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Found 15 Skills
CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.
Creates missing instruction files (CLAUDE.md, AGENTS.md, GEMINI.md), audits token budget, prompt cache safety, cross-agent consistency. Use after setup or when instruction files need alignment.
MindOS Knowledge Base Operation Guide (Chinese) for Agent tasks on local markdown/csv knowledge bases. It should be automatically triggered whenever tasks involve note files, SOP/workflow documents, profile/context documents, CSV tables, knowledge base organization, cross-Agent handover or decision synchronization, and are executed via the MindOS MCP tool. Typical requests include "update notes", "search knowledge base", "organize files", "execute SOP", "review according to team standards", "hand over tasks to another Agent", "synchronize decisions", "append to CSV", "retrospect this conversation", "extract key experiences", "adaptively update retrospective results to corresponding documents", "route this information to corresponding files", "synchronously update all related documents", etc.; it should be triggered even if the user does not explicitly mention MindOS.
This skill should be used when agent work needs file-backed context: durable scratchpads, tool-output offloading, just-in-time discovery, cross-agent handoff files, filesystem memory, or cleanup policies for context stored outside the prompt.
Use this skill to manage already-installed skills across Claude Code, Codex, Gemini, OpenCode, OpenClaw, Cursor, Copilot, and other configured agent tools by comparing skill status and linking from configured source directories such as ~/.cc-switch/skills/ and ~/.agents/skills/. Trigger it in two major cases: first, when the user wants to sync, remove, repair, or align skills or agent skills across multiple agents; second, when the user does not yet know the current skill state and wants to inspect skill differences, missing skills, per-agent skill coverage, per-skill coverage, or decide what skill changes to make next. Use this skill when the topic is cross-agent skill or agent-skill management, not for general agent comparison, general model capability questions, or creating, editing, or installing skills from GitHub.
Install a single Skill or a collection of Skills to general Agents, Claude Code, Codex, WorkBuddy, Grok, Hermes Agent, Kiro, Qwen Code, Cline and other Agents. Use this when users request cross-Agent installation, synchronization, viewing, deduplication, or uninstallation of Skills.
Ecosystem self-evolution orchestrator. Detects project lifecycle phases, evaluates agent relevance, synthesizes cross-agent knowledge, and proposes evolution actions (health checks, fitness scoring, evolution proposals).
Analyze codebase structure, dependencies, changes, and cross-agent handoffs. Use when user asks about project structure, where code is located, how files connect, what changed, how to resume work, or before starting any coding task.
Give sparse, evidence-based progress updates for long-running agent tasks and projects. Use when work will take more than 15 minutes, spans multiple milestones, involves delegated agents, or when the user asks for progress bars, status cadence, fewer updates, or a global cross-agent progress rule. Not for short answers or tasks with no meaningful intermediate state.
Manage the user's shared agent-skill library via skills-manager-cli — install, update, remove, deploy or undeploy skills per agent, manage presets, organize tags, search, and adopt existing skills. Use this whenever the user wants Claude Code, Codex, Cursor, or another agent to gain or lose a skill, wants to organize the central library, or asks what is installed or deployed. Prefer this over direct agent-folder installs because Skills Manager preserves source metadata, preset membership, updates, and cross-agent deployment state.
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Report portable vs runtime-specific skill fields across supported harnesses. Use when auditing cross-agent compatibility. NOT for live installs or packaging.