3,522 件 ・ 関連度順
概要と使いどころ
コードファースト資産（エージェント定義・Code Apps の構成・IaC など）を秘匿化・汎用化したテンプレートとして Git 管理し、pre-commit ゲート → 自律レビューゲート → 承認・デプロイ → リリース記録までを CI/CD で回すための共通基盤。GitHub Actions / Azure Pipelines / その他 Git ホスティングと、GitHub Secrets / Azure DevOps 変数グループ / Azure Key Vault の各シークレットストアに対応し、プロダクト固有のデプロイ手順は各スキルへ委譲する。
Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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。
Instruction dispatch pipeline for routing work items to available agents. Implements a 7-stage fetch-allocate-prepare-hook-store-launch-confirm pipeline with batch convoy mode, formula expansion, idempotent dispatch, and crash recovery.
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。
Package conversation research into a GSD-ready mission package. Produces a three-stage Vision → Research → Mission pipeline as a LaTeX PDF following GSD/NASA SE methodology, designed to be handed to gsd-skill-creator for execution. Use this skill when the user has been discussing, researching, or brainstorming a topic and then asks to turn it into a research mission, research pack, mission package, or says 'package this as a mission', 'make this a research pack', 'turn this into a mission for skill-creator', 'create a research mission from this', or 'use the research mission skill'. The skill harvests findings, sources, and structure from the current conversation and any prior research, then produces the complete pipeline document. Also trigger if the user asks to 'create a research mission on [topic]' cold — in that case, conduct web research first, then package.
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。
Pipeline retirement protocol for completed work items. Implements a 7-stage validate-commit-push-submit-notify-cleanup-terminate pipeline with irreversibility after push. Done means gone.
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。
Comprehensive guide for building robust, secure, and efficient CI/CD pipelines using GitHub Actions. Use when the task involves `creating or editing GitHub Actions workflows`, `.github/workflows/*.yml`, `CI/CD pipelines`, `GitHub Actions best practices`, or `workflow optimization`.
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。
Category-theoretic pipes with GF(3) flow balance checking. Validates data transformation pipelines using triadic trit algebra.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD pipelines, including env var intermediary patterns, direct expression injection, dangerous sandbox configurations, and wildcard user allowlists. Use when reviewing workflow files that invoke AI coding agents, auditing CI/CD pipeline security for prompt injection risks, or evaluating agentic action configurations.
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。
Configures CI/CD pipelines for automated Python package publishing to PyPI or GitLab Package Registry. Use when creating GitHub Actions or GitLab CI release workflows, setting up trusted publishing or API token-based PyPI authentication, configuring version management with git tags and hatch-vcs, writing pyproject.toml publishing metadata, testing packages against TestPyPI, or documenting the release process for a Python project.
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。
Use when designing or validating GitLab CI/CD and automatic release lifecycles, inspecting pipeline and job state read-only, composing non-interactive merge-request commands, or setting up protected refs and release credentials. Also covers reusable components and inputs, GitLab Functions, gitlab-ci-local, glab command composition, and GitLab Flavored Markdown. For pipeline execution or retry requests, provide inspection and command drafting while leaving execution to the operator.
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。
Use this skill when answering business analytics, RevOps, GTM metric, pipeline, revenue, funnel, customer, or warehouse questions with Deepline. Triggers on phrases like 'query Snowflake', 'analyze pipeline', 'total ACV', 'break down by quarter', 'use the semantic layer', 'run a semantic query', or any use of snowflake_get_semantic_layer / snowflake_run_semantic_query. Skip prospecting, enrichment, contact finding, outbound, or personalization workflows; use deepline-gtm for those.
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 ingestion-to-serving pipelines on MotherDuck, including stage boundaries, transformations, and publication.
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。
Medical/scientific paper writing workflow skill. Manages the full pipeline from literature search to submission-ready manuscript. Creates and manages a project directory with IMRAD-format section files, literature matrix, reference management, and quality checklists. Supports both English and Japanese papers. Triggers: 'write paper', 'paper-write', 'start manuscript', '論文を書く', '論文執筆', '論文プロジェクト', 'manuscript', 'research paper', '原稿作成'.
Use when adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.
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。
Use when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable directory. Covers the workflow from a single `block.py` file to a published custom-block repo that consumers can load via `ModularPipeline.from_pretrained(<repo>, trust_remote_code=True)`.
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。
Use when the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Python scripts for generation 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。
Debug CI failures in unfamiliar repo pipelines. Reads CI configs, fetches failure logs, classifies the failure type, and guides the user through diagnosing and fixing the issue. Use when CI fails on your PR in an OSS repo, when you don't understand a CI error, or when debugging GitHub Actions/CircleCI/other CI systems. Not for setting up CI from scratch — this is for debugging existing pipelines.
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。
皮皮虾（Pipixia）内容数据查询 skill，通过 MaxHub API 查询作品详情、统计、评论、首页流、搜索、热搜、话题和用户相关内容。适合内容研究、互动分析、热点追踪和素材筛选。所有请求发送到 https://www.aconfig.cn。
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。
Weekly feature launch announcement pipeline. Reads recent MRs, generates Slack announcement (with approval gate), then autonomously produces a blog post, LinkedIn posts (brand + personal), and a LinkedIn carousel. Use when: weekly roundup, feature launch announcement, what's new, launch pipeline, weekly announcement.
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。
Use when auditing CRM data quality, finding duplicate records, identifying stale leads, cleaning pipeline data, or improving data hygiene. Triggers: 'clean my CRM', 'audit CRM data', 'find duplicates', 'CRM hygiene', 'data quality check', 'stale leads', 'pipeline cleanup'.
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。
Use when generating weekly GTM performance reports, summarizing pipeline activity, reviewing outreach metrics, or creating executive updates. Triggers: 'weekly report', 'GTM report', 'pipeline review', 'weekly update', 'outreach summary', 'Friday report'.
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。
Use when scoring leads, qualifying prospects, prioritizing pipeline, deciding which accounts to pursue, or building a lead scoring model. Triggers: 'score this lead', 'qualify this prospect', 'is this a good lead', 'lead scoring', 'prioritize my pipeline', 'qualification framework'.
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。
Use when the user wants to smoke-test the evolve pipeline, test tools, or verify the plugin works end-to-end. Also use when the user says 'dry run', 'smoke test', or 'test pipeline'.
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。