Loading...
Loading...
Found 11 Skills
Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use developing-agentforce); writes or runs test specs (use testing-agentforce); uses sf agent preview for local development iteration; deploys or publishes agents.
Build autonomous game-playing agents using AI and reinforcement learning. Covers game environments, agent decision-making, strategy development, and performance optimization. Use when creating game-playing bots, testing game AI, strategic decision-making systems, or game theory applications.
Summarize lessons learned from ccbox session logs (projects/sessions/history/skills) so the agent can do better next time. Produce copy-ready instruction updates (project + global) backed by evidence, with optional skill-span context to attribute failures to specific skills. Use when asked to run /ccbox:insights, generate a "lessons learned" memo, or propose standing instructions from session history.
Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing agent tool sets.
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.
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.
Start a repo-local OptimizeSpec self-improvement change. Use when the user wants to create evals, optimize an agent with GEPA, define an agent self-improvement loop, or begin an ASI-first evaluation workflow.
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".
Agent Design Consultant and Review Tool. Based on 12-Factor AgentOps best practices, it is used for: (1) Discussing Agent architecture design solutions; (2) Reviewing the design of existing Agents/Skills/workflows, identifying issues, and providing improvement suggestions. Trigger phrases: Review my agent, Help me analyze this skill, Agent design, Agent optimization, Help me review this workflow, What's wrong with this agent, How to design an agent, Agent architecture consultation.
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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.