返回技能列表

utility-pm-workflow-orchestrator

product-on-purpose
更新于 7 days ago
283
39
283
在 GitHub 上查看
aitestingautomation

关于

This skill sequentially executes multiple pm-skills against a single input, pausing for approval between steps and stopping on failures. On Claude Code, it uses the pm-skills plugin for native execution, while on other clients it runs inline after a capability check. It's currently experimental and requires explicit, dry-run invocation.

快速安装

Claude Code

推荐
主要方式
npx skills add product-on-purpose/pm-skills -a claude-code
插件命令备选方式
/plugin add https://github.com/product-on-purpose/pm-skills
Git 克隆备选方式
git clone https://github.com/product-on-purpose/pm-skills.git ~/.claude/skills/utility-pm-workflow-orchestrator

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

<!-- 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 仓库

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

相关推荐技能

content-collections

Content Collections 是一个 TypeScript 优先的构建工具,可将本地 Markdown/MDX 文件转换为类型安全的数据集合。它专为构建博客、文档站和内容密集型 Vite+React 应用而设计,提供基于 Zod 的自动模式验证。该工具涵盖从 Vite 插件配置、MDX 编译到生产环境部署的完整工作流。

查看技能

polymarket

这个Claude Skill为开发者提供完整的Polymarket预测市场开发支持,涵盖API调用、交易执行和市场数据分析。关键特性包括实时WebSocket数据流,可监控实时交易、订单和市场动态。开发者可用它构建预测市场应用、实施交易策略并集成实时市场预测功能。

查看技能

creating-opencode-plugins

该Skill帮助开发者创建OpenCode插件,用于接入命令、文件、LSP等25+种事件。它提供了插件结构、事件API规范和JavaScript/TypeScript实现模式,适合需要拦截操作、扩展功能或自定义事件处理的场景。开发者可通过它快速构建响应式模块来增强OpenCode AI助手的能力。

查看技能

sglang

SGLang是一个专为LLM设计的高性能推理框架,特别适用于需要结构化输出的场景。它通过RadixAttention前缀缓存技术,在处理JSON、正则表达式、工具调用等具有重复前缀的复杂工作流时,能实现极速生成。如果你正在构建智能体或多轮对话系统,并追求远超vLLM的推理性能,SGLang是理想选择。

查看技能