638건 · 관련도순
PiFlow 技能生成器。当用户完成了一次完整并成功的数据处理任务时调用，根据当前 skills/generated 的本地约定和 references/piflow_skill_template.md 通用模板创建、更新或校验 PiFlow-compatible skill，包括 UTF-8 编码的 SKILL.md、DAG 可读的 input_params/output_params、version/tag 元数据、skill.json、scripts/references/assets 资源目录，以及本地校验脚本。仅在用户明确要求生成或保存 skill，或某次数据处理任务已经完成并需要把已验证成功的操作流程沉淀为 skill 时使用；默认不要因为“可能需要 skill”或“当前能力不足”而优先触发此 skill。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
PiFlow 技能生成器。当用户完成了一次完整并成功的数据处理任务时调用，根据当前 skills/generated 的本地约定和 references/piflow_skill_template.md 通用模板创建、更新或校验 PiFlow-compatible skill，包括 UTF-8 编码的 SKILL.md、DAG 可读的 input_params/output_params、version/tag 元数据、skill.json、scripts/references/assets 资源目录，以及本地校验脚本。仅在用户明确要求生成或保存 skill，或某次数据处理任务已经完成并需要把已验证成功的操作流程沉淀为 skill 时使用；默认不要因为“可能需要 skill”或“当前能力不足”而优先触发此 skill。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
用于 Workflow Planning 阶段的占位算子。当前 Skill 库不存在满足用户需求的业务 Skill 时，使用本算子保持 DAG 数据流完整，等待后续生成真实 Skill 后替换。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Dataspace 文件输出终止算子。用于接收上游算子的单个文件输出，按指定相对目录上传到 Dataspace 空间目录中，实际文件名自动沿用输入文件名。该算子必须作为 DAG 的终止节点使用。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Dataspace 文件输入源算子。用于根据 Dataspace 数据源实例 ID 和空间内文件路径下载单个文件，并将该文件作为工作流输入提供给下游算子。该算子没有上游输入，必须作为 DAG 的起始节点使用。
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Multi-agent coordination, critical path method, dependency DAG, and agent allocation optimization
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OpenAI Codex CLI + Claude Code (Hizir) birlikte kullanim rehberi. Is dagitim pattern'leri, GitHub Actions workflow ornekleri, review dongusu ve iki AI yazilim asistaninin guclu yanlarini birlestiren orchestration stratejileri.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when a tutorial concept DAG exists and the run needs an ordered teaching plan with objectives and outputs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Automate construction data workflows. Build ETL pipelines and DAG workflows for recurring tasks.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Route Apache Airflow repo tasks across Dag authoring, operations, providers, deployment, and contribution workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build container-based Foundry Agents with Azure AI Projects SDK (ImageBasedHostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Orchestrate AI/ML pipelines for data ingestion, model training, batch inference, and RAG indexing using Prefect, Airflow, or Dagster.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.