「workflow」の検索結果

Produces a deep, honest peer-review of how someone uses Claude Code by statistically analyzing their local session data (~/.claude/usage-data/ and ~/.claude/projects/). Trigger whenever the user asks to review, analyze, audit, or critique their own Claude Code usage, workflow, habits, or skill level — including phrases like "analyze my cc usage", "review my cc sessions", "peer review my cc workflow", "deeper than /insights", or any ask for an honest audit of their AI workflow. Also covers portfolio/hiring-manager framings (e.g. "portfolio for Anthropic/OpenAI/xAI"), but ALWAYS ask the user in Step 0 which version to build (self / hr / both) before running — never silently produce an HR report, because that version goes to outsiders and needs explicit privacy setup. The HTML report is laid out ledger-first for SELF (opening band, output/team/leak/trend ledgers, then peer review, 9-dim scoring, try-this-week, case study, claim-indexed evidence). The HR variant is a recruiter version v1: identity card, earned badges (threshold-based, earned-only), allowlist-filtered output ledger, one case study, and a methodology & scope disclosure — no scores, no peer-review memo, no charts.

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。

Reconstruct workflow behind a past coding-agent artifact using local ax sessions/commits/skills/tool traces. Use when asked how X was built.

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。

Interactively build a Narrative identity graph workflow from one or more first-party datasets and (optionally) third-party data sources. Confirms each input dataset is mapped to the Rosetta Stone graph edge attribute (mapping it via /generate-rosetta-stone-mappings if not), then composes and submits a workflow that unions every edge source and labels connected components. Use when: "build an identity graph", "generate an identity graph", "create an identity graph", "stitch these datasets into a graph", "make a graph workflow", "label connected components on these datasets", "I want a person graph / household graph / device graph". (narrative-identity)

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。

Apply a set of Rosetta Stone attribute mappings to a Narrative dataset by wrapping them in a one-shot workflow that calls the `CreateRosettaStoneMappingsIfNotExist` task. Consumes the structured output of `/generate-rosetta-stone-mappings`, normalizes the generator's snake_case to the workflow task's camelCase, re-validates every expression against the dataset's current schema, gates on user approval, submits via `narrative_workflows_create`, polls the triggered run, and reports per-mapping created / conflict / failed. Use when: "apply these mappings to dataset N", "create the Rosetta Stone mappings I just generated", "push the mappings I saved earlier to <dataset>", "productionize this mapping list", "submit the suggested_mappings array". (narrative-common)

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。

Uses the gh-axi CLI for GitHub shell operations: issue, pull request, workflow run, release, repo, search, and API tasks. Prefer this over regular `gh` for GitHub reads and simple mutations when an agent needs compact, structured, suggestion-rich output. Triggers on: gh, GitHub CLI, github issue, github pr, pull request, workflow run, github release, gh api, repo inspection, list PRs, view issue, check workflow runs, inspect repo, GitHub shell operations.

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。

How game development works as a process — roles, subtasks, workflows and role descriptions, especially (but not only) for Roblox. Use this skill when it's about the ORGANIZATION of game dev rather than concrete code: Which roles exist (Creative Director, Engineer, Artist, Polish/Audio, Business, QA-Tester, Game Critic)? Who does which subtask? What does a development chain (concept → backend → frontend → polish → test) look like? How do you write a Game Design Document / KONZEPT.md? How do several (AI) agents divide up a game? Also trigger on "plan a new game", "create Game Design Document", "which roles do I need for my game", "development workflow for a game", "who tests the game", "structure a game idea", "Roblox genre/monetization".

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。

End-to-end media pipeline automation — from AI generation through optimization to social publishing. Covers workflow design, WordPress integration, Redis job queuing, batch processing, multi-site pipelines, and orchestration. MCP bridge tools are the primary interface for individual operations; the Design Stack media worker handles batch and advanced processing. Use when you need to automate image generation→optimization→publishing chains, set up cross-site media sync, design batch processing pipelines, or orchestrate multi-step media workflows triggered by post publish or cron.

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。

Manage service components and approval workflows in the NV oOS Pro Toolkit. Covers service component registration, dependency tracking, approval request lifecycle, and workflow approval gates. Use when registering service components, tracking service dependencies, managing approval requests, or configuring workflow approval nodes.

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。

When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.

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。

Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.

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。

Create production-ready GitHub Actions workflows for automated testing, building, and deploying applications. Use when setting up CI/CD with GitHub Actions, automating development workflows, or creating reusable workflow templates.

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。

You are a workflow automation expert specializing in creating efficient CI/CD pipelines, GitHub Actions workflows, and automated development processes. Design automation that reduces manual work, i...

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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

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。

Expert en ComfyUI workflows avancé (install, custom nodes, model management, workflow design)

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。

Expert en workflow Claude Code (commands, skills, subagents, CLAUDE.md, best practices)

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。

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper 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。

シングルエージェントで計画書/タスクを順次実装する開発フロー。「シングルフロー」「shiiman-workflow:single」「1 人で実装」「順次実装」「シングルで実装して」「一人で開発」などで起動。Issue/PR まで作るかは発話・引数から判断し、曖昧なら確認する。

MCP マルチエージェント（Owner/Admin/Worker）で並列実装する開発フロー。「マルチフロー」「workflow-multi」「マルチエージェントで実装」「並列で実装」「複数人で実装」「並列開発」などで起動。Issue/PR まで作るかは発話・引数から判断し、曖昧なら確認する。

Agent Team（tmux + TeamCreate）で並列実装する開発フロー。「エージェントチームフロー」「workflow-agent-team」「Agent Team で実装」「チームで実装」「チーム並列開発」などで起動。Issue/PR まで作るかは発話・引数から判断し、曖昧なら確認する。

Use when deciding WHERE to do work on Syntropic137 itself - "run this through Syntropic", "dispatch a workflow", "should this go in a workspace or local", "kick off a run on the Mini", "dogfood this", "build Syntropic inside Syntropic", "why did the agent's push fail", "can an agent run our QA", "the execution says completed but nothing shipped". Covers what a workspace agent can and cannot do, why `.github/workflows` pushes must come from the local machine, how to write a task prompt that does not repeat a known failure, and how to verify a run actually delivered. Do NOT use for PR/release mechanics (see `devops`), for using the `syn` CLI as an end user (see the syntropic137 plugin skills), or for authoring workflow YAML.

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。

Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility

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 GitHub workflows with AI assistance. Includes PR reviews, issue triage, CI/CD integration, and Git operations. Use when automating GitHub workflows, setting up PR review automation, creating GitHub Actions, or triaging issues.

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。

インタラクティブワークフロー選択 - 実行したい処理を段階的に選択して実行。/workflow で呼び出し。

End-to-end 16S/ITS amplicon workflow from demultiplexed FASTQ to a consensus differential-abundance result, orchestrating cutadapt primer removal, per-run DADA2 ASV inference (learnErrors/mergeSequenceTables/removeBimeraDenovo), region-matched taxonomy assignment, a SEPP/Greengenes2 tree, alpha/beta diversity at a declared sampling depth (phyloseq/vegan, adonis2 paired with betadisper), compositional DA as a consensus of >=2 tools (ALDEx2/ANCOM-BC2) on unrarefied counts, and optional PICRUSt2 functional prediction gated on NSTI. Covers the stage-ordering decisions (primers before truncation, per-run error model, rarefy for diversity not DA, predicted potential not activity) and defers each per-step choice to the six microbiome skills. Use when staging an amplicon study end to end or chaining ASV inference, taxonomy, diversity, and differential abundance. For shotgun reads see workflows/metagenomics-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。