Browse reusable Agent Skills, each with a clear purpose and practical guidance.
This skill should be used when crafting prompts for Nano Banana Pro (Gemini image generation). Use when users want help writing image generation prompts, need guidance on prompt structure, or want to optimize their prompts for better results.
This skill should be used when the user wants to add components (commands, agents, skills, hooks, or MCP servers) to the Component Reference section of the website.
The multi-agent communication and orchestration discipline for a staged pipeline whose stages each carry calibrated uncertainty. Agents talk through files with defined schemas and status fields (never free text); the orchestrator passes input paths plus an explicit output path plus a return contract, verifies each artifact's confidence before advancing a stage gate, holds and integrates worker outputs rather than passing them through, freezes its protected control logic against a checksum, maintains a diversity floor, hardens the fragile handoffs not the resilient hubs, and names the substitution effect every optimized score produces. Conference-agnostic; preloaded by a pipeline orchestrator. Use when coordinating a staged agent pipeline, designing the orchestrator-worker contract, or deciding when to advance, reconcile, or escalate. Trigger keywords - pipeline orchestration, stage gates, structured agent communication, invariant guard, confidence propagation, conflict surfacing.
Evaluates GraphRAG systems across knowledge graph completeness, retrieval relevance, answer correctness, reasoning depth, and hallucination prevention. Provides structured evaluation frameworks, metric selection guidance, and testing protocols. Use when evaluating GraphRAG quality, benchmarking multi-step reasoning, measuring hallucination reduction, or when user mentions evaluate GraphRAG, quality metrics, answer correctness, test my GraphRAG, or measure RAG performance.
This skill should be used when users want to discover, browse, or audit cc-handbook marketplace plugins. Shows all available plugins with installation status, versions, and component breakdown (skills, agents, commands, MCP/LSP servers, hooks). Trigger phrases include "discover plugins", "list handbook plugins", "what plugins are available", "browse marketplace".
Transforms vague or unreliable prompts into structured, constraint-aware prompts with explicit roles, task decomposition, output formats, and quality checks. Use when prompts produce inconsistent outputs, need explicit structure and constraints, require safety guardrails, involve multi-step reasoning that needs decomposition, need domain expertise encoding, or when user mentions improving prompts, prompt templates, structured prompts, prompt optimization, reliable AI outputs, or prompt patterns.
Classifies an opposing player, manager, or agent into one of a configurable archetype set using Bayesian inference over observed behavior (roster composition, transaction pattern, lineup moves, trade activity). Domain-neutral scaffold -- callers supply the archetype taxonomy (names, priors, characteristic feature distributions) and observed features; the skill returns a normalized posterior, MAP archetype, classification confidence, feature-contribution breakdown, and best-response hints. Use when modeling opponents, classifying player types, performing Bayesian archetype inference, producing opponent posteriors, or when user mentions opponent archetype, classify opponent, Bayesian archetype inference, player type classification, opponent modeling, or archetype posterior.
Designs retrieval strategies for querying knowledge graphs in RAG systems, covering pattern selection (global-first, local-first, U-shaped hybrid), query decomposition for multi-hop reasoning, ranking and constraint configuration, and provenance tracking for citation. Use when designing retrieval pipelines, orchestrating search over knowledge graphs, or when user mentions retrieval strategy, search orchestration, query decomposition, multi-hop reasoning, provenance tracking, or citation in GraphRAG.
Create a new evaluation dataset or add cases to an existing one for the Azure SDK QA bot evaluation. WHEN: "add eval dataset item", "add a test case", "new evaluation dataset", "create dataset", "add question to dataset", "curate eval data", "promote staging cases", "upload dataset asset", "new scenario dataset". DO NOT USE FOR: running evaluations, pipeline troubleshooting, knowledge-graph indexing.
Write effective prompts for Jimeng Seedance 2.0 multimodal AI video generation. Use when users want to create video prompts using text, images, videos, and audio inputs with the @ reference system. Covers camera movements, effects replication, video extension, editing, music beat-matching, e-commerce ads, short dramas, and educational content.