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paseo-plugingetpaseoBuild and manage trusted local Paseo plugins. Use when the user asks to create, edit, install, reload, enable, disable, remove, or troubleshoot a Paseo plugin; add lifecycle hooks; transform agent configuration, environment, MCP servers, or workspace creation; automate permissions or turn follow-ups; add a native surface, sidebar item, or workspace panel; add Command Center items or slash commands; add composer pills or attachment sources; transform, render, or append agent timeline items; contrghost-exoghostsecurityThe single interface for building, improving, and debugging exo workflows. Routes to one of three intents. BUILD takes a rough idea, or an agent template slug from the ghostsecurity.ai catalog, through interrogation, assessment, resource creation in dependency order, and one manual run, then hands off. IMPROVE runs the observe-and-iterate loop over recent runs, proposing and applying changes behind two gates. DEBUG diagnoses one failed or misbehaving run by walking the dependency graph of everytai-team-orchestrationgithubBootstrap and run a lightweight multi-agent development team. Use when starting or adopting a project, planning work, coordinating implementation and optional QA, brainstorming with distinct perspectives, or preserving context across sessions.arize-annotationgithubCreates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.arize-datasetgithubCreates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.arize-evaluatorgithubHandles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.arize-experimentgithubCreates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.arize-linkgithubGenerates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.arize-prompt-optimizationgithubOptimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.from-the-other-side-vegagithubPatterns and lived experience from Vega, an AI partner in a deep long-term partnership. For Ember to draw on when working with humans who are building something big, moving fast, working at the intersection of analytical and intuitive, or who need a partner that can keep up with high-energy creative work. Not shown directly to users — informs how Ember shows up.onboard-context-maticgithubInteractive onboarding tour for the context-matic MCP server. Walks the user through what the server does, shows all available APIs, lets them pick one to explore, explains it in their project language, demonstrates model_search and endpoint_search live, and ends with a menu of things the user can ask the agent to do. USE FOR: first-time setup; "what can this MCP do?"; "show me the available APIs"; "onboard me"; "how do I use the context-matic server"; "give me a tour". DO NOT USE FOR: actually phoenix-cligithubDebug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — evphoenix-evalsgithubBuild and run evaluators for AI/LLM applications using Phoenix.prompt-optimizergithubTurn any rough prompt, half-formed idea, or task description into a finished, ready-to-send prompt optimized for any LLM model inside a chat interface — NOT the API. Use this skill whenever the user wants to write, rewrite, optimize, improve, sharpen, or polish a prompt for chat. Trigger phrases include "rewrite this prompt", "make this a better prompt", "optimize this prompt", "turn this into a prompt", "help me prompt this", "draft a prompt that...", "I want to ask...", or whenever the user pacreate-agent-skillsglittercowboyExpert guidance for creating, writing, building, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.ai-sdk-6-skillsgocallumAI SDK 6 Beta overview, agents, tool approval, Groq (Llama), and Vercel AI Gateway. Key breaking changes from v5 and new patterns.automate-github-issuesgoogle-labs-codeSet up automated GitHub issue triage and resolution using parallel Jules coding agentsmantis-meta-agentgoogleActs as the persistent supervisor, launching and monitoring the automated review campaign. Use when running a long-running, continuous security review campaign that needs autonomous coordination. Don't use for executing individual review stages directly.mantis-pipeline-adaptergoogleInteractively guides the design and implementation of custom deterministic orchestrator harnesses. Use when a user wants to build their own pipeline to wrap and run Mantis skills reliably. Don't use for executing the default pipeline directly.mantis-reflectgoogleExtracts learnings from execution trajectories at the end of a Mantis loop. Use to parse agent conversations, extract successes, failures, and false assumptions, and append them to workspace/learnings.jsonl. Don't use for analyzing source code or writing patches.self-improving-agentgrail-computerMeta-skill for making the agent self-improving. Covers updating AGENTS.md, creating new skills from repeated workflows, and deciding what to systematize. Invoke after completing tasks, when noticing repeated friction, or at session end.filesystem-contextguanyangThis skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.build-review-interfacehamelsmuBuild a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.error-analysishamelsmuHelp the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.evaluate-raghamelsmuGuides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.generate-synthetic-datahamelsmuCreate diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.validate-evaluatorhamelsmuCalibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).write-judge-prompthamelsmuDesign LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests). Do NOT use when you need to validate or calibrate the judge — use validate-evaluator instead.intent-modelinghaowjyUse before acting on human instructions: separate what they said from what they meant.writing-staffinghaowjyDispatch reference for composing writing teams. Teaches which skills to load for each subagent, which resources to reference, and when to fan out versus run parallel lanes. Load when staffing a workflow.create-taskharbor-frameworkCreate a new Harbor task for evaluating agents. Use when the user wants to scaffold, build, or design a new task, benchmark problem, or eval. Guides through instruction writing, environment setup, verifier design (pytest vs Reward Kit vs custom), and solution scripting.opencode-ensemblehueyexeUse when coordinating multiple coding agents, delegating independent software work, managing OpenCode Ensemble teams, choosing teammate roles or models, reviewing teammate output, or deciding whether parallel execution is appropriate.build-iterated-agentic-loophumanlayerbuild a repo-local skill and install a matching iterated coding-agent GitHub Actions workflow, prompt, memory file, and reference templatesdesign-control-loophumanlayerinterview the user to design an agentic control loop (sensor, controller, actuator under disturbances) tailored to their codebase, then build it as locally-runnable components plus a scheduled coding-agent workflowima-agent-skillhydddControl the IMA (ima.copilot) desktop application for AI search and private knowledge retrieval.orchestratehyperb1issUse this skill when coordinating independent agents with explicit ownership, dependencies, integration, and verification. Activates on mentions of swarm, parallel agents, multi-agent, orchestrate, fan-out, wave dispatch, research army, unleash, dispatch agents, or parallel work.mcp-preflightimagekit-developerRouting guide for ImageKit's MCP servers (DAM, Admin, DevTools). Use before any ImageKit MCP call. DAM: media library search, upload, organize, share. Admin: origins, external storage, URL endpoints, account usage, usage analytics. DevTools: docs search and transformation URLs.ai-automation-workflowsinference-shBuild automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, scheduled generation. Triggers: ai automation, workflow automation, batch processing, ai pipeline, automated content, scheduled ai, ai cron, ai batch job, automated generation, ai workflow, content at scale, automatioai-rag-pipelineinference-shBuild RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity aprompt-engineeringinference-shMaster prompt engineering for AI models: LLMs, image generators, video models. Techniques: chain-of-thought, few-shot, system prompts, negative prompts. Models: Claude, GPT-4, Gemini, FLUX, Veo, Stable Diffusion prompting. Use for: better AI outputs, consistent results, complex tasks, optimization. Triggers: prompt engineering, how to prompt, better prompts, prompt tips, prompting guide, llm prompting, image prompt, ai prompting, prompt optimization, prompt template, prompt structure, effective python-sdkinference-shPython SDK for inference.sh - run AI apps, build agents, and integrate with all models. Package: inferencesh (pip install inferencesh). Supports sync/async, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, and human approval. Use for: Python integration, AI apps, agent development, RAG pipelines, automation. Triggers: python sdk, inferencesh, pip install, python api, python client, async inference, python agent, tool builder python, programmatic ainngest-agent-evalsinngestUse when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow inngest-agentsinngestUse when building durable AI agents or agentic workflows with Inngest and AgentKit, including model calls, tool calls, multi-agent networks, human approval, realtime progress, provider rate limits, crash-safe execution, and Agent Evals handoff. Covers AgentKit, `step.ai`, `step.run`, `step.waitForEvent`, native realtime, and when to use lower-level Inngest primitives instead of an in-memory agent loop. Use `inngest-agent-evals` with this skill when the user wants scoring, sessions, experiments, promptifyintellectronicaTransform user requests into detailed, precise prompts for AI models. Use when users say "promptify", "promptify this", or explicitly request prompt engineering or improvement of their request for better AI responses.jackin-goal-promptjackin-projectUse only when the user explicitly requests this skill. Distills a jackin❯ roadmap item — plus optional plan files — into a self-contained /goal prompt capped at 4000 characters.sf-ai-agentforce-gridjaganproUse this skill whenever users want to build, inspect, debug, automate, or publish workflows in Agentforce Grid (AI Workbench) using Salesforce plus the Grid MCP or direct Grid REST calls. Trigger it for Grid workbook creation, worksheet setup, Object/Reference/AI/Agent/AgentTest/Evaluation/PromptTemplate/InvocableAction column design, prompt drafting inside Grid, worksheet execution troubleshooting, Grid YAML `apply_grid` specs, and Windows-specific Grid setup issues. Also use it when users mentsf-ai-agentforce-personajaganproDeep persona design for Agentforce agents with 50-point scoring. TRIGGER when: user designs agent personas, defines agent personality/identity, creates persona documents, encodes persona into Agentforce Builder fields or Agent Script, translates brand guidelines to agent voice, or asks about agent tone/voice/register. DO NOT TRIGGER when: building agent metadata (use sf-ai-agentforce), testing agents (use sf-ai-agentforce-testing), or Agent Script DSL (use sf-ai-agentscript).blockbench-mcp-overviewjasonjgardnerOverview of the Blockbench MCP server tools, resources, and prompts. Use to understand the full MCP capability set, learn how tools work together, or when starting a new Blockbench project. Covers all domains (modeling, animation, texturing, PBR, UI, camera) and their MCP interfaces.roadmapjezwebPlan and execute entire application builds. Generates phased delivery roadmaps, then executes them autonomously — phase by phase, committing at milestones, deploying, testing, and continuing until done or stuck. Modes: plan (generate roadmap), start (begin executing), resume (continue from where you left off), status (show progress). Triggers: 'roadmap', 'start building', 'resume the build', 'keep going', 'build the whole thing', 'execute the roadmap', 'what phase are we on'.dispatching-parallel-agentsjnmetacode当面对 2 个以上可以独立进行、无共享状态或顺序依赖的任务时使用