools like ffmpeg, edge-tts, and Pexels.

Generates images from text prompts using diffusion models, covering prompt engineering, inpainting/outpainting, ControlNet, and API integration for production workflows.

Generates creative launch blueprints for memecoins on pump.fun, including names, symbols, narratives, and buyback plans, with built-in safety guardrails.

封装阶段3的Copy Spec为可批量执行的提示词包，生成JSONL请求并调用APIMart API出图。

Generates creative launch blueprints for memecoins on pump.fun, including names, symbols, narratives, and buyback plans, with safety guardrails and mandatory disclaimers.

Query free LLM APIs from OpenRouter, Groq, Cerebras, Google AI, and Mistral, with commands to compare models and check status.

Provides PhD-level mathematical analysis for machine learning, including optimization theory, statistical learning theory, and convergence proofs.

Designs and optimizes prompts for LLM-powered applications, covering system prompt architecture, context management, output formatting, and evaluation.

Enforces production-grade Python and AI engineering standards for FastAPI, LangChain/LangGraph, RAG pipelines, and LLM integrations, covering type safety, error handling, testing, and security.