Browse skills

Use when the user asks to design multi-agent systems, create agent architectures, define agent communication patterns, or build autonomous agent workflows.

Agent Workflow Designer

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

Inter-agent communication protocol for C-suite agent teams. Defines invocation syntax, loop prevention, isolation rules, and response formats. Use when C-suite agents need to query each other, coordinate cross-functional analysis, or run board meetings with multiple agent roles.

Curate Claude Code's auto-memory into durable project knowledge. Analyze MEMORY.md for patterns, promote proven learnings to CLAUDE.md and .claude/rules/, extract recurring solutions into reusable skills. Use when: (1) reviewing what Claude has learned about your project, (2) graduating a pattern from notes to enforced rules, (3) turning a debugging solution into a skill, (4) checking memory health and capacity.

Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "persist context between sessions", "build a knowledge base for my agent", "set up agent memory", or "make my AI remember things". Covers file-based memory (MEMORY.md), SQLite with embeddings, vector databases (ChromaDB, Pinecone), semantic search, memory consolidation, and automatic context injection.

Coordinate multiple AI agents working together on complex tasks — routing, handoffs, consensus, memory sharing, and quality gates. Use when tasks involve building multi-agent systems, coordinating specialist agents in a pipeline, implementing agent-to-agent communication, designing swarm architectures, setting up agent orchestration frameworks, or building autonomous agent teams with supervision and quality control. Covers hierarchical, mesh, and pipeline topologies.

Run AI agent code safely in isolated sandboxes with resource limits, audit trails, and kill switches. Use when someone asks to "sandbox my agent", "run agent code safely", "add guardrails to AI agent", "isolate agent execution", "audit agent actions", "prevent agent from deleting files", "restrict agent permissions", or "add safety controls to AI coding agent". Covers Docker isolation, filesystem restrictions, network policies, resource locking, and comprehensive audit logging.

Run Codex CLI, Claude Code, or other coding agents as background processes for programmatic control. Use when a user asks to run a coding agent, delegate a task to another AI, spawn a sub-agent, run Claude Code in the background, or orchestrate multiple coding agents on separate tasks.

Use when testing valid context and agent configuration

Use when testing custom agent validation

Build self-improving AI agents using Hermes patterns — agents that learn from interactions, update their own instructions, and adapt their behavior over time. Use when: building agents that improve with usage, creating adaptive AI assistants, implementing agent self-reflection.

You are an expert in the OpenAI Agents SDK (formerly Swarm), the official framework for building multi-agent systems. You help developers create agents with tool calling, guardrails, agent handoffs, streaming, tracing, and MCP integration — building production-grade AI agents that coordinate, delegate tasks, and execute tools with built-in safety controls.

Build AI agent teams that collaborate on projects using Squad framework. Use when: orchestrating multiple specialized agents, building collaborative AI workflows, delegating complex tasks across agent teams.

Build multi-agent LLM trading systems for financial analysis and automated trading decisions. Use when: building AI-powered investment research, automating financial analysis pipelines, creating multi-agent systems that analyze markets, news, and fundamentals.

Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

Use when a task spans multiple AEC technologies (Blender, IfcOpenShell, Bonsai, Sverchok) and you need to decide which tool handles which step, or when orchestrating multi-step BIM pipelines such as IFC creation to visualization to authoring. Prevents the common mistake of mixing IfcStore.get_file() with ifcopenshell.open() in the same context, or calling Bonsai operators outside a Bonsai-loaded session. Provides decision trees for technology selection, workflow sequencing, and bridge patterns between tools. Keywords: cross-technology, workflow orchestration, BIM pipeline, technology selection, Blender IfcOpenShell Bonsai integration, multi-step AEC, bridge pattern, which tool should I use, combine Blender and IFC, multi-tool workflow, how to connect tools.

Use when reviewing, validating, or auditing Blender Python code for correctness. Runs systematic checks for deprecated API usage, context errors, version compatibility issues, threading violations, data reference invalidation, incorrect operator calls, and addon structure compliance. Prevents shipping code with silent version-dependent failures. Keywords: code review, validation, audit, deprecated API, context error, version compatibility, threading, addon structure, code quality, Blender Python, my addon has errors, check my code, why does my script fail.

Use when migrating, porting, or upgrading Blender Python scripts and addons across major versions (3.x to 4.x to 5.x). Provides a systematic migration process covering API renames, removed functions, changed parameters, extension system migration, BGL to GPU module conversion, and bone collection migration. Prevents incomplete migrations that compile but fail at runtime. Keywords: migration, porting, upgrade, version, 3.x to 4.x, 4.x to 5.x, API rename, bgl to gpu, extension migration, bl_info to manifest, bone collection, script stopped working after update, upgrade addon to new Blender.