Skills worth trusting.
Agent skills from Anthropic, OpenAI, skills.sh, ClawHub, SkillsMP and other reputable sources. Savant scans each one with NVIDIA SkillSpector and evaluates it live, with an LLM drafting and running test cases and Jev validating and scoring them. Workspaces import any skill into their own repositories through a reviewed pull request. Every listing on each hub is enumerated; packages are fetched, scanned and evaluated most-popular first as daily capacity allows.
paperclip-board
Manage a Paperclip company as a board member via chat. Use when the user wants onboarding, company or agent management, approvals, task monitoring, cost oversight, or work product review in the Paperclip control plane.
create-agent-adapter
Create or modify Paperclip agent adapters across server, UI, and CLI surfaces. Use when adding support for a new CLI agent, API agent, custom process, or adapter package.
company-creator
Create agent company packages that conform to agentcompanies/v1. Use when asked to create a company, scaffold an agent team, hire agents, or turn a repo/skills collection into a company package.
status-card-query
Create and maintain agent-authored Paperclip status cards, or compile a prose interest prompt into bounded CompanySearchQuery objects and write the first summary from the assigned Summarizer run.
reflection-coach
Reflect on another agent's recent execution record and propose the smallest durable instruction, skill, or tool-description change. Use for evidence-backed coaching proposals, never hot-swaps.
ramp
Fetch and follow Ramp's published agent playbooks inside Paperclip, with mandatory approval gates for spend, incorporation, cards, account setup, and other financial actions.
frontend-api-contracts
This skill should be used when the user asks to "add an API call", "change a backend", "update settings persistence", "change conversation events", "fix backend auth", "add Agent Server support", or changes src/api, backend registry, Cloud/runtime transport, settings, secrets, compatibility, or conversation resume behavior.
local-stack-runtime
This skill should be used when the user asks to "change the dev stack", "add a runtime service", "change the launcher", "update Docker", "bump Agent Server", "change ingress routing", or changes scripts/dev-*.mjs, runtime-services metadata, config/defaults.json, Docker, automation startup, process shutdown, or local authentication.
design-taste-frontend
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
stitch-design-taste
Semantic Design System Skill for Google Stitch. Generates agent-friendly DESIGN.md files that enforce premium, anti-generic UI standards — strict typography, calibrated color, asymmetric layouts, perpetual micro-motion, and hardware-accelerated performance.
high-end-visual-design
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.
odysseus
Use when the user asks Codex to read or write Odysseus data (todos, email, calendar, memory, documents) or to launch/monitor/stop a Cookbook model-serve task through the scoped Codex Agent API. Requires ODYSSEUS_URL and ODYSSEUS_API_TOKEN.
agent-reach
MUST USE when user wants to 调研/research/搜索/search/查/找/look up anything on the internet — e.g. 全网调研 X / 帮我调研一下 X / 查一下 X / 搜搜 X / 看看大家怎么评价 X / X 上有什么讨论 / research this topic。 Also MUST USE when user mentions any platform or shares any URL/链接: 小红书/xiaohongshu/xhs, Twitter/推特/X, B站/bilibili, Reddit, Facebook, Instagram, V2EX, LinkedIn/领英/Boss直聘/招聘/求职/jobs, YouTube, GitHub code search, 小宇宙播客, 雪球/股票行情, RSS feeds, or any web URL. 16 platforms, multi-backend routing (OpenCLI / per-platform CLIs / APIs). Zero config for 6 channels. Run `agent-reach doctor --json` to see which backend serves each platform right now. NOT for: 写报告/数据分析/翻译等内容加工（本 skill 只负责从互联网获取内容）； 发帖/评论/点赞等写操作；已有专门 skill 的平台（先用专门 skill）。 【路由方式】SKILL.md 包含路由表和常用命令，复杂场景需按需阅读对应分类的 references/*.md。 分类：search / social (小红书/推特/B站/V2EX/Reddit/Facebook/Instagram) / career(LinkedIn/Boss直聘) / dev(github) / web(网页/文章/RSS) / video(YouTube/B站/播客) / finance(雪球/股票)。
bootstrap
Generate a personalized SOUL.md through a warm, adaptive onboarding conversation. Trigger when the user wants to create, set up, or initialize their AI partner's identity — e.g., "create my SOUL.md", "bootstrap my agent", "set up my AI partner", "define who you are", "let's do onboarding", "personalize this AI", "make you mine", or when a SOUL.md is missing. Also trigger for updates: "update my SOUL.md", "change my AI's personality", "tweak the soul".
claude-to-deerflow
Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.
find-skills
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
agent-tracing
Use for agent traces by operation ID (拉线上 tracing): failed tools, arguments/results, available tools, execution location, LLM calls and context.
agent-work
Use for the Agent Work output registry: registration, deduplication, cards, skill providers, CLI scanners and Work types.
builtin-tool
Use for LobeHub builtin agent tools: manifests, executors, runtimes, inspectors, renders, streaming and intervention.
deep-review
Use for independent PR, diff or branch review; not explanations. Full multi-agent review requires an explicit deep-review request.
full-text-search
Use for product search: FtsSearchRepo, pg_search/pg_like/Elasticsearch, mapping migrations, projections, Outbox sync, reindexing and performance. Excludes agent web search.
agent-runtime-hooks
Use for agent lifecycle hooks, tool mocks, intervention, sub-agent calls and context compression.
agent-signal
Use for Agent Signal sources, actions, policies, middleware, workflow handoff and deduplication.
agent-testing-bot
Use for real bot-channel acceptance in Discord, Slack, Telegram, WeChat/Weixin, Lark/Feishu, QQ or iMessage on macOS. Extends acceptance with native chat apps.
heterogeneous-agent
Use for Claude Code/Codex external-agent adapters, IPC, event mapping, sessions, persistence and tool-call chains.
llm-generation
Use for application prompts, generateObject/generateText, model selection and generation tracing. Excludes provider adapters and agent snapshots.
query-netdata-agents
Query or explain direct Netdata Agent APIs and Functions; review direct-query recipes or helpers; troubleshoot bearer authentication. Use query-netdata-cloud for Cloud-proxied calls.
query-snmp-traps
Query, explain or review SNMP trap logs and recipes through Cloud or an Agent, including severity, senders, dedup, decode errors and TRAP_* fields. Guide installed custom-MIB conversion; profile development belongs to collectors-snmp-trap-profiles.
triage-support-bundle
Investigate a Netdata support bundle offline - the archive `netdata-support-bundle` produces - to explain one host's alerts, missing data, collector failures, crashes, high CPU or memory, streaming, cloud claiming, retention, dashboard reachability, permissions, install and update, container and Windows problems. Use for "analyse this support bundle", "a customer sent a bundle", "what does this bundle say", "why did this agent crash", "why is this collector showing no data", "why are alerts not firing", or when reading `MANIFEST.json`, `summary.txt`, `status-file.json`, or anything under `01-system` through `09-permissions`. Not for SNMP evidence under `06-state/snmp-diagnostics` (triage-snmp-diagnostics), not for fleet-wide crash or regression clustering (triage-agent-events), not for live queries against an Agent or Cloud (query-netdata-agents, query-netdata-cloud), and not for changing the bundle collector itself.
triage-agent-events
Investigate Netdata crashes, panics and fatals from agent-events captures or authorized fleet queries. Use for AE_* fields, restart/dedup timing, structured filters, version comparisons and reviews of these investigation helpers. Ordinary logs use the Agent/Cloud query skills.
query-netdata-cloud
Query or explain Netdata Cloud APIs for metrics, logs, topology, flows, alerts, DynCfg, Functions and discovery; review Cloud-query recipes. Use query-netdata-agents for direct Agent access.
agent-builder
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
deploying-scalable-agents
Dalhin ang isang gumaganang prototype ng agent sa isang scalable, observable na production deployment sa Microsoft Foundry. Saklaw nito ang mga deployment pattern (client-hosted, hosted agents, agent workflows), ang lifecycle ng agent, model routing, response caching, evaluation gates, human-in-the-loop approval, observability gamit ang OpenTelemetry, cost optimisation, at smoke-testing ng mga deployed na agent gamit ang AI Smoke Test action. Batay sa Lesson 16 ng AI Agents for Beginners. GAMITIN PARA SA: pag-deploy ng agent sa production, pag-scale ng agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test ng hosted agent, production customer support agent. HUWAG GAMITIN PARA SA: pagbuo ng iyong unang agent (simulan sa Lesson 01), pagpapatakbo ng mga agent nang lokal sa device (gamitin ang local-ai-agents / Lesson 17), Azure infrastructure prov
local-ai-agents
Build local-first AI agents wey dey run fully for developer workstation wit Microsoft Foundry Local and Qwen function-calling models. E cover Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG wit Chroma, local MCP servers, hybrid cloud/local routing, and di privacy/cost/offline trade-offs. E based on Lesson 17 of AI Agents for Beginners. USE FOR: run agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant for my machine. DO NOT USE FOR: deploying agents to di cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
microsoft-docs
ค้นหาเอกสารอย่างเป็นทางการของ Microsoft เพื่อค้นหาแนวคิด บทแนะนำ และตัวอย่างโค้ดสำหรับ Azure, .NET, Agent Framework, Aspire, VS Code, GitHub และอื่นๆ ใช้ Microsoft Learn MCP เป็นค่าเริ่มต้น โดยใช้ Context7 และ Aspire MCP สำหรับเนื้อหาที่อยู่นอก learn.microsoft.com.
orchestration
Coordinate supervised Orca workers: threaded messages, blocking ask/reply, task dispatch, worker_done/escalation waits, task DAGs, decision gates, coordinator loops, and decomposing work across agents. Use `orca-cli` for full ownership handoffs — "hand off", "handoff", "handover", "give this to another agent", "another worktree" — unless asked to supervise, monitor, or coordinate a DAG, and for terminal control, lightweight terminal prompts, shell commands, Orca worktree management, and reading or waiting on terminals.
orca-cli
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only when a visible window needs GUI control that a CLI, filesystem, or API cannot do.
memory-management
AgentDB memory system with HNSW vector search. Use when: need to store patterns, search for solutions, semantic lookup. Skip when: no learning needed, ephemeral tasks.
agent-workflow
Agent skill for workflow - invoke with $agent-workflow
agent-workflow-automation
Agent skill for workflow-automation - invoke with $agent-workflow-automation
agentdb-advanced-features
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
agentdb-learning-plugins
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
agentdb-memory-patterns
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
agentdb-performance-optimization
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
agentdb-query
Query AgentDB through the controller bridge -- semantic routing, hierarchical recall, causal graphs, context synthesis, pattern store/search
agentdb-vector-search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
agentic-jujutsu
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
browser-intent
Execute a natural-language browser intent via page-agent (browser_act) when the target is easier to describe than to select — degrades gracefully when page-agent or an OpenAI-compatible LLM provider isn't configured