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Found 409 Skills
LangGraph workflow patterns for state management, routing, parallel execution, supervisor-worker, tool calling, checkpointing, human-in-loop, streaming, subgraphs, and functional API. Use when building LangGraph pipelines, multi-agent systems, or AI workflows.
Manage agent fleet through CRUD operations and lifecycle patterns. Use when creating, commanding, monitoring, or deleting agents in multi-agent systems, or implementing proper resource cleanup.
Project development lifecycle management with a strict three-phase workflow (investigate -> proposal -> implement), file-based plan tracking in docs/plan/, task tracking in docs/task/, and claim-before-work multi-agent coordination. Use when handling feature development, bug fixes, refactors, planning, progress tracking, or multi-agent execution in an existing codebase. Supports English and Chinese project templates.
Persistent cross-session task queue for AI agents using Claude Code Tasks schema. Add, claim, complete, and reassign tasks with move-based locking, dependency tracking (blocks/blockedBy), conversation transcript linking, and staleness detection. Use for: (1) saving tasks for future agent sessions, (2) cross-session task persistence, (3) multi-agent task coordination, (4) linking conversation transcripts to tasks. Triggers: task queue, save task, agent task, queue task, persistent task, cross-session task, task for agent.
Parallel read-only multi-agent review of a current git diff or explicit file scope to find behavioral regressions, security or privacy risks, performance or reliability issues, and contract or test coverage gaps. Use when the user asks for a review swarm, parallel review, diff review, regression review, security review, or wants high-signal issues plus a prioritized fix path without editing files.
Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases. Triggers on: new/empty project setup for AI agents, AGENTS.md or CLAUDE.md creation, harness engineering questions, making agents work better on a codebase. ALSO triggers when users are frustrated or complaining about agent quality — e.g. 'the agent keeps ignoring conventions', 'it never follows instructions', 'why does it keep doing X', 'the agent is broken' — because poor agent output almost always signals harness gaps, not model problems. Covers: context engineering, architectural constraints, multi-agent coordination, evaluation, long-running agent harness, and diagnosis of agent quality issues.
Declarative workflow orchestration for multi-agent tasks. Activate when users need to coordinate multiple agent jobs, run parallel tasks, or create reusable automation pipelines.
This skill should be used when the user wants to implement features or fix bugs using test-driven development. Enforces the RED-GREEN-REFACTOR cycle with vertical slicing, context isolation between test writing and implementation, human checkpoints, and auto-test feedback loops. Uses multi-agent orchestration with the Task tool for architecturally enforced context isolation. Supports Jest, Vitest, pytest, Go test, cargo test, PHPUnit, and RSpec.
Track, optimize, and control token consumption across multi-agent systems. Covers budget allocation, real-time monitoring, cost attribution, per-agent limits, and proactive cost optimization for production LLM deployments.
Master skill for SynkOS multi-agent orchestration. Use whenever you need to spawn panes, delegate work to agents, manage parallel execution, coordinate multi-model squads, or use todo_manager.
Design and implement agent-based models (ABM) for simulating complex systems with emergent behavior from individual agent interactions. Use when "agent-based, multi-agent, emergent behavior, swarm simulation, social simulation, crowd modeling, population dynamics, individual-based, " mentioned.
Build production-ready AI agents using Google's Agent Development Kit with AI assistant integration, React patterns, multi-agent orchestration, and comprehensive tool libraries. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.