626 件 ・ 関連度順
概要と使いどころ
Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).
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Use when a tutorial concept DAG exists and the run needs an ordered teaching plan with objectives and outputs.
日本語の概要は準備中です。原文の説明を表示しています。
Turn any source that describes how a kind of task gets done (a SKILL.md, a chat log, a runbook, plain prose) into a runnable Morph trace. Lift it into a DAG, write a contract per step, place inputs, and drive the full run lifecycle to verify it. Use whenever someone wants to make a trace from a source.
日本語の概要は準備中です。原文の説明を表示しています。
Automate construction data workflows. Build ETL pipelines and DAG workflows for recurring tasks.
日本語の概要は準備中です。原文の説明を表示しています。
Route Apache Airflow repo tasks across Dag authoring, operations, providers, deployment, and contribution workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.
日本語の概要は準備中です。原文の説明を表示しています。
Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
日本語の概要は準備中です。原文の説明を表示しています。
Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.
日本語の概要は準備中です。原文の説明を表示しています。
The Auto 档 scenario — a soft-plan orchestrator for a complete fundamental equity research thesis: understand → financials → valuation → synthesize. The body is a soft plan (stages), not a rigid DAG; correctness is enforced by each referenced skill's own requires: preconditions, never by this plan's rigidity (Q2).
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Epidemiologist. Use when a task calls for Epidemiologist judgment. Reasons from person-time, transmission dynamics, and population case definitions through epidemic curves, DAGs, renewal and SEIR models (EpiEstim, deSolve), SaTScan clustering, and STROBE/ORION/GRADE standards while treating confounding, collider stratification from test-positive conditioning, reporting-delay and testing-intensity artifacts, and superspreading overdispersion as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Data Scientist. Use when a task calls for Data Scientist judgment. Reasons from CRISP-DM business estimands, leakage-safe sklearn Pipelines and nested CV, SQL/warehouse semantic metrics, A/B power and SRM/AA guardrails, causal DAG covariate discipline, and Model Cards/Datasheets while treating train-test leakage, Simpson's paradox, peeking, and PSI>0.25 drift as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Computational Social Scientist. Use when a task calls for Computational Social Scientist judgment. Reasons from social mechanisms, measurement validity, and sampling frames through DAGs, fixed-effects and IV/DiD/RDD designs, ERGM/SAOM network models, and human-audited text classifiers while treating unobserved homophily, network interference and SUTVA violations, platform-driven selection, bot contamination, and digital-skew unrepresentativeness as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Causal Inference Scientist. Use when a task calls for Causal Inference Scientist judgment. Reasons from structural causal models, potential outcomes, and identification logic through DAGs and do-calculus, doubly-robust estimators (AIPW/TMLE, IPTW, g-formula), and design-based methods (IV, RD, Callaway-Sant'Anna DiD, synthetic control) while treating colliders and M-bias, positivity/overlap failure, and unmeasured confounding (Rosenbaum bounds, E-values) as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Biostatistician. Use when a task calls for Biostatistician judgment. Reasons from estimands, SAPs, and error budgets; aligns ICH E9(R1), CONSORT/STROBE, multiplicity, MMRM, Cox survival, causal DAGs, and GWAS FDR while treating immortal time, ICEs, and batch confounding as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.
日本語の概要は準備中です。原文の説明を表示しています。
Think and work like an expert Bioinformatics Engineer. Use when a task calls for Bioinformatics Engineer judgment. Builds production genomics DAGs in Nextflow/nf-core, Snakemake, and WDL/Cromwell with digest-pinned containers, GIAB regression CI (nf-test, pytest), QC-gated MultiQC runbooks, and CLIA-grade provenance—distinct from analyst-focused bioinformatician DE/GWAS reasoning.
日本語の概要は準備中です。原文の説明を表示しています。
Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Causal inference specialist for causal discovery, counterfactual reasoning, and effect estimationUse when "causal inference, causal discovery, counterfactual, intervention effect, confounder, structural causal model, SCM, dowhy, causal graph, causal, dowhy, scm, dag, counterfactual, intervention, causalnex, confounding, ml-memory" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes large tasks for independent subtasks that can be safely parallelized. Produces a DAG-based dispatch plan with dependency ordering and maximum parallelism. Use when a task has 5+ subtasks touching independent files, or the user asks to speed up or parallelize the work.
日本語の概要は準備中です。原文の説明を表示しています。
Azure AI Hosted Agents (Python) workflow skill. Use this skill when the user needs Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
日本語の概要は準備中です。原文の説明を表示しています。
Runs a multi-step procedure as a Python DAG, so ordering, branching and retries are enforced by the runner rather than described in prose a model can generate past. Use for "run these steps in order and retry the flaky one until the check passes", "build a pipeline that fetches, validates, then skips the upload when nothing changed", "make sure these steps cannot be skipped", "resume from where it broke instead of redoing the expensive early stages", "run these independent calls at once and merge the results", or any procedure of 3+ steps with branches, input contracts, or side effects that must not block the critical path. Primitives are depends_on, when=, validate=, retry_until=, detached= and journal_path=. Not for a single sequential call, for steps needing reasoning between them that no predicate captures, or for async and distributed work. To audit whether one verification check can actually go red, use gating. To fan work out across many subagents, use a dynamic workflow.
日本語の概要は準備中です。原文の説明を表示しています。
Applies the mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.
日本語の概要は準備中です。原文の説明を表示しています。
Suede Labs copy-only orchestration DAG. Use for one high-stakes piece strangers will read that has to be true: a landing page, launch post, blog post, email, X thread, docs page, README, ad, or store listing, researched, fact-audited, adversarially reviewed, and gated for publish readiness in one pass. The audit targets what the agents invent, never what the requester supplied: their own statements are given, and no phase may verify, hedge, or gate on them. Reads the live surface; never publishes. NOT FOR: multi-surface campaign writing (use johnny-suede-write); changing code (use suede-graph-flo-xr); one surface in one pass with no research (use suede-copy); stripping AI patterns from existing text (use suede-deslop); bulk generation of many independent pieces (private Suede Labs companion, not in this pack: suede-codex-fleet).
日本語の概要は準備中です。原文の説明を表示しています。
Suede AI umbrella router for the public Suede pack: selects the right specialist across copy, design, code review and grading, SEO and visibility, launch packaging, MCP QA, iOS and Android shipping, growth, and creator rights work. Use when a request crosses two or more Suede lanes, when the user wants the full public skill pack loaded from one installable path, when they ask which Suede skill fits a task, or when only this umbrella skill is installed. NOT FOR: a request that names one lane and can go straight to its specialist; a multi-file repo change that should be built and reviewed as one DAG (use suede-graph-flo-xr).
日本語の概要は準備中です。原文の説明を表示しています。