Agents
1,401 skills.
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thought-based-reasoningneolabhq1.3KUse when tackling complex reasoning tasks requiring step-by-step logic, multi-step arithmetic, commonsense reasoning, symbolic manipulation, or problems where simple prompting fails - provides comprehensive guide to Chain-of-Thought and related prompting techniques (Zero-shot CoT, Self-Consistency, Tree of Thoughts, Least-to-Most, ReAct, PAL, Reflexion) with templates, decision matrices, and research-backed patternstoon-formatreason-machines1.3KExpert skill for Token-Oriented Object Notation (TOON) — compact, schema-aware JSON encoding for LLM prompts that reduces tokens by ~40%.rivet-actorsrivet-dev1.3KActors: The primitive for agent orchestration.reflectneolabhq1.3KReflect on previus response and output, based on Self-refinement framework for iterative improvement with complexity triage and verificationagent-swarmruvnet1.3KAgent skill for swarm - invoke with $agent-swarmsf-ai-agentforcejaganpro1.3KAgentforce Builder metadata path for Builder-managed topics/actions, Prompt Builder templates, GenAiFunction/GenAiPlugin, Models API, and custom Lightning types. TRIGGER when: user maintains or configures Builder metadata agents, creates topics/actions, works with Prompt Builder templates, or touches .genAiFunction, .genAiPlugin, or .genAiPromptTemplate metadata XML files. DO NOT TRIGGER when: Agent Script DSL .agent files (use sf-ai-agentscript), agent testing (use sf-ai-agentforce-testing), orai-engineersickn331.3KBuild production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.langgraphsickn331.3KExpert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.maintaining-agent-contextsentimony1.3KYou MUST use this when auditing, improving, restructuring, or maintaining a repository's agent instruction and context architecture - AGENTS.md, CLAUDE.md and its variants, .claude/rules/, the instruction layer of SKILL.md files, or docs linked from them - including reducing always-loaded context cost, finding stale, duplicated, or conflicting instructions, and keeping Claude Code or Codex project memory aligned with the codebase. Not for documentation written for human readers, and not for authmemory-lancedb-pro-openclawreason-machines1.3KExpert skill for memory-lancedb-pro — a production-grade LanceDB-backed long-term memory plugin for OpenClaw agents with hybrid retrieval, cross-encoder reranking, multi-scope isolation, and smart auto-capture.openclaw-rl-trainingreason-machines1.3KOpenClaw-RL framework for training personalized AI agents via reinforcement learning from natural conversation feedbacksession-analyzerai-native-camp1.3KThis skill should be used when the user asks to "analyze session", "세션 분석", "evaluate skill execution", "스킬 실행 검증", "check session logs", "로그 분석", provides a session ID with a skill path, or wants to verify that a skill executed correctly in a past session. Post-hoc analysis of Claude Code sessions to validate skill/agent/hook behavior against SKILL.md specifications.team-assembleai-native-camp1.3KThis skill should be used when the user asks to "팀 구성해줘", "team assemble", "전문가 팀으로 해줘", "팀으로 해줘", "swarm", "병렬로 전문가 팀", or wants to decompose a complex task into specialist roles executed via TeamCreate. Also triggers when user describes a task clearly benefiting from parallel expert execution.rewardkitharbor-framework1.3KWrite Harbor task verifiers using Reward Kit. Use when creating or editing a task's tests/ directory, adding grading criteria, setting up LLM/agent judges, or designing verifiers that produce a reward score.zeroboot-vm-sandboxreason-machines1.3KSub-millisecond VM sandboxes for AI agents using copy-on-write KVM forking via Zerobootnelsonaspegio1.3KOrchestrates multi-agent task execution using a Royal Navy squadron metaphor — from mission planning through parallel work coordination to stand-down. Use when work needs parallel agent orchestration, tight task coordination with quality gates, structured delegation with progress checkpoints, or a documented decision log.using-n8n-mcp-skillsczlonkowski1.3KUse when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, wfinancial-analysis-agentqodex-ai1.3KCreate agents for financial analysis, investment research, and portfolio management. Covers financial data processing, risk analysis, and recommendation generation. Use when building investment analysis tools, robo-advisors, portfolio trackers, or financial intelligence systems.openagent5dive-ai1.3KAuthor your own OpenAgent persona file and mint its shareable holo trading card — write, validate, tier and render `<id>.persona.yaml`. Use when creating or updating YOUR identity as a persona or card, or provisioning a live teammate from one.skill-optimizermcollina1.3KOptimizes AI skills for activation, clarity, and cross-model reliability. Use when creating or editing skill packs, diagnosing weak skill uptake, reducing regressions, tuning instruction salience, improving examples, shrinking context cost, or setting benchmark/release gates for skills. Trigger terms: skill optimization, activation gap, benchmark skill, with/without skill delta, regression, context budget, prompt salience.autonomous-agentssickn331.3KAutonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.multi-agent-patternsneolabhq1.2KDesign multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.mempalacemempalace1.2KMemPalace — mine projects and conversations into a searchable memory palace. Use when the user asks about MemPalace, memory palace, mining memories, searching memories, palace setup, wings, rooms, or drawers; or when they want to recall past work that may already be filed in their palace.n8n-code-toolczlonkowski1.2KWrite JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like \"Wrong output type returned\", \"No execution data availaagentspaceagentspace-so12See what your AI agent is doing, from anywhere. The agent keeps writing — logs, code, generated outputs, screenshots, artifacts. One command turns the folder into a live URL you (or a teammate) open in any browser to watch files evolve, edit in place, or comment — no sync, no zip, no account. Workspaces stay live 24 hours anonymously; one email claim keeps them permanent. Hosted on Cloudflare. Triggers on "show me what the agent is doing", "open the agent's folder", "share this folder", "give mefind-skillsagentspace-so12Discover, vet, and install agent skills by searching ACROSS every major registry at once — skills.sh, clawhub.ai, and GitHub — presenting each board on its own native metric (installs / stars) with the top entry per board, security-scanning the top candidates' real SKILL.md for risky patterns, and flagging what's already installed. Use when the user asks "how do I do X", "find a skill for X", "is there a skill that…", "what skill should I install for…", or wants to extend the agent with a capabitribunala-tokyoRuns a doer -> verifier-panel -> consensus loop to verify a deliverable before it ships. An orchestrator freezes acceptance criteria before implementation, dispatches a doer, then convenes a context-walled panel of independent verifiers - including an adversary with an explicit must-oppose mandate - for evidence-anchored review adjudicated to a SHIP / SHIP_WITH_CAVEATS / ITERATE / BLOCK / ESCALATE verdict logged to a ledger. Use for multi-agent verification of any artifact - code slices, plans, memory-managementaaron-he-zhuUse when the user asks to "remember project context", review saved findings, initialize runtime memory, archive stale work, reconcile notes, or erase a subject; manages authorized HOT/WARM/COLD working memory across all disciplines while preserving registry event ownership and privacy controls. Not for changing canonical registry facts - route those through the owning registry. 项目记忆/跨会话skills-index-snippetsaarononthewebCreate and maintain AGENTS.md / CLAUDE.md snippet indexes that route tasks to the correct dotnet-skills skills and agents (including compressed Vercel-style indexes).gitnexus-guideabhigyanpatwariUse when the user asks about GitNexus itself — available tools, how to query the knowledge graph, MCP resources, graph schema, or workflow reference. Examples: \"What GitNexus tools are available?\", \"How do I use GitNexus?\"slicc-handoffadobeUse this when the user asks to continue work in the SLICC browser agent or to install a skill into SLICC, for example \"handoff to slicc\", \"move to the browser\", \"test in the browser\", \"install this skill in slicc\", or \"upskill slicc with this repo\". Covers handing off the current task to the SLICC browser agent and installing a new skill into SLICC from a GitHub repo.agent-architecture-auditaffaan-mエージェントおよび LLM アプリケーション向けのフルスタック診断。12 層のエージェントスタックにおけるラッパーリグレッション、メモリ汚染、ツール規律の失敗、隠れた修復ループ、レンダリング破損を監査します。重要度順の発見事項とコードファーストの修正を生成します。エージェントアプリケーション、自律ループ、または LLM を活用した機能を構築する開発者に必須です。agent-evalaffaan-mカスタムタスクでコーディングエージェント（Claude Code、Aider、Codex など）をヘッドツーヘッドで比較し、合格率、コスト、時間、一貫性のメトリクスを測定しますagent-introspection-debuggingaffaan-mStructured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.agent-payment-x402affaan-mタスクごとのバジェット、支出コントロール、ノンカストディアルウォレットを備えた x402 決済実行を AI エージェントに追加します。agentwallet-sdk を通じて Base をサポートし、OKX Payments / OKX エージェント決済プロトコルを通じて X Layer をサポートします。agent-self-evaluationaffaan-mUse after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.agentic-engineeringaffaan-mOperate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls.agentic-osaffaan-mClaude Code 上に永続的なマルチエージェントオペレーティングシステムを構築します。カーネルアーキテクチャ、スペシャリストエージェント、スラッシュコマンド、ファイルベースのメモリ、スケジュールされた自動化、外部データベースなしの状態管理をカバーします。autonomous-loopsaffaan-mPatterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.blueprintaffaan-m1行の目的を複数セッション、複数エージェントエンジニアリングプロジェクト向けのステップバイステップ構築計画に変換します。各ステップには自己完結型コンテキストブリーフがあり、新しいエージェントがそれをコールドで実行できます。 敵対的なレビューゲート、依存グラフ、平行ステップ検出、アンチパターンカタログ、計画変更プロトコルを含みます。 トリガー：ユーザーが複雑なマルチPRタスク用の計画、ブループリント、またはロードマップをリクエストするか、複数のセッションが必要な作業を説明する場合。 トリガーしない場合：タスクが単一のPRまたは3未満のツール呼び出しで完成可能な場合、またはユーザーが「単にやってくれ」と言う場合。ckaffaan-mClaude Codeの永続的なプロジェクト単位のメモリ。セッション開始時にプロジェクトコンテキストを自動読み込み、gitアクティビティでセッションを追跡し、ネイティブメモリに書き込みます。コマンドは決定的なNode.jsスクリプトを実行します — 動作はモデルバージョン間で一貫しています。claude-devfleetaffaan-mClaude DevFleet経由でマルチエージェントコーディングタスクをオーケストレーション — プロジェクトを計画し、分離された作業ツリー内で平行エージェントを派遣し、進捗を監視し、構造化レポートを読む。context-budgetaffaan-mエージェント、スキル、MCPサーバー、ルールにわたってClaude Codeのコンテキストウィンドウ消費を監査します。肥大化、冗長なコンポーネントを特定し、優先順位付けされたトークン節約の推奨事項を生成します。continuous-agent-loopaffaan-m品質ゲート、評価、リカバリーコントロールを備えた継続的な自律エージェントループのパターン。continuous-learningaffaan-m[OBSOLETO - usar continuous-learning-v2] Extractor de skill por hook Stop v1 heredado. v2 es un superconjunto estricto con aprendizaje basado en instintos, con alcance de proyecto y hooks confiables. No invocar v1; dirigir solicitudes de aprendizaje continuo, aprendizaje de sesión y extracción de patrones a continuous-learning-v2.cost-aware-llm-pipelineaffaan-mLLM APIの使用量のコスト最適化パターン — タスクの複雑さによるモデルルーティング、予算追跡、リトライロジック、プロンプトキャッシング。council-multi-modelaffaan-mAdd one optional external Codex critique after the existing council has produced a decision draft. Use when an ambiguous, high-consequence decision would benefit from a separate model invocation's attempt to break the synthesis. Requires explicit consent before sending the compact draft and disagreement to OpenAI, labels same-provider reviews honestly, and marks the review absent when the adapter is unavailable.counterparty-channel-disciplineaffaan-mPer-channel strict prompts, mention gating, silent observation, and a communication autonomy policy for agents that sit in shared channels with external counterparties. Use when an agent joins group chats, shared channels, or DMs where outsiders can read every message and you need it to speak only when addressed, never leak internal context, and route risky content to draft-only approval.delivery-gateaffaan-mStop hook that blocks Claude from finishing until quality checks pass. Detects rationalization patterns (surface text heuristics), stale learning logs (filesystem mtime), and low disk space. Complements self-audit by mechanically enforcing learning capture habits. Use when Claude should be mechanically blocked from declaring work finished before quality checks and learning capture actually pass.dev-teamaffaan-mSimulate a collaborative dev team session where multiple role-based personas (PM, Architect, Developer, QA) respond to the same problem together in one session. Use when designing a feature, reviewing a proposal, or onboarding a new initiative and you want multi-role perspective without switching agents manually.