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Agent Visio Use

DrawForge 的轻量引导型 Skill,不包含完整仓库。它指引 Agent 克隆仓库、按指定顺序阅读入口文档、运行标准冷启动冒烟测试并验证 Visio 桥接与执行链路,最终进入基于 Visio 的可编辑图形复现流程。

qweadzchn
qweadzchn
热度 32.74
最近更新 2026-05-11 17:36:03
分类:开发依赖:代码执行 / 读写文件应用场景:工作流 / 自动化脚本

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add drawforge-agent-visio-use
Metadata
name
drawforge-bootstrap
description
Bootstrap skill for DrawForge. Use this skill to onboard an agent into the DrawForge GitHub repository, understand the project structure, run the canonical cold-start smoke test, and begin working with the Visio-based drawing loop safely.
version
0.1.2
metadata
{"openclaw":{"homepage":"https://github.com/qweadzchn/DrawForge","requires":{"bins":["git","python"],"env":["VISIO_BRIDGE_TOKEN"]}}}

DrawForge Bootstrap

This is a lightweight onboarding skill for the DrawForge repository.

It is not the full DrawForge system. Its job is to guide an agent to the correct GitHub repository, documents, smoke test, and execution flow.

DrawForge itself is an agent-driven closed loop built on top of Microsoft Visio. Its goal is to turn reference figures into directly editable diagram assets by helping agents operate Visio more like a capable human user rather than as a blind API caller.

What this skill can do

This skill can help an agent:

  • understand what DrawForge is trying to achieve
  • find the correct GitHub repository and entry documents
  • avoid random first-run behavior and jump into the intended workflow
  • run the canonical cold-start smoke test
  • start reproducing reference figures through the DrawForge Visio loop
  • move toward a result that a human can continue editing directly in Visio

Typical outcomes

After using this skill, an agent should be able to:

  • explain the DrawForge workflow clearly
  • bootstrap itself into the repository with the correct read order
  • validate that the Visio bridge and execution path are working
  • begin work on figure reproduction with better layer awareness
  • help produce editable .vsdx outputs instead of dead image copies

What this skill is for

Use this skill when an agent needs to:

  • find the DrawForge source repository
  • understand the top-level architecture quickly
  • avoid free-form blind retries
  • run the canonical cold-start smoke test
  • begin work in the correct layer

When to use it

Use this skill when:

  • an agent is entering DrawForge for the first time
  • a new environment needs to be validated before real drawing work
  • a user wants an agent to help reproduce a figure through Visio
  • the goal is not only to look similar to the reference, but to obtain a directly editable diagram asset

What this skill is not

This skill does not bundle the whole repository. It does not include Visio bridge code, benchmark PNGs, or runtime artifacts.

The full project lives in the GitHub repository:

https://github.com/qweadzchn/DrawForge

Recommended workflow

  1. Clone the GitHub repository locally.
  2. Read the cold-start entry documents.
  3. Run the canonical smoke test before doing open-ended drawing work.
  4. Only then move on to real jobs or system improvements.

Clone the repository

git clone git@github.com:qweadzchn/DrawForge.git
cd DrawForge

If SSH is not available, use HTTPS instead.

Read order

Read these files first:

  1. AGENT_START_HERE.md
  2. AGENT_GUIDE.md
  3. GET_STARTED.md
  4. docs/human/setup/AGENT_COLD_START_SMOKE_TEST.md
  5. MODE_POLICY.md

Canonical smoke test

From the repo root:

python Setup\prepare_smoke_test.py --config Setup\examples\smoke-test-inputpng-1.json
python Setup\run_draw_job.py --config Setup\examples\smoke-test-inputpng-1.json
python Setup\execute_drawdsl.py --config Setup\examples\smoke-test-inputpng-1.json --round 1 --save-final

Expected outputs:

  • OutputPreview/smoke-inputpng-1/round-01.png
  • OutputEditable/1_smoke_test_final.vsdx

Routing rule

When working inside DrawForge:

  • if the issue is round-specific, keep it in review artifacts
  • if it looks structural but still needs validation, write a proposal
  • if it is already reusable experience, promote it into a lesson
  • if the shared fix is clear, patch the owning layer directly

Where to go next

See:

  • README.md
  • CONTRIBUTING.md
  • docs/architecture/FEEDBACK_PROMOTION_LOOP.md