スキル一覧に戻る

utility-pm-workflow-orchestrator

product-on-purpose
更新日 7 days ago
283
39
283
GitHubで表示
メタaitestingautomation

について

このスキルは、単一の入力に対して複数のpm-skillsを順次実行し、各ステップ間で承認を一時停止し、失敗時には停止します。Claude Code上では、ネイティブ実行のためにpm-skillsプラグインを使用し、他のクライアントでは機能確認後にインラインで実行されます。現在は実験的機能であり、明示的なドライラン呼び出しが必要です。

クイックインストール

Claude Code

推奨
メイン
npx skills add product-on-purpose/pm-skills -a claude-code
プラグインコマンド代替
/plugin add https://github.com/product-on-purpose/pm-skills
Git クローン代替
git clone https://github.com/product-on-purpose/pm-skills.git ~/.claude/skills/utility-pm-workflow-orchestrator

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->

PM Workflow Orchestrator (Dispatch Skill)

Cross-client dispatch wrapper for the pm-workflow-orchestrator sub-agent. Detects the runtime; dispatches to the native sub-agent on Claude Code; reads agents/pm-workflow-orchestrator.md and walks the run loop inline on non-Claude clients via the "reference + execute inline" pattern. The orchestrator runs an ordered sequence of pm-skills from a saved prioritized-action-plan (Mode A) or a user-named chain (Mode B), pausing for go/no-go by default and stopping on a failed or empty step.

Status summary (v2.24.0): EXPERIMENTAL on every client at ship.

  • Claude Code (native): EXPERIMENTAL until a live smoke test confirms the engine can invoke a downstream skill via the Skill tool in the installed plugin, and whether that skill runs inline in the engine context or isolated (see the v2.24.0 residual questions). The conductor is the model only for the run-loop / checkpoint / refusal SHAPE; no repo agent has ever declared Skill, so the mechanism is unproven.
  • All non-Claude clients (Codex CLI, Cursor, Windsurf, Copilot CLI, Gemini CLI): EXPERIMENTAL until a dedicated maintainer-gate test exercises a real multi-artifact inline WRITE run. The orchestrator writes up to three full PM artifacts and may thread state; this is strictly harder than the conductor's inline path and has never been live-validated off Claude Code.

On ANY client, run --dry-run FIRST as the readiness check (it exercises parsing, checkpointing, stop-on-fail, and the tool-capability pre-flight without invoking consequential skills). See the Sub-Agent Compatibility Matrix for the canonical safe-usage matrix.

When to Use

  • You produced a foundation-prioritized-action-plan and want to run its runnable Section 7 prompts in order, pausing for go/no-go (Mode A).
  • You want to run an ad-hoc, user-named chain of pm-skills against shared context (Mode B), for example deliver-prd then deliver-user-stories.
  • You want a generic runner for a dynamic chain that is NOT a pre-curated workflow-* command.

Run modes (orthogonal to client):

  • CHECKPOINTED (default): pause after each OK step for approve / edit / skip / redo.
  • GUARDED AUTO (--auto, opt-in): run without pausing on clean steps; still stop on failed, pause on empty, and stay checkpointed for Complex / Chaotic plans unless --force-auto.

When NOT to Use

  • You want a fixed, curated, named chain that an author maintains by hand (for example a full Foundation Sprint or Customer Discovery) -> use the existing workflow-* command (workflow-foundation-sprint, workflow-customer-discovery, ...). The orchestrator does NOT supersede those and surfaces a workflow step as MANUAL rather than nesting it.
  • You only need to PRODUCE a prioritized action plan (not run it) -> use foundation-prioritized-action-plan (which can then offer to hand its plan here).
  • You only need to review one artifact -> use utility-pm-critic.
  • You want to run library-maintenance machinery as if it were PM work -> the orchestrator refuses Tier-3 maintenance skills.

Instructions

Runtime detection step. Determine which AI client is invoking this skill. Run mode (checkpointed vs auto) comes from the user's flags and is independent of the client.

If you are running in Claude Code with the pm-skills plugin installed

Invoke @agent-pm-skills:pm-workflow-orchestrator with the user's input plus any flags from $ARGUMENTS (--auto, --force-auto, --dry-run). The native sub-agent parses the input, builds the step list, and delegates each step via the Skill tool to the real downstream skill, running its own per-step loop with confirmation pauses. Relay the engine's per-step output blocks and the terminal output to the user.

This path is EXPERIMENTAL at v2.24.0 (see Status summary). Instruct the user to run --dry-run first.

If you are running in any other AI client

Codex CLI, Cursor, Windsurf, Copilot CLI, Gemini CLI, or any client without native pm-skills plugin sub-agent support:

  1. Tool-capability pre-flight (FIRST action, before parsing or step 1). Verify the client can (a) write a file under _pm-skills/plan-orchestrator/<run>/ and (b) run any Bash a downstream skill needs.
    • If file write is unavailable: degrade explicitly to CHAT-ONLY mode (warn that multi-step reliability drops and context budget rises because full artifacts must stay in-window) for short runs, or refuse a long run with a clear message. Do not fail mid-chain.
    • If Bash is unavailable for a step that needs it: surface that step as blocked at its boundary.
    • Record the detected capabilities; they govern disk-write below.
  2. Read the canonical sub-agent definition at agents/pm-workflow-orchestrator.md.
  3. Read the shared parse and step-status contract at references/PARSE-CONTRACT.md (the engine reads the same file; this is what keeps the inline branch and the engine from drifting).
  4. Execute the engine body as your operating instructions: run the pre-flight refusals, build the ordered step list (Mode A parse or Mode B named chain), then walk the per-step loop.
  5. At each step, read the downstream skill's SKILL.md and execute its method inline in this window; classify the return PRODUCED / EMPTY / FAILED with the rubric in references/PARSE-CONTRACT.md; write NN-<skill-name>.md to the run directory when disk-write is on.
  6. Honor the run mode: pause at each OK step in CHECKPOINTED; auto-advance only unambiguously-PRODUCED steps in GUARDED AUTO; a FAILED step STOPS the run; an EMPTY or ambiguous step always pauses for confirmation (a forced checkpoint pause the user can clear), regardless of --force-auto.
  7. Emit the terminal output (run complete or halted) per the engine's section 4.5.

The "reference + execute inline" pattern is what enables cross-client compatibility. It is EXPERIMENTAL here until the maintainer-gate WRITE harness validates a real multi-artifact run on each client.

--dry-run (readiness probe; run this FIRST on any client)

--dry-run walks the full step list and exercises parsing, checkpointing, stop-on-fail, and the tool-capability pre-flight, but does NOT invoke any consequential downstream skill. Each step emits "NOT EXECUTED - dry run" plus what WOULD happen (which skill, which mode, whether disk-write would engage). Use it to confirm the plan parses, the chain resolves, and the client has the tool access the live run needs, before spending tokens on real delegation.

Cross-Client Notes

Because non-Claude clients run the loop and every step inline in ONE context window:

  1. Context budget. The combined budget (engine body + per-step downstream skill reads + produced artifacts) can approach context limits on multi-step runs. The engine auto-enables disk-write for 2+ step runs and keeps only a one-line summary per completed step in context; CHAT-ONLY mode loses that mitigation, so prefer a write-capable client for long chains.
  2. Tool authorization. Inline execution must have file-write and Bash access (not Read-only). The pre-flight (step 1 above) checks this. Codex may need its sandbox write flag enabled; Cursor and Gemini may interpose approval gates per step.
  3. No native chaining. This is why the engine delegates by Skill (Claude Code) or inline read-and-execute (non-Claude), never by spawning a sub-agent. A Mode A step that resolves to a dispatch skill which would fan out to a sub-agent (only utility-pm-critic) is handled by inlining the leaf agent, never by chaining (engine section 7.2).

Reference Files

GitHub リポジトリ

product-on-purpose/pm-skills
パス: skills/utility-pm-workflow-orchestrator
0
agent-skillsagentskillsai-skillsclaude-codeclaude-desktopcodex

関連スキル

content-collections

メタ

このスキルは、Content Collections(Markdown/MDXファイルを型安全なデータコレクションに変換するTypeScriptファーストのツール)の本番環境でテストされた設定を提供します。Zodバリデーションによる型安全性を実現し、ブログ、ドキュメントサイト、コンテンツ重視のVite + Reactアプリケーション構築時にご利用ください。Viteプラグインの設定、MDXコンパイルから、デプロイ最適化、スキーマバリデーションまで、すべてを網羅しています。

スキルを見る

polymarket

メタ

このスキルは、開発者がPolymarket予測市場プラットフォームを活用したアプリケーション構築を可能にします。API統合による取引や市場データの取得に加え、WebSocketを介したリアルタイムデータストリーミングにより、ライブ取引や市場活動を監視できます。取引戦略の実装や、ライブ市場更新を処理するツールの作成にご利用ください。

スキルを見る

creating-opencode-plugins

メタ

このスキルは、開発者がコマンド、ファイル、LSP操作など25種類以上のイベントタイプにフックするOpenCodeプラグインを作成することを支援します。JavaScript/TypeScriptモジュール向けに、プラグイン構造、イベントAPI仕様、および実装パターンを提供します。カスタムイベント駆動ロジックでOpenCode AIアシスタントのライフサイクルをインターセプト、監視、または拡張する必要がある場合にご利用ください。

スキルを見る

sglang

メタ

SGLangは、高性能なLLMサービングフレームワークであり、RadixAttentionプレフィックスキャッシュを活用したJSON、正規表現、エージェントワークフロー向けの高速で構造化された生成を特長とします。特にプレフィックスが繰り返されるタスクにおいて、大幅に高速な推論を実現し、複雑な構造化出力やマルチターン対話に最適です。制約付きデコードが必要な場合や、広範なプレフィックス共有を伴うアプリケーションを構築する場合は、vLLMなどの代替案ではなくSGLangを選択してください。

スキルを見る