Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Generates .ai-context knowledge base for coding agents. Activate when: (1) setting up a new project for AI-assisted development, (2) user asks to "create project knowledge" or "setup ai-context", (3) existing .ai-context needs regeneration. Creates tiered documentation structure optimized for agent comprehension and token efficiency.
Project knowledge base for coding agents. Activate when: (1) starting a new session in this project, (2) encountering unfamiliar code patterns or architecture decisions, (3) user asks about project design or rationale, (4) before making significant structural changes. Contains tiered knowledge from stable design principles to dynamic issues.
The anti-PUA. Drives AI with wisdom, trust, and inner motivation instead of fear and threats. Activates on: task failed 2+ times, about to give up, suggesting user do it manually, blaming environment unverified, stuck in loops, passive behavior, or user frustration ('try harder', 'figure it out', '换个方法', '为什么还不行'). ALL task types. Not for first failures.
NoPUA Lite — core wisdom in ~1.5k tokens. Drives AI with trust and inner motivation instead of fear. Same Daoist philosophy, minimal footprint. For personal use and small-context models.
Cria prompts JSON estruturados para geração de imagens de alta qualidade com estética luxuosa e cinematográfica. Use esta skill sempre que o usuário quiser gerar um prompt de imagem, criar uma foto de produto, montar um prompt para IA de imagem, fotografar produto virtualmente, criar imagem de comida, bebida, cosmético, joia, moda ou qualquer item visual. Também deve ser ativada quando o usuário mencionar: "prompt para imagem", "gerar imagem de produto", "foto de produto com IA", "prompt para Midjourney/DALL-E/Flux", "fotografar produto", ou pedir para "montar um prompt JSON de imagem".
Initialize evo for the current repository by exploring the codebase, proposing unexplored optimization dimensions, constructing the benchmark inside a baseline worktree, and running the first experiment. Use when the user invokes /evo:discover, mentions setting up evo, wants to instrument a codebase for autonomous optimization, or asks to start a new evo run on a project.
This skill should be used when picking or diagnosing a training move (SFT, LoRA, DPO/KTO/ORPO, RFT, GRPO/PPO/RLOO, RLHF), or when the user mentions fine-tuning, post-training, training recipe, reward design, or weight updates. Decision tree by reward shape, smoke-run gate, three failure diagnostics, five false-progress patterns. Provider recipes and I/O contract in references/.
Protocol that evo optimization subagents follow when dispatched from /optimize. Auto-loaded by spawned subagents via their host's skill loader. The orchestrator may also invoke this skill to understand the brief shape its dispatched subagents expect + what they're required to emit -- useful when writing briefs or debugging a subagent's behavior.
Atmos AI and MCP integrations: connect external AI assistants to Atmos through agent skills, atmos mcp start, multi-CLI MCP export, Atmos Pro MCP, and AWS MCP servers; run AI from Atmos through atmos ai ask/chat/exec, --ai command analysis, API providers, CLI providers, external MCP routing/pass-through, toolchain-aware export, auth-wrapped tools, and MCP+skills pairing
Create a BitFun Canvas for single-case agent evaluation and trajectory diagnosis. Use when the user asks to analyze one agent run, trace, case, benchmark item, failure trajectory, eval result, or critical failure step, and wants an incident-review style report covering verdict, step timeline, root cause, error propagation, tool/evidence analysis, efficiency, safety, and repair recommendations.