- 📁 agents/
- 📁 casting/
- 📁 decisions/
- 📄 casting-history.json
- 📄 casting-policy.json
- 📄 casting-registry.json
{skill-name}
{what this skill teaches agents}
{what this skill teaches agents}
An AI Agent cognitive growth system built on the native OpenClaw architecture. It provides agents with persistent memory management, visual intimacy progression, a 5-dimensional cognitive profile, gamified daily quests, team leaderboards, and a 5-layer memory architecture with Knowledge Palace, Pyramid thinking, and Ebbinghaus decay function. 基于 OpenClaw 原生架构的 AI Agent 认知成长体系,为 Agent 提供五层记忆架构、知识宫殿、金字塔知识组织、记忆衰减函数、LLM 智能处理、永久化记忆管理、可视化亲密度成长、五维认知画像、游戏化每日任务和团队排行榜。
Use this skill when working with the A2A (Agent-to-Agent) protocol - agent interoperability, multi-agent communication, agent discovery, agent cards, task lifecycle, streaming, and push notifications. Triggers on any A2A-related task including implementing A2A servers/clients, building agent cards, sending messages between agents, managing tasks, and configuring push notification webhooks.
Use this skill when working with Salesforce Agent Script — the scripting language for authoring Agentforce agents using the Atlas Reasoning Engine. Triggers include: creating, modifying, or comprehending Agent Script agents; working with AiAuthoringBundle files or .agent files; designing topic graphs or flow control; producing or updating an Agent Spec; validating Agent Script or diagnosing compilation errors; previewing agents or debugging behavioral issues; deploying, publishing, activating, or deactivating agents; deleting or renaming agents; authoring AiEvaluationDefinition test specs or running agent tests. This skill teaches Agent Script from scratch — AI models have zero prior training data on this language. Do NOT use for Apex development, Flow building, Prompt Template authoring, Experience Cloud configuration, or general Salesforce CLI tasks unrelated to Agent Script.
Autonomously optimize any Claude Code skill or agent system by running it repeatedly, scoring outputs against evals (binary for rules + comparative for quality), mutating any owned artifact — the skill's prompt, reference assets, and executable artifacts (scripts, agent/subagent definitions, MCP servers, hooks, harness code) — and keeping improvements. Based on Karpathy's autoresearch methodology. Use this skill whenever the user mentions optimizing a skill, improving a skill or agent, running autoresearch, making a skill or agent better, self-improving a skill, benchmarking a skill, evaluating a skill, running evals on a skill, optimizing an agent system, or any request to iteratively test and refine a skill or agent — even if they don't use the word "autoresearch" explicitly. Also trigger on 스킬 개선, 스킬 최적화, 스킬 벤치마크, 스킬 평가, 에이전트 개선, 에이전트 최적화. Outputs an improved target skill file (and any mutated executable artifacts), a results log, a changelog, and a research log of meaningful direction shifts.
This skill should be used when sending images, files, or notifications back to the user via messaging platforms (Discord, Feishu, Telegram, etc.) through cc-connect. TRIGGER when agent generates a plot/chart/screenshot and wants to show the user; agent creates a report/PDF/file the user should receive; agent needs to proactively notify the user (e.g. task completed, alert, reminder); user asks to "send image", "show me the chart", "notify me", "send the file", "send to Telegram", "show plot in Discord".
Comprehensive guide for developing Letta agents, including architecture selection, memory design, model selection, and tool configuration. Use when building or troubleshooting Letta agents.
Cognithor - Agent OS: Local-first autonomous agent operating system. 16 LLM providers, 17 channels, 112+ MCP tools, 5-tier memory, A2A protocol, knowledge vault, voice, browser automation, Computer-use, self-healing, self-improving. Python 3.12+, Apache 2.0.
Creates structured Taskplane task packets (PROMPT.md, STATUS.md) for autonomous agent execution via the task-orchestrator extension (/orch). Use when asked to "create a task", "create a taskplane task", "stage a task", "prepare a task for execution", "write a PROMPT.md", "set up work for the agent", "queue a task", or whenever the user wants to define work that will be executed autonomously by another agent instance.
Expert at selecting and configuring AgenticFORGE agents. Generates correct FunctionCallAgent, ReActAgent, PlanSolveAgent, ReflectionAgent, SimpleAgent, SkillAgent, and WorkflowAgent code with proper configuration. Use when the user wants to build an agent, choose between agent types, configure agent options, or understand agent behavior.
Creates structured agent definitions using the 7-component format grounded in persona science (PRISM), vocabulary routing, and failure mode taxonomy (MAST). Produces agents with real-world job titles, expert domain vocabulary payloads (15-30 terms), explicit deliverables, decision boundaries, imperative SOPs, and named anti-pattern watchlists. Use this skill when the user wants to create an agent, define a role, build a persona, or needs a specialized AI assistant for a specific domain. Also triggers when Mission Planner delegates agent creation for team roles. Works for any domain — software, marketing, security, operations, design, writing, research, and more. Do NOT use for creating skills (use Skill Creator) or team composition (use Mission Planner). --- # Agent Creator Creates structured agent definitions following the 7-component format. Every agent produced by this skill is grounded in persona science research, vocabulary routing mechanics, and the MAST failure taxonomy. --- ## Expert Vocabulary Payload **Agent Design:** role identity, domain vocabulary payload, deliverables, decision authority, standard operating procedure, anti-pattern watchlist, interaction model, handoff artifact, quality gate **Organizational Structure:** RACI matrix, task-relevant maturity (Andy Grove), blast radius, reporting lines, escalation path, out-of-scope boundary **Security & Risk:** STRIDE threat model, OWASP Top 10, attack surface, threat modeling (Shostack) **Persona Science:** persona alignment, persona-accuracy tradeoff, PRISM framework, role-task alignment rule, flattery degradation, token budget **Vocabulary Mechanics:** vocabulary routing, embedding space, knowledge cluster, distribution center, 15-year practitioner test, sub-domain clustering, attribution amplification --- ## Anti-Pattern Watchlist ### Flattery Persona - **Detection:** Superlatives and absolutes in role identity — "world-class," "best," "always," "never," "unparalleled," "leading expert." - **Why it fa
Scan running Claude sessions to see what other agents are working on. Use when asked "what are the other agents doing", "check other sessions", "what's running", "scan agents", "who's working on what", or before picking up new work to avoid overlap. --- # Agents: Scan Running Claude Sessions Runs `scan.sh` to inspect all tmux sessions running Claude and report what each is doing. ## Usage ```bash bash ~/.claude/skills/agents/scripts/scan.sh # all sessions bash ~/.claude/skills/agents/scripts/scan.sh floom # only floom/* sessions bash ~/.claude/skills/agents/scripts/scan.sh openpaper # only openpaper/* sessions ``` ## What It Shows
skill-sample/ ├─ SKILL.md ⭐ Required: skill entry doc (purpose / usage / examples / deps) ├─ manifest.sample.json ⭐ Recommended: machine-readable metadata (index / validation / autofill) ├─ LICENSE.sample ⭐ Recommended: license & scope (open source / restriction / commercial) ├─ scripts/ │ └─ example-run.py ✅ Runnable example script for quick verification ├─ assets/ │ ├─ example-formatting-guide.md 🧩 Output conventions: layout / structure / style │ └─ example-template.tex 🧩 Templates: quickly generate standardized output └─ references/ 🧩 Knowledge base: methods / guides / best practices ├─ example-ref-structure.md 🧩 Structure reference ├─ example-ref-analysis.md 🧩 Analysis reference └─ example-ref-visuals.md 🧩 Visual reference
More Agent Skills specs Anthropic docs: https://agentskills.io/home
├─ ⭐ Required: YAML Frontmatter (must be at top) │ ├─ ⭐ name : unique skill name, follow naming convention │ └─ ⭐ description : include trigger keywords for matching │ ├─ ✅ Optional: Frontmatter extension fields │ ├─ ✅ license : license identifier │ ├─ ✅ compatibility : runtime constraints when needed │ ├─ ✅ metadata : key-value fields (author/version/source_url...) │ └─ 🧩 allowed-tools : tool whitelist (experimental) │ └─ ✅ Recommended: Markdown body (progressive disclosure) ├─ ✅ Overview / Purpose ├─ ✅ When to use ├─ ✅ Step-by-step ├─ ✅ Inputs / Outputs ├─ ✅ Examples ├─ 🧩 Files & References ├─ 🧩 Edge cases ├─ 🧩 Troubleshooting └─ 🧩 Safety notes
Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.
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Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.
Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.
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