- 📄 SKILL.md
- 📄 svg-widgets.yaml
Use this skill when asked to audit, assess, or report on AI agent security posture across Copilot Studio and Microsoft 365 Copilot agents. Triggers on keywords like "AI agent posture", "agent security audit", "Copilot Studio agents", "agent inventory", "agent authentication", "unauthenticated agents", "agent tools", "MCP tools on agents", "agent knowledge sources", "XPIA risk", "agent sprawl", "AI agent risk", "agent governance", or when investigating AI agent configurations, access policies, tool permissions, or credential exposure. This skill queries the AIAgentsInfo table in Advanced Hunting to produce a comprehensive security posture assessment covering agent inventory, authentication gaps, access control misconfigurations, MCP tool proliferation, knowledge source exposure, XPIA email exfiltration risk, hard-coded credential detection, HTTP request risks, creator governance, and agent sprawl analysis. Supports inline chat and markdown file output.
Guide for creating new DAAF agent definition files with full ecosystem integration. Use when adding a new specialized agent, revising agent structure, or verifying agent integration completeness across documentation. --- # Agent Authoring Create new DAAF agents that conform to the canonical template and are fully wired into the system documentation for discoverability and usability. ## What This Skill Does - Guides creation of agent `.md` files conforming to `agent_reference/AGENT_TEMPLATE.md` (12 mandatory sections) - Ensures cross-agent consistency (standardized confidence model, Learning Signal, STOP format, etc.) - Provides a **complete integration checklist** covering every file that references agents across the codebase to ensure it is discoverable and its invocation patterns are well-understood by the system agents - Complements `skill-authoring`: this skill handles the behavioral protocol file; if the new agent also needs a companion skill, invoke `skill-authoring` separately ## Decision Tree: What Do You Need? ``` What are you doing? │ ├─ Creating a brand-new agent │ └─ Follow "New Agent Workflow" below │ ├─ Revising an existing agent to match the template │ └─ Read: references/template-walkthrough.md │ + agent_reference/AGENT_TEMPLATE.md (the canonical blueprint) │ ├─ Checking if an agent is fully integrated into the ecosystem │ └─ Read: references/integration-checklist.md │ ├─ Understanding what must be identical across all agents │ └─ Read: references/cross-agent-standards.md │ └─ Understanding the current agent landscape before adding to it └─ Read: agents/README.md (Agent Index + "Commonly Confused Pairs") ``` ## New Agent Workflow ### Phase 1: Design (before writing) Before beginning, you MUST have a clear, coherent, and compelling answer to each of the following questions: 1. **Define the role** in one sentence — what does this agent do and why does it exist? 2. **Identify pipeline stage(s)** — which stage(s) does it operate in, or i
- 📁 assets/
- 📁 config/
- 📁 core/
- 📄 __init__.py
- 📄 _meta.json
- 📄 anima_doctor.py
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。
- 📁 references/
- 📄 evals.json
- 📄 README.md
- 📄 SKILL.md
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.
- 📁 assets/
- 📁 references/
- 📄 SKILL.md
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.
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".
- 📁 roles/
- 📁 specs/
- 📄 SKILL.md
Universal team coordination skill with dynamic role generation. Uses team-worker agent architecture with role-spec files. Only coordinator is built-in -- all worker roles are generated at runtime as role-specs and spawned via team-worker agent. Beat/cadence model for orchestration. Triggers on "Team Coordinate ".
- 📄 __init__.py
- 📄 manifest.json
- 📄 SKILL.md
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.
- 📁 references/
- 📄 schemas
- 📄 SKILL.md
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
Manage Bernstein agents — list active agents, inspect their output, kill stalled agents, or stream live logs. Use when the user asks about agents, wants to see what an agent is doing, or needs to kill one. --- # Bernstein Agent Management Inspect, monitor, and control active Bernstein agents. ## When to Use - User asks "what agents are running?" or "show me the agents" - User wants to see what a specific agent is working on - User says "kill that agent" or "stop the backend agent" - User asks "why is that agent stuck?" or wants to inspect agent output - User wants to see agent logs ## Instructions ### List agents 1. Run `scripts/agents.sh list` to get all active agents. 2. Present them clearly: ``` ## Active Agents (3) | Agent | Role | Model | Status | Task | Runtime | Cost | |-------|------|-------|--------|------|---------|------| | ses-a1b2 | backend | claude-sonnet-4 | alive | TASK-042: Fix auth | 4m 12s | $0.32 | | ses-c3d4 | qa | gpt-4.1 | alive | TASK-043: Write tests | 2m 45s | $0.18 | | ses-e5f6 | frontend | claude-sonnet-4 | stalled | TASK-044: Update UI | 8m 03s | $0.51 | ``` ### Inspect agent 3. To see what an agent is doing: `scripts/agents.sh logs <session_id>` 4. Show the last ~20 lines of output. ### Kill agent 5. To kill a stalled or misbehaving agent: `scripts/agents.sh kill <session_id>` 6. Confirm: "Agent ses-e5f6 terminated. Task TASK-044 returned to open queue." ### Stall detection 7. If any agent shows `stalled` status, proactively suggest killing it. 8. An agent is stalled if it hasn't sent a heartbeat in >60 seconds.
