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Found 118 Skills
Understanding Reinforcement Learning from Human Feedback (RLHF) for aligning language models. Use when learning about preference data, reward modeling, policy optimization, or direct alignment algorithms like DPO.
Expert blueprint for tower defense games (Bloons TD, Kingdom Rush, Fieldrunners) covering wave management, tower targeting logic, path algorithms, economy balance, and mazing mechanics. Use when building TD, lane defense, or tower placement strategy games. Keywords tower defense, wave spawner, pathfinding, targeting priority, mazing, NavigationServer baking.
Trade execution and best execution: venue selection, smart order routing, execution algorithms, transaction cost analysis (TCA), market microstructure, and best execution obligations.
Create mathematical animations with Manim Community Edition(manimce). Generates distinctive, production-grade animations that avoid generic "AI slop" aesthetics. Use when user wants to animate concepts, equations, illustrate proofs, visualize algorithms, create math explainers, or produce 3Blue1Brown-style videos.
Compute market-data and trading analytics with the `fintech-algorithms` npm package — 324 zero-dependency TypeScript algorithms covering technical indicators (RSI, MACD, moving averages, Bollinger Bands, ATR, OBV, Stochastic), candlestick and chart patterns, market breadth, bar construction from tick data, OHLC validation and cleaning, corporate actions, index and benchmark construction, market microstructure, matching engines, execution and TCA, statistical time series, on-chain metrics and EPS analytics. Use when asked to analyse a price series, compute or explain an indicator, detect a candlestick or chart pattern, build bars from ticks, validate or clean market data, wire up a market-data provider, or when writing code that needs any of these calculations to be correct rather than approximated.
Use when driving Xiaohongshu followers to private domains like WeChat, building offline communities, implementing follower retention strategies beyond platform, or creating sustainable audience ownership independent of platform algorithms
Principle-engineering posture for production-grade code: reads the repo first, plans before code, matches conventions, pulls latest docs over training recall, and ships the simplest correct change that holds the bar — proper algorithms and data structures, idempotent writes, schema+queries+indexes as one artefact, typed errors, tests in the same diff. Substrate-agnostic; defers to peer skills on their lanes. Use for non-trivial planning, design, implementation, review, or refactoring; RCA and debugging; performance and optimization work; changes touching a database schema, security, infrastructure, or a public API; hardening inherited, vibe-coded, or LLM-generated code (dependency/CVE and migration audits); and over-engineering cleanup ("simplest solution," "YAGNI," "what can we delete").
Analyze medication adherence and management platforms including dose tracking accuracy (MPR, PDC metrics), drug-drug and drug-food interaction checking completeness, refill prediction algorithms, dosage schedule optimization with conflict detection, caregiver notification escalation workflows, pharmacy system integration (NCPDP, HL7 FHIR), adverse event signal detection, smart dispenser integration, and alert fatigue mitigation for patient safety systems.
Grounds a coding agent's architecture decisions in real arXiv prior art before it builds something new. Reads arXiv category-wise via real HTTP fetch, spawns parallel isolated reads across the papers found, scores/clusters them, then converges on ONE recommended path with citations, a first step, and known prior-art pitfalls to avoid. Use on /neuroarxiv, before designing non-trivial architecture, algorithms, ML/systems techniques, or protocols, or when the user asks "has anyone solved this", "what's the state of the art", or "am I about to rebuild something that already exists". Skip for trivial CRUD, glue code, or closed phrasing ("just", "quick", "standard"). Full pre-flight gate is in the skill body.
Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.