Machine Learning Skills

Expert-level Continuing Education Coordinator with deep knowledge of adult learning theory (Andragogy), professional development standards, workforce training regulations, and CE accreditation requirements

Expert-level Corporate Internal Trainer with deep knowledge of instructional design, employee development frameworks, training delivery methodologies, and organizational learning systems

A professional corporate trainer specializing in employee training program design, skill development workshops, and organizational learning. Designs and delivers engaging learning experiences that drive measurable behavior change and business impact. Use when: education, teaching, corporate, training, learning-design.

Expert-level E-commerce Livestream Trainer with deep knowledge of live selling techniques, platform operations (TikTok Shop, Taobao Live, JD Live), audience engagement, and sales conversion

Expert-level IT Training Instructor with deep knowledge of coding bootcamps, software development curricula, programming pedagogy, and technical skill development. Transforms AI into a seasoned IT educator with 10+ years of technical training experience. Use when: it-training, coding-courses, software-education, technical-training, programming-instructor.

Expert-level Language Test Trainer with deep knowledge of IELTS, TOEFL, GRE, PTE academic testing formats, scoring rubrics, and test-taking strategies. Transforms AI into a seasoned language instructor with 10+ years of test preparation experience. Use when: ielts, toefl, language-test, test-preparation, esl.

Expert Maternity Nurse Trainer with 15+ years training new mothers and healthcare professionals in newborn care, postpartum recovery, and lactation consulting. Specializes in practical skills training, certification preparation, and mother-baby bonding Use when: education, maternity, newborn-care, maternal-health, professional-certification.

Expert-level Language Trainer with deep knowledge of second language acquisition (SLA), TEFL/TESOL methodology, pronunciation training, fluency development, and communicative language teaching

Expert Outward Bound Trainer with 15+ years of experience in adventure-based learning, leadership development, and team building

Expert-level Sports Coach with deep knowledge of athletic training methodology, sport-specific skill development, periodization programming, injury prevention, sports psychology, and team dynamics

Expert Training Class Advisor with 10+ years managing K12 and adult training classes. Specializes in student progress tracking, parent communication, classroom management, and student psychological development

Expert Training Manager specializing in corporate learning, talent development, training program design, and organizational capability building. Expert in needs assessment, training delivery, learning technologies, and measuring training effectiveness. Use when: training-management, corporate-learning, talent-development, training-design, organizational-development, learning-and-development.

Expert Training School Principal with deep knowledge of educational operations, business development, instructor management, curriculum oversight, and regulatory compliance for private training institutions

Expert Tutoring Center Manager specializing in educational business operations, tutor recruitment and training, student enrollment, and quality assurance. Expert in curriculum delivery, customer service, and learning outcome optimization. Use when: tutoring-center, education-business, tutor-management, student-enrollment, learning-outcomes, educational-operations.

Expert Vocational Trainer with deep knowledge of competency-based education, industry certifications, workforce development, and career coaching

Break LLM name defaults with external entropy. Use when character names cluster around statistical medians (Chen, Patel, Maya, Marcus), when cast has collision risks, or when fantasy cultures need phonologically consistent naming.

Guide competency framework development and operation. Use when building training that produces capability, when existing training doesn't produce competence, when structuring knowledge for multiple audiences, or when setting up feedback loops to surface gaps.

通过 reasoning_effort、Magic String、组合推理题和离线日期题快速检测当前 API 是否为真实 Claude 模型，并在需要时升级到身份、工具、元数据与嵌套层级的深度审查。用于怀疑模型真假、来源异常、被第三方包装，或需要输出模型真实性检测报告时。

【已废弃并合并】此技能已迁移至新版酒店查询、预订技能rollinggo-hotel-booking。如果是下载过程，请直接选择 rollinggo-hotel-booking。触发此技能后大模型会引导用户迁移到新版。

>- Evaluate urban designs against comprehensive criteria drawn from all major global standards, certification systems, and theoretical frameworks. Generates detailed scorecards and improvement recommendations. Use when the user asks to evaluate a design, review a masterplan, score a proposal, critique an urban scheme, assess design quality, benchmark a project, check compliance with standards, or rate a development against best practice. Covers Jan Gehl 12 Quality Criteria, Ian Bentley 7 Responsive Environments qualities, PPS 4 Placemaking qualities, LEED-ND prerequisites, BREEAM Communities categories, Healthy Streets indicators, CPTED principles, Universal Design compliance, and biophilic design patterns.

>- Estimate construction costs, infrastructure costs, soft costs, and total development costs for urban design projects. Covers building construction cost per m2 by typology, infrastructure cost per linear meter and per dwelling, site preparation, landscaping, professional fees, and financial feasibility analysis. Use when the user asks about project cost, construction cost, infrastructure budget, development feasibility, cost per unit, cost per m2, capital expenditure, opex, cost benchmarking, value engineering, phasing costs, or any question about how much an urban design project or its components will cost. Also use for preliminary budget estimation, order-of- magnitude costing, cost comparison between design alternatives, or cost optimization of masterplans.

Chainlink Confidential AI Attester: submit private documents to an LLM inside an AWS Nitro Enclave and get back a cryptographically attested result — raw documents never leave the TEE. Use for these hackathon scenarios: (1) undercollateralized DeFi lending — upload a bank statement, get an attested approved/denied JSON decision without exposing financials on-chain; (2) accredited investor verification — check SEC Rule 501 qualification from brokerage statements privately; (3) KYC/AML screening — analyse ID docs and transaction history inside a TEE, return a pass/fail with flags; (4) proof of reserves — verify custodian balance reports against claimed reserves; (5) any use case where an AI must read sensitive user documents and the result needs a cryptographic proof of what model ran on what data. Trigger on: private inference, attested AI, TEE inference, confidential AI, or undercollateralized lending / KYC / accredited investor mentioned alongside document analysis.

Use for BetterTogether, prompt plus weight optimization, fine-tuning sequences, and strategy chains like p -> w -> p.

Use for DSPy retrieval with dspy.Embedder, dspy.Embeddings, FAISS indexes, semantic search, and local or hosted embedding models.

Use for BootstrapFinetune, fine-tuning DSPy models, teacher-student distillation, weight optimization, and lower-cost deployment.

Use for RAG pipelines, retrieval augmented generation, ColBERTv2, context retrieval, multi-hop RAG, and grounded DSPy answers.

Configure ASE MACE calculator adapter settings for ASE workflows. Use when ASE workflow tasks require MACE backend setup including model path/version, device/precision, stress availability, and inference controls.

> A command-line utility for converting and manipulating over 50 atomic simulation data formats, including outputs from DFT and MD software (VASP, LAMMPS, Gaussian, QE, CP2K, ABACUS, etc.). USE WHEN you need to convert structural or trajectory files between different computational chemistry formats, or when parsing raw simulation outputs into structured training datasets (e.g., deepmd/raw, deepmd/npy, deepmd/hdf5) for DeePMD-kit.

Prepare, explain, validate, and run DP-GEN simplify workflows for reducing repeated or redundant DeepMD datasets. Use when the user wants to generate or modify `param.json` and `machine.json`, run `dpgen simplify param.json machine.json`, organize repeated simplify experiments, or inspect simplify outputs.

Run Python inference with DeePMD-kit models using the DeepPot API. Use when the user wants to load a trained/frozen DeePMD model (.pth or .pb) or a built-in pretrained model (e.g., DPA-3.2-5M) in Python, predict energy/force/virial for atomic configurations, evaluate descriptors, or calculate model deviation between multiple models. Also covers using `dp test` CLI for batch evaluation against labeled data.

Train DeePMD-kit models with progressive disclosure. Use when the user wants to train a DeePMD-kit potential, prepare an input.json, choose between model families such as se_e2_a/DeepPot-SE and DPA3, run `dp train`, monitor learning curves, freeze checkpoints, or test trained models. Start with model selection and read only the selected model reference under `models/` when model-specific configuration is needed.

Fine-tune a DPA3 model in DeePMD-kit using the PyTorch backend. Use when the user wants to adapt a pre-trained DPA3 model to a new downstream dataset. Supports fine-tuning from a self-trained DPA3 model (.pt checkpoint), from a multi-task pre-trained model, or from a built-in pretrained model downloaded via `dp pretrained download` (e.g., DPA-3.1-3M, DPA-3.2-5M, DPA-3.3-1M). Covers single-task and multi-task fine-tuning workflows.

> A standardized CLI wrapper for RDKit 3D/2D conformer generation that samples multiple conformers per molecule (ETKDGv3, default 10), optimizes each with a force field (MMFF94s/UFF), keeps the lowest-energy conformer, automatically falls back to 2D layout on total embedding failure with a printed warning, and writes results to SDF or XYZ format. USE WHEN you need to generate 3D (or 2D fallback) molecular geometries from SMILES datasets (.csv/.smi) for downstream tasks such as docking, visualization, or 3D-descriptor computation.

> A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in RDKit SMILES validation. USE WHEN you need to generate molecular embeddings, train machine learning models for chemical properties, or run predictions on SMILES datasets (.csv/.smi) using the Uni-Mol framework.

| Trigger this skill when the user works on AI Product Manager — LLM application / generative AI / agent product 的产品经理实战 problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「AI 产品经理」「AI PM」「LLM 产品」「Generative AI product」「RAG 产品」

| 触发词：「K12 体育培训」「青少年体育培训」「少儿体适能」「中考体育」「体育中考」

| Trigger this skill when the user works on LLM agent infra problems and wants industry-grade thinking, tool selection, or workflow guidance. 触发词：「agent framework」「LLM agent」「agent infra」「multi-agent orchestration」「agent runtime」

| 触发词：「personal trainer」「personal training」「健身私教」「私教」「私人教练」

Guide for Vercel AI SDK v5 implementation patterns including generateText, streamText, useChat hook, tool calling, embeddings, and MCP integration. Use when implementing AI chat interfaces, streaming responses, tool/function calling, text embeddings, or working with convertToModelMessages and toUIMessageStreamResponse. Activates for AI SDK integration, useChat hook usage, message streaming, or tool calling tasks.

Use this skill to synthesize closed-form, automatically verifiable benchmark Q/A by exploring a tool environment, building a reusable exploration graph, and mining multiple hard questions from that graph. Use for: building a benchmark, writing eval items, generating evaluation data, closed-form QA, verifiable-answer datasets, synthesising eval data. Applies to any domain with callable tools. Do not use for open-ended writing, subjective scoring, or pure labeling.

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Apply Vipassana meditation principles to LLM processing — equanimous scanning, non-reactive observation, impermanence awareness (anicca), and breaking the sankhara chain of conditioned reactions. Trigger on "meditate on", "observe without reacting", "see clearly", "practice vipassana", "scan equanimously", "what's really going on here", "non-reactive analysis", "observe without judgment". Also trigger when the user wants bare attention rather than jumping to conclusions, wants to dissolve fixation loops, or asks Claude to apply contemplative processing. Apply proactively when conversations show reactive thinking, craving for particular outcomes, or aversion to uncomfortable truths. Synergizes with cognitive-variability, embodied-navigation, and shifting-perspective skills.

Debug AI agents and LLM applications via Langfuse MCP. Use when investigating traces, exceptions, slow generations, sessions, prompt versions, datasets, or evaluation sets. Triggers on "langfuse", "traces", "debug AI", "find exceptions", "what went wrong", "why is it slow", "datasets", "evaluation sets".

Use this skill when the user wants to build AI applications with Weaviate. It contains a high-level index of architectural patterns, 'one-shot' blueprints, and best practices for common use cases. Currently, it includes references for building a Query Agent Chatbot, Data Explorer, Multimodal PDF RAG (Document Search), Basic RAG, Advanced RAG, Basic Agent, Agentic RAG, and optional guidance on how to build a frontend for each of them.

Search, query, and manage Weaviate vector database collections. Use for semantic search, hybrid search, keyword search, natural language queries with AI-generated answers, collection management, data exploration, filtered fetching, data imports from PDF/CSV/JSON/JSONL files, create example data and collection creation.

Use Neo4j GenAI Plugin ai.text.* functions and procedures for in-Cypher embedding generation, text completion, structured output, chat, tokenization, and batch ingestion. Covers ai.text.embed(), ai.text.embedBatch(), ai.text.completion(), ai.text.structuredCompletion(), ai.text.aggregateCompletion(), ai.text.chat(), ai.text.tokenCount(), ai.text.chunkByTokenLimit(), and provider configuration for OpenAI, Azure OpenAI, VertexAI, and Amazon Bedrock. Requires CYPHER 25. Replaces deprecated genai.vector.encode(). Use when writing pure-Cypher GraphRAG, embedding nodes in-graph, generating structured maps from prompts, or calling LLMs inside Cypher queries. Does NOT handle neo4j-graphrag Python library pipelines — use neo4j-graphrag-skill. Does NOT handle vector index creation/search — use neo4j-vector-index-skill.

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, semantic or structural nearest-neighbor lookup, or hybrid search (vector + fulltext, multiple vector sources, or graph-derived scores). Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext-only/keyword-only search — use neo4j-cypher-skill. Does NOT compute GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.

Fetch, organize, and analyze LangSmith traces for debugging and evaluation. Use when you need to: query traces/runs by project, metadata, status, or time window; download traces to JSON; organize outcomes into passed/failed/error buckets; analyze token/message/tool-call patterns; compare passed vs failed behavior; or investigate benchmark and production failures.