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.
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.
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.
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.
create-skill
Helps you create new agent skills for Zed. Use this to create a skill, ask about SKILLs.md, or package reusable agent instructions.
verify-openhands
This skill should be used to "verify OpenHands features", "test the Canvas UI like a user", "drive Agent Canvas", "check a UI change in the real app", "create or update the feature map", "run the daily pass" or "run the weekly feature audit". Ships control-openhands (launch, doctor, browser, LLM profiles, conversations, evidence, map tooling, cleanup) and the maintained map of every user-facing feature.
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.
archify-tuning
Improve Archify diagram accuracy, visual quality and complete authoring speed with fewer repairs. Use for generation experiments or evaluating changes to layout, routing, diagnostics, authoring instructions and acceptance rules against actual diagrams and agent traces.
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".
llm-generation
Use for application prompts, generateObject/generateText, model selection and generation tracing. Excludes provider adapters and agent snapshots.
heterogeneous-agent
Use for external-agent launchers and execution identity, Claude Code/Codex adapters, IPC, event mapping, sessions, persistence and tool-call chains.
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.
deep-review
Use for independent PR, diff or branch review; not explanations. Full multi-agent review requires an explicit deep-review request.
builtin-tool
Use for LobeHub builtin agent tools: manifests, executors, runtimes, inspectors, renders, streaming and intervention.
agent-work
Use for the Agent Work output registry: registration, deduplication, cards, skill providers, CLI scanners and Work types.
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.
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-runtime-hooks
Use for agent lifecycle hooks, tool mocks, intervention, sub-agent calls and context compression.
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.
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.
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.
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-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.
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.
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
microsoft-docs
Fråga den officiella Microsoft-dokumentationen för att hitta koncept, handledningar och kodexempel inom Azure, .NET, Agent Framework, Aspire, VS Code, GitHub och mer. Använder Microsoft Learn MCP som standard, med Context7 och Aspire MCP för innehåll som finns utanför learn.microsoft.com.
local-ai-agents
Bumuo ng mga local-first AI agents na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry Local at Qwen function-calling models. Saklaw nito ang Small Language Models (SLMs), ang OpenAI-compatible na lokal na endpoint, sandboxed local tools, lokal na RAG gamit ang Chroma, lokal na MCP servers, hybrid cloud/local routing, at ang privacy/cost/offline trade-offs. Batay sa Lesson 17 ng AI Agents for Beginners. GAMITIN PARA SA: pagpapatakbo ng agent nang lokal, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, lokal na RAG, Chroma vector database, lokal na MCP server, privacy-preserving agent, hybrid local at cloud agent, small language model agent, engineering assistant sa aking makina. HUWAG GAMITIN PARA SA: pag-deploy ng mga agents sa cloud nang malakihan (gamitin ang deploying-scalable-agents / Lesson 16), paggawa ng iyong unang agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.
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
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.
agent-arch-system-design
Agent skill for arch-system-design - invoke with $agent-arch-system-design
agent-analyze-code-quality
Agent skill for analyze-code-quality - invoke with $agent-analyze-code-quality
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
cognitive-pattern
Define and manage cognitive patterns for agent reasoning and decision-making
cross-host-federation
Join and operate a signed cross-host agentbbs federation, and coordinate work claims across nodes. Use when: connecting ruflo agents across machines, sharing status/tasks/results between hosts, propagating work claims across a swarm, or standing up a federation hub. Skip when: single-host local work with no other nodes to coordinate with.
harness-genome
7-section repo readiness report from `metaharness genome {path}`. Returns repo_type / agent_topology / risk_score / mcp_surface / test_confidence / publish_readiness. Pure-read; degrades gracefully (ADR-150).
harness-mint
Scaffold a custom AI agent harness via `metaharness new {name} --template {id} --host {id}`. Defaults to DRY-RUN (no writes) unless --confirm is passed. Refuses to write to the calling repo root or anywhere inside it. Honors ADR-150 architectural constraint + ruflo's "destructive-action confirmation" pattern.
harness-security-bench
Run `@metaharness/darwin security bench` (upstream "Darwin Shield" / ADR-155) — evolves a champion security-detection harness against a 10-vuln / 9-decoy corpus and grades it on TPR/FPR/patch-pass/repro/unsafe vs four baselines (B0 static, B1 LLM-single-pass, B2 fixed-agent, B3 Darwin-champion). Closest reference implementation for ruflo's own ADR-155 nightly self-learning security harness (PR
harness-similarity
ADR-152 — weighted similarity between two harness fingerprints (genome + score JSON). Returns overall score in [0,1] plus per-component breakdown (cosine over 9 numerics, categorical agreement over 4 enums, jaccard over agent_topology). Unblocks ADR-151 §3.2 Recommender, §3.3 Drift Detection, §3.5 Plugin Compat. Pure-TS, no `@metaharness/*` dep — preserves ADR-150's four architectural constraints.
init-project
Initialize a new Ruflo project with MCP tools, hooks, and agent configuration. Use when setting up Ruflo in a fresh repo, or when the user says "init ruflo", "set up ruflo", or asks how to bootstrap the MCP server, hooks, and agent configs from scratch.
pod-sales
Run one tick of the sales business-pod (ADR-164 §4.1, Phase 2). Loads templates/sales.json, validates it against the pod-schema, resolves agents against ruflo's agent registry, reserves budget via the Phase-2 file-based stub ledger (atomic SQLite tracker is Phase 3 per ADR-164.1), constructs per-agent dry-run prompts, posts a summary envelope to room "sales" via the federation_bbs_publish JSONL backing store, and emits a structured {podName, tickId, agentsRan, totalUsd, envelopeId, status} line for /loop ingestion. Dry-run by default; --live is reserved for Phase 3.
reasoningbank-with-agentdb
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
review-team-deliverables
Checks an AI team run against its acceptance criteria using the evidence bundle. Use when the user asks whether the work is done or correct, or before completing a run. Separates verified evidence from agent claims.
ruflo-status
Diagnose Ruflo health, then report system, MCP server, and active-agent status without changing the installation
ruos-host-run
Run a ruflo swarm agent on the user's own ruOS cloud desktop through the tenant-authenticated fleet MCP, stream its output, and record it in ruflo's swarm state. Use when the user wants an agent to run on a ruOS desktop, asks to "run this on my ruOS desktop", or places a swarm agent on a remote ruOS host.
swarm-orchestration
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.
tdd-repair
Test-Driven Repair — given a failing test, spawn a bounded headless `claude -p` (Read/Edit/Bash only) that makes the test pass without modifying it. Modeled on agent-harness-generator's ADR-175 Test-Driven Repair mode. Bounded cost via --max-budget-usd, bounded capability via --allowedTools. Closes the loop the TDD plugins didn't — we generate tests, this fixes the code to satisfy them.