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arch-flow拱流

Agent Skill

arch-flow 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

210

周安装

9

GitHub Stars

公开资料未说明

下载量

73
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:arch-flow(拱流)
来源仓库:https://github.com/vitadynamics/vita-cc-market
仓库路径:skills/arch-flow
安装命令:
npx skills add https://github.com/vitadynamics/vita-cc-market --skill arch-flow
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/vitadynamics/vita-cc-market --skill arch-flow

简介

用于项目规划流程管理,适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

  • 按 Sketch→Spec→Plan→ADR 阶段推进。
  • 适合结构化需求拆解与文档沉淀。
  • 使用时需提供初始想法或指定阶段起点以继续流程。
  • arch-flow 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Arch-Flow Skill

Purpose: Top-level orchestrator for the planning pipeline. Runs Sketch → Spec → Plan → ADR in sequence, pausing at each stage gate for user confirmation. Detects existing docs/ artifacts and resumes from the right stage automatically.

Input

$ARGUMENTS

Can be:

  • A plain-text feature idea → start from Stage 1 (Sketch)
  • docs/sketches/<file>.md → start from Stage 2 (Spec)
  • docs/specs/<file>.md → start from Stage 3 (Plan)
  • docs/plans/<file>.md → start from Stage 4 (ADR)
  • A topic keyword (no path) → auto-detect stage (see below)
  • Empty → ask via AskUserQuestion: "What feature or topic would you like to plan?"

Auto-Detection (Resume Protocol)

When given a topic string (not a file path), search for existing artifacts in this order:

ls docs/plans/ 2>/dev/null | grep -i "<topic>"   # → Stage 4
ls docs/specs/ 2>/dev/null | grep -i "<topic>"   # → Stage 3
ls docs/sketches/ 2>/dev/null | grep -i "<topic>" # → Stage 2

If an existing artifact is found, present via AskUserQuestion:

"Found existing [plan/spec/sketch] at [path]. Resume from Stage N ([stage name])? Or start fresh from Stage 1?"

Pipeline

Each stage ends with a gate — do not proceed to the next stage without explicit user confirmation.

Display a progress banner at each gate:

Pipeline: [✓ Sketch] → [✓ Spec] → [→ Plan] → [ ADR]

Stage 1: Sketch (light-plan)

Goal: Capture a freeform brainstorm/design through dialogue.

Follow the light-plan skill workflow:

  1. Check current project state (files, docs, recent commits)
  2. Ask questions one at a time (multiple choice preferred) to refine the idea
  3. Propose 2-3 approaches with trade-offs; lead with your recommendation
  4. Present the design in sections of 200-300 words, validate each section
  5. Write final sketch to docs/sketches/YYYY-MM-DD-<topic>.md with frontmatter: --- stage: sketch created: YYYY-MM-DD topic: <topic> status: draft ---

Gate: "Sketch complete at docs/sketches/<file>. Proceed to spec generation? (Y / edit / stop)"


Stage 2: Spec (turn2spec)

Goal: Transform the sketch into a formal, implementation-agnostic feature specification.

Follow the turn2spec skill workflow:

  1. Load docs/sketches/<file>.md as source material
  2. Extract: Feature Name, Actors, Core Actions, Constraints, Success Signals, Decisions Made, Open Questions, Data Entities
  3. Map extracted content to the spec template (plugins/Vengineer/skills/reference/spec_template.md)
  4. Write spec to docs/specs/<feature-name>.md with frontmatter: --- stage: spec created: YYYY-MM-DD feature: <feature-name> source-sketch: docs/sketches/<file>.md status: draft ---
  5. Update sketch frontmatter: add next-spec: docs/specs/<feature-name>.md
  6. Handle up to 3 [NEEDS CLARIFICATION] markers via AskUserQuestion (one at a time)
  7. Validate: no implementation details, all acceptance scenarios in Given/When/Then format

Gate: "Spec complete at docs/specs/<feature-name>.md. Proceed to implementation plan? (Y / review / stop)"


Stage 3: Plan (medium-plan + optional deepen-plan)

Goal: Produce a structured implementation plan with parallelization strategy.

Follow the medium-plan skill workflow:

  1. Run these three agents in parallel. For each: read the referenced file to get the system prompt (body after the YAML frontmatter ---), then launch a general-purpose Task agent with that system prompt. Agent 1 — Repository Research: Agent 2 — Best Practices Research: Agent 3 — Framework Documentation:

- System prompt file: plugins/Vengineer/agents/research/repo-research-analyst.md - User prompt: "Research repository conventions and patterns for: {feature from spec}" - System prompt file: plugins/Vengineer/agents/research/best-practice-research.md - User prompt: "Research industry best practices relevant to: {feature from spec}" - System prompt file: plugins/Vengineer/agents/research/framework-docs-researcher.md - User prompt: "Research framework and library documentation relevant to: {feature from spec}"

  1. Run SpecFlow analysis — launch a general-purpose Task agent (prefer Claude Haiku if available) with:

- System prompt file: plugins/Vengineer/agents/core/spec-flow-analyzer.md (body after frontmatter) - User prompt: "{spec content}. Research findings: {research findings}"

  1. Build the plan following the spec's requirements and research findings
  2. Include an Execution Strategy section with:

- Mermaid graph LR dependency diagram (green fill = can start immediately) - Phase table: Phase | Name | Depends On | Can Parallelize With | Effort - Inline task tags: [PARALLEL:group-id] or [SERIAL:after-group-id]

  1. Write plan to docs/plans/<feature-name>.md with frontmatter: --- stage: plan created: YYYY-MM-DD feature: <feature-name> source-spec: docs/specs/<feature-name>.md status: draft ---
  2. Update spec frontmatter: add next-plan: docs/plans/<feature-name>.md

Then ask: "Run deepen-plan for maximum research depth? (Y / skip)"

  • If Y: follow the deepen-plan workflow on docs/plans/<feature-name>.md — launch 12 parallel research agents across architecture, performance, security, testing, integration dimensions; integrate findings

Gate: "Plan complete at docs/plans/<feature-name>.md. Proceed to ADR generation? (Y / review / stop)"


Stage 4: ADR (adr)

Goal: Crystallize the key architectural decisions into permanent records.

Follow the adr skill workflow on docs/plans/<feature-name>.md:

  1. Scan plan for architectural decision candidates
  2. Present candidate list via AskUserQuestion — user selects which to record
  3. For each selected decision: determine next NNNN, write docs/adr/NNNN-<title>.md, update docs/adr/README.md
  4. Update plan frontmatter: add adr-refs: list

Gate: "ADR(s) written. Create GitHub issues from the plan? (Y / skip / done)"


Stage 5: Issues (batch-issues, optional)

Goal: Decompose the plan into actionable GitHub issues.

Follow the batch-issues skill workflow on docs/plans/<feature-name>.md.


Pipeline Summary (displayed at end)

## Pipeline Complete

| Stage  | Artifact         | Path                          |
|--------|------------------|-------------------------------|
| Sketch | Brainstorm/design | docs/sketches/YYYY-MM-DD-...  |
| Spec   | Feature spec      | docs/specs/<feature-name>.md  |
| Plan   | Impl plan         | docs/plans/<feature-name>.md  |
| ADR    | Decision record(s)| docs/adr/NNNN-*.md            |

Key Principles

  • Gate at every stage — never auto-advance without user confirmation
  • Resume, don't restart — detect existing artifacts and offer to continue from the right point
  • Pipeline stages are inline — run each stage's workflow directly in this conversation (not as Task subagents), preserving context across stage transitions
  • Stop is always valid — the user can stop at any gate; all artifacts written so far are valid standalone documents

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

39.28%
按下载量换算29

Claude

27.02%
按下载量换算20

Cursor

19.25%
按下载量换算14

Gemini CLI

8.71%
按下载量换算6

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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