Loading...
Loading...
Found 571 Skills
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
Find and fix game performance problems methodically — measure with the engine profiler first, reason about the frame-time budget, locate the CPU-vs-GPU bottleneck, then apply the right fix: object pooling, draw-call batching, fewer allocations/GC spikes, and asset budgets. Engine- neutral method that pairs with each engine's profiler. Use when the user mentions performance, optimize, low/dropping FPS, frame drops, stutter, lag, profiler, frame budget, draw calls, batching, garbage collection/GC spikes, object pooling, or "the game runs slow".
Run fable-mode execution discipline on Claude Opus — the strongest staged run available. Routes the task to the @fable-orchestrator agent (Opus, Write-less), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on opus", "stage this on opus", "deep work mode, opus"). Do NOT use for ordinary single-pass tasks — and prefer fable-sonnet or fable-haiku when the task doesn't need peak reasoning.
Launch an intelligent sub-agent with automatic model selection based on task complexity, specialized agent matching, Zero-shot CoT reasoning, and mandatory self-critique verification
Apply causal inference whenever the user is interpreting metrics, debugging system behavior, reading A/B test results, or trying to understand whether an observed change was caused by an action or by something else. Triggers on phrases like "X caused Y", "since we deployed this, metrics changed", "the A/B test showed a lift", "why did this metric move?", "is this correlation or causation?", "we changed X and Y improved", "how do we know this worked?", "the data shows…", or any situation where conclusions are being drawn from observational data. Also trigger before any decision based on metric interpretation — confusing correlation with causation leads to interventions that don't work and misattribution of credit. Never assume causation without applying this skill.
Reflective sleep-and-dream heuristic for learning from recent experience. Use when the user asks to sleep on something, dream about it, reflect overnight, learn from yesterday, or extract lessons after a meaningful task, conversation, or debugging session. Avoid for first-pass analysis, simple factual lookups, direct execution, or tasks that do not benefit from reflection.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
Builds a week-by-week Q4 restock plan from August through November with FBA inbound delay buffers, peak velocity multipliers, and FBM fallback triggers. Q4 is 30-40% of annual revenue and FBA receiving takes 2-3 weeks during peak. Standard restock math stocks out at the worst possible moment. Use when a user asks about Q4 planning, Black Friday inventory, or Prime Big Deal Days restock. Trigger phrases: "Q4 restock plan", "Black Friday inventory", "Prime Big Deal Days restock", "Christmas Amazon inventory", "FBM fallback for FBA". Works with zero tools.
Graph of Thoughts (GoT) Controller - 管理研究图状态，执行图操作（Generate, Aggregate, Refine, Score），优化研究路径质量。当研究主题复杂或多方面、需要策略性探索（深度 vs 广度）、高质量研究时使用此技能。
Search and analyze X/Twitter posts using xAI's Grok API with real-time social media data. Use when the user needs to (1) search X/Twitter for specific topics, keywords, or trends, (2) analyze sentiment or discussions on X, (3) find posts from specific users or time periods, (4) research what people are saying about companies/products/events on X, or (5) gather social media insights from Twitter/X platform.
Apply IRAC (Issue, Rule, Application, Conclusion) method for structured legal analysis. Use this skill when the user needs to analyze a legal question systematically, write a legal memo, evaluate whether a law applies to a situation, or structure a legal argument — even if they say 'does this law apply', 'analyze this legal issue', or 'write a legal analysis'.
Use when extracting imperatives from agent instruction files, analyzing rule coverage, or preparing input for /policy-algebra and /distill.