Data analysis, machine learning, LLM apps, and prompt design (9.1K skills)
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
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Estimate a covariance / correlation / precision matrix incrementally with precise. Use when data arrives as a stream and you want the matrix updated per observation, or when you want an online (partial_fit) drop-in for sklearn.covariance, which is batch-only.
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Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.
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Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.
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Semantic memory and context - store and retrieve information with embeddings for similarity search. Use for long-term memory, context recall, and knowledge persistence.
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SQLi and NoSQLi hunting - error-based, boolean-blind, time-based, UNION, NoSQL operator injection. sqlmap automation after manual confirmation. Wiki-first, FIND schema output.
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LLM / AI application attack hunting - prompt injection (direct + indirect), excessive agency, insecure output handling, system-prompt + data leakage. OWASP LLM Top 10. Wiki-first, FIND schema output.
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精细化 AI 短剧 IP 创作技能（v0.6.0）。三阶段架构：Phase 1 创作（剧本+ref图，反复迭代）→ Phase 1.5 分镜图（每 grid 1-4 张候选静态图 = 视频首帧，工业级核心层）→ Phase 2 出片（按集解锁，4 模自动选）。v0.6.0 关键升级：① ref 库工艺偏置铁律（现代摩天楼易拟物，古建筑必出 chibi 人体，前期 IP 设计阶段就要避坑）② 即梦 5.0 失败模式 + 敏感词清单（3 类 fail 区分 / prompt 1500 字硬上限 / 反派词替换表 / 暧昧词清单）③ 分镜图 8 段 prompt 模板（CHARACTER/BACKGROUND/ACTION/SCENE/CAMERA/LIGHT/TEXT/STYLE）+ NOT humans 子句必加。沿用 v0.3.0 升级：Phase 1.5 分镜图层、4 模视频、ref 5-8 最优。v0.2.0：bash → Python subprocess、36 grid × 4-10s 变奏、红果必爆 7 招、工业级 ref 库 80-150 张、单 prompt 300-500 字。务必触发：用户提到短剧、微短剧、竖屏剧、AI 短剧、AI 漫剧、剧本创作、分镜、即梦/Seedance 出片、红果/番茄/抖音 IP 改编、爽剧、重生、穿越、赘婿、追妻、神医相师、AI 漫剧奇观、或"帮我做一部短剧"类请求。
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Use when polishing, diagnosing, tailoring, or exporting resumes for LLM, RAG, Agent, Agentic RL, post-training, pretraining, AIGC, search/ranking, multimodal, AI backend, or LLM algorithm internships from raw resume text, a materials folder, and/or a target job description. Audits evidence, maps JD fit, enforces truth boundaries, writes polished and targeted resumes, generates interviewer-style grilling questions, answer cards, evidence-upgrade plans, and optional open-source project recommendations without fabricating experience.
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Manage OpenViking collections (OV libraries) from the command line with `ov-cp` — list / create / get / update / usage / get the data-plane API key / delete, plus managing the users and account data spaces of an enterprise-tier library (list / create / register / update / delete), account-scoped users / API keys / usage, and configuring how a library is billed (AgentPlan AFP deduction vs Volcano pay-as-you-go, `--pay-type` / `--seat-id`). Use when the user wants to provision or inspect an OpenViking library, fetch a library's data-plane API key, do the create→get-key cold-start, manage a library's users or accounts / 数据空间, set or switch a library's billing, or otherwise drive the OpenViking control plane (topapi). Authenticates with an Ark AgentPlan ApiKey.
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Use when generating or refactoring Spring Boot 4 JPA entities, repositories, queries, projections, persistence tests, entity relationships, embeddables, IDs, or Hibernate mappings. Covers Jakarta Persistence 3.2 imports, Hibernate 7 entity modeling, new-state detection, N+1 prevention, projections, keyset pagination, batch writes, and common agent mistakes.
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Use when implementing caching, session storage, rate limiting, or any Redis integration. Covers cache-aside pattern, key naming, TTL strategy, and serialization config.
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Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI 2.0 ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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Use when generating JPA entities, repositories, queries, or anything touching the persistence layer. Covers entity conventions, N+1 prevention, projections, and query patterns.
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Use when integrating LLMs, chat clients, embeddings, RAG pipelines, or AI agents into Spring Boot. Covers Spring AI ChatClient, prompt templates, embeddings, vector stores, and structured output. Use when user mentions Spring AI, LLM, ChatGPT, Claude, RAG, embeddings.
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Use when creating database migrations, schema changes, seed data, or any SQL that modifies database structure. Covers Flyway naming conventions, versioning, and safe migration patterns.
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Position an AI product when everyone claims AI and skeptics call it "just a wrapper." Find the real wedge (data, workflow, trust, domain), make reliability the differentiator, answer "won't the big labs just build this," and stop leading with "AI-powered." Use when your AI or dev tool blends into a sea of similar demos, buyers doubt the accuracy, or you can't say why you win when the model is a commodity.
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Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.
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Author, package, validate, or run mathematical evaluations as Jacobian Harbor datasets.
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OI-3 spike harness — heavy consumer skill, INLINE arm. Builds an incident post-mortem and sources the readability standard mid-workflow via an inline (non-forked) guidance skill. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.
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OI-3 spike harness — heavy consumer skill, FORKED arm. Builds an incident post-mortem and sources the readability standard mid-workflow via a forked (context fork) guidance skill. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.
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OI-3 spike harness — heavy consumer skill, BASELINE arm. Builds an incident post-mortem and sources the readability standard mid-workflow by reading a file directly, with NO Skill-tool call. Use only when explicitly invoked by the spike harness with a TRIAL_ID and data path.
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