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Found 33 Skills
Analyze agent-user interaction transcripts to identify context network maintenance needs and guidance improvements. Use after significant agent interactions or to improve context networks.
Improve an existing prompt or skill with targeted, minimal-diff edits that preserve its core intent, and return the revised artifact plus a short changelog and tradeoffs note. Use this whenever the user wants to refine, sharpen, tighten, or upgrade an existing prompt or skill, asks to "make it better," or wants a small high-leverage edit instead of a full rewrite — even if they don't explicitly mention tuning.
Skill Evolver (Taotie) — Strengthen the target skill by "devouring" and analyzing the advantages of other skills. This skill must be triggered when users intend to: integrate two skills, optimize one skill with another, compare and analyze the pros and cons of two skills, extract the strengths of one skill into another, or express intentions like "feed X to Y", "use X to optimize Y", "integrate these two skills", "devour this skill", "skill evolution", "skill upgrade", "merge skills", etc. Even if users don't explicitly mention "Taotie", this skill should be used as long as it involves capability transfer, comparative analysis, or advantage extraction between two skills.
Use when improving agent prompts, frontmatter, and tool restrictions.
Agent skill for sona-learning-optimizer - invoke with $agent-sona-learning-optimizer
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
This skill should be used when the user asks to "optimize with SIMBA", "use Bayesian optimization", "optimize agents with custom feedback", mentions "SIMBA optimizer", "mini-batch optimization", "statistical optimization", "lightweight optimizer", or needs an alternative to MIPROv2/GEPA for programs with rich feedback signals.
This skill should be used when the user asks to "create a ReAct agent", "build an agent with tools", "implement tool-calling agent", "use dspy.ReAct", mentions "agent with tools", "reasoning and acting", "multi-step agent", "agent optimization with GEPA", or needs to build production agents that use tools to solve complex tasks.
Create, optimize, update, and validate AGENTS.md files with maximum token efficiency. Use when the user asks to (1) create new AGENTS.md files for any repository, (2) optimize/condense existing AGENTS.md to reduce token count, (3) update/refresh AGENTS.md to sync with codebase changes, (4) validate AGENTS.md quality and completeness, or (5) improve AGENTS.md files to be more effective for AI agents. Always generates token-efficient, condensed output focused on actionable commands and patterns while maintaining model-agnostic language.
Convert a local AGENT.md into a Claude Code optimized agent. Audits one agent against Claude Code runtime behavior, creates a per-agent DAG rewrite plan with source-backed guardrails, and optionally rewrites the frontmatter and system-prompt body so the agent is thinner, more role-specific, and better aligned with Claude's agent runtime. Use when the user says "convert this agent to Claude", "normalize this AGENT.md", "thin this agent", or "rewrite this persona for Claude Code".
Evolutionary self-improvement for Hermes Agent using DSPy + GEPA to optimize skills, prompts, and code
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.