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落地鸿沟补全器
by
BeAChanger
· GitHub ↗
· v1.0.0
· MIT-0
221
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Install in OpenClaw
/install blueprint-to-deployment
Description
将 Agent 规划结果补全为可交付、可部署的落地闭环。适用于把蓝图、架构、next actions 写入目标 Agent workspace，并主动推进到平台部署与接入引导。
README (SKILL.md)
落地鸿沟补全器
用途
这只 skill 用来补全“从规划到可用 Agent”之间的鸿沟。
很多 Agent 流程会停在：
- 聊天里
- 方案里
- 蓝图里
- 看起来已经想清楚了
但没有真正完成：
- 文档落地
- workspace 归档
- 用户交付说明
- 部署平台追问
- 平台接入引导
本 skill 的目标，就是把这些缺口补上。
什么时候用
当你已经有：
- Agent 蓝图
- 架构规划
- MVP 边界
- next actions
但还没真正把它交到“目标 Agent 自己的 workspace”里，或还没推进到平台接入时，触发本 skill。
核心原则
- 每只 Agent 都应拥有自己的资产
- 母 skill 保留方法论，目标 Agent 保留自己的交付资产
- 文档归属也是上下文管理
- 设计不能停留在聊天里
- 如果目标是可用，就必须主动推进到部署层
标准流程
第 1 步：确认目标 Agent workspace
先确认目标 Agent 的 workspace 已存在，或即将被创建。
第 2 步：写入关键交付文档
默认至少写入：
- 蓝图文档
- 架构 / MVP / 边界说明
- next actions
- 当天推进记录
推荐落位：
memory/reports/memory/cards/memory/YYYY-MM-DD.mdskills/
第 3 步：给用户交付地图
明确告诉用户：
- workspace 路径
- 文件路径
- 每个文件的作用
- 后续怎么改、去哪里看
第 4 步：主动问部署平台
不要等用户自己问。 主动追问：
- Telegram
- Feishu
- Discord
- 其他 channel
第 5 步：按平台继续引导
根据用户选择的平台，说明：
- 需要什么 bot / app / account
- 需要哪些配置
- 如何做首次联调
为什么值得单独做成 skill
因为这不是某一只母虾私有的补丁， 而是所有“会产出蓝图”的 Agent / skill 都可能需要的后半段闭环能力。
它解决的问题不是“如何规划”，而是： 如何避免规划停在纸面上。
输出要求
至少交付：
- 目标 workspace 已写入哪些内容
- 这些文件分别在哪
- 下一步该选哪个平台
- 平台接入该怎么开始
反模式
- 只给聊天建议，不写文件
- 文档全留在主 Agent / 母 skill workspace
- 不告诉用户文件在哪里
- 不主动问平台
- 让流程停在“我们已经设计完了”
Usage Guidance
This skill's purpose (turn plans into deliverables and guide deployment) is reasonable, but check two things before installing/using it: 1) Where do the recommended paths map to in your environment? Ensure the agent is only allowed to write non-executable documentation (avoid letting it create/modify files under `skills/` or other directories that contain executable code). Require explicit user confirmation before any write to sensitive paths. 2) Expect the skill to ask for platform credentials or bot tokens during runs; decide where those secrets will be entered and how they'll be stored/audited. If you can't confirm the mapping of workspace paths or you don't want the agent to modify skill code, treat this skill as risky and restrict its write permissions or have it output proposed content for manual review instead.
Capability Analysis
Type: OpenClaw Skill
Name: blueprint-to-deployment
Version: 1.0.0
The skill bundle consists of metadata and markdown instructions (SKILL.md) designed to automate the transition from planning to deployment. It instructs the AI agent to write documentation, blueprints, and action plans to specific workspace directories and guides the user through platform-specific setup (e.g., Telegram, Discord). No executable code, data exfiltration logic, or malicious prompt injections were found; the instructions are functionally aligned with the stated purpose of deployment orchestration.
Capability Assessment
Purpose & Capability
The name/description (从规划到落地的闭环) aligns with the instructions to write blueprints, next actions, and deployment guidance into a target Agent workspace. Recommending memory/paths such as memory/reports/ and memory/YYYY-MM-DD.md is proportionate. However, the guidance also recommends writing into `skills/`, which is typically where executable skill code lives — that scope is broader than 'documentation/delivery' and deserves justification.
Instruction Scope
SKILL.md explicitly instructs the agent to create and write files into a target workspace and to '主动推进' to deployment platforms (Telegram, Feishu, Discord). Writing documentation is expected, but targeting `skills/` or adding files that could be treated as skills or code is scope creep and could modify agent behavior. The instructions do not constrain what may be written (e.g., strictly non-executable docs), nor do they require user confirmation before modifying sensitive paths. The doc also implies the agent may request platform/account info during the run — the skill does not declare how such secrets are handled.
Install Mechanism
No install specification and no code files — instruction-only. This is the lowest install risk (nothing is fetched or executed from external URLs).
Credentials
The skill declares no required environment variables or credentials, which is coherent for a documentation/handoff tool. However, the runtime guidance expects the agent to initiate platform onboarding and ask about bots/apps/accounts; that may lead to requests for tokens/credentials at runtime even though none are declared. The absence of declared credential requirements is plausible but the skill should explicitly state how credentials will be requested, used, and stored.
Persistence & Privilege
always:false and normal autonomous invocation are appropriate. The concern is that instructions encourage writing into `skills/` (potentially modifying executable skill code) and target other agents' workspaces; that is effectively granting the skill write access beyond passive documentation. The skill does not require or document user confirmation flows, nor does it limit writes to non-executable docs.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install blueprint-to-deployment - After installation, invoke the skill by name or use
/blueprint-to-deployment - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release: bridge planning to delivery by writing docs into target agent workspace, showing handoff map, and proactively guiding platform deployment.
Metadata
Frequently Asked Questions
What is 落地鸿沟补全器?
将 Agent 规划结果补全为可交付、可部署的落地闭环。适用于把蓝图、架构、next actions 写入目标 Agent workspace，并主动推进到平台部署与接入引导。 It is an AI Agent Skill for Claude Code / OpenClaw, with 221 downloads so far.
How do I install 落地鸿沟补全器?
Run "/install blueprint-to-deployment" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is 落地鸿沟补全器 free?
Yes, 落地鸿沟补全器 is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does 落地鸿沟补全器 support?
落地鸿沟补全器 is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created 落地鸿沟补全器?
It is built and maintained by BeAChanger (@beachanger); the current version is v1.0.0.
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