drawio-skill
Agents365-ai/drawio-skill · Agent Skill
建立、編輯和同步 draw.io 圖表。適用於架構、ERD、UML、BPMN 等圖。將程式碼轉換為圖表。
前往安裝 ↓總覽
| 發布者 | |
|---|---|
| 儲存庫 | Agents365-ai/drawio-skill ↗ |
| 技能資料夾 | drawio-skill |
| 星 | 9,126 |
| 附帶檔案 | 1 |
| 授權 | MIT |
| 最近更新 | 2026年9月2日 |
| 連結 | 技能原始碼 ↗ · 儲存庫 ↗ |
Mozgi 檢查
我們在技能檔案中發現的內容。詳細資訊需訂閱。
安裝
技能就是一個含 SKILL.md 的資料夾。選擇你的代理,在專案根目錄的終端機(macOS、Linux 或 Windows 的 Git Bash)執行指令。
把提示貼到 Claude、Codex 或其他助理:它會自行檢查並安裝技能。
Install the “drawio-skill” skill from mozgi.io (https://mozgi.io/skills/drawio-skill/) for me. 1. Download the archive: https://mozgi.io/skills/drawio-skill/download If it fails, take the skill folder from the source: https://github.com/Agents365-ai/drawio-skill/tree/main/skills/drawio-skill 2. Read SKILL.md and the other files. Check the skill is safe: no hidden commands, no sending data elsewhere, no scripts doing something other than described. If anything looks suspicious, stop and tell me. 3. Put the “drawio-skill” folder into your agent's skills folder: .claude/skills/ for Claude Code, .agents/skills/ for Codex, .cursor/skills/ for Cursor, .gemini/skills/ for Gemini CLI. Inside the project if we are in one, otherwise in the home folder (~/.claude/skills/ etc.). 4. Make sure <skills folder>/drawio-skill/SKILL.md exists. 5. Briefly tell me what this skill does and offer to try it on my task. If you cannot do a step yourself, tell me what to click or type.
git clone --depth 1 --branch main https://github.com/Agents365-ai/drawio-skill.git /tmp/drawio-skill mkdir -p .claude/skills cp -r /tmp/drawio-skill/skills/drawio-skill .claude/skills/drawio-skill
重新啟動 Claude Code — 技能就會出現在清單中,手動呼叫:/drawio-skill。要套用到所有專案,請放在 ~/.claude/skills。
git clone --depth 1 --branch main https://github.com/Agents365-ai/drawio-skill.git /tmp/drawio-skill mkdir -p .agents/skills cp -r /tmp/drawio-skill/skills/drawio-skill .agents/skills/drawio-skill
Codex 會從目前資料夾往上到儲存庫根目錄,在 .agents/skills 中尋找技能;用 /skills 或 $drawio-skill 呼叫。所有專案:~/.agents/skills。
git clone --depth 1 --branch main https://github.com/Agents365-ai/drawio-skill.git /tmp/drawio-skill mkdir -p .cursor/skills cp -r /tmp/drawio-skill/skills/drawio-skill .cursor/skills/drawio-skill
Cursor 啟動時會載入技能;在 Agent 聊天輸入「/」呼叫。Cursor 也會讀取 .claude/skills 與 .agents/skills。所有專案:~/.cursor/skills。
git clone --depth 1 --branch main https://github.com/Agents365-ai/drawio-skill.git /tmp/drawio-skill mkdir -p .gemini/skills cp -r /tmp/drawio-skill/skills/drawio-skill .gemini/skills/drawio-skill
任務符合時,Gemini CLI 會提議啟用技能,由你確認。所有專案:~/.gemini/skills。
安裝前請先檢視 SKILL.md 與相關檔案。點擊檔案即可預覽。
SKILL.md 內容
作者原文 · 唯讀Draw.io Architecture Studio
Produce editable .drawio artifacts, not flattened pictures. The preferred
entrypoint is scripts/diagramctl.py, which unifies generation, incremental
sync, multi-view projection, semantic queries/tests/reviews, failure analysis,
and accessible publishing over a shared Diagram IR.
Choose the workflow
| Request | Route |
| --- | --- |
| Natural-language diagram with precise styling | Read references/diagram-types.md, then references/xml-authoring.md and author XML |
| Standard flowchart/mindmap/gantt/timeline/etc. with no special styling | If draw.io >=30, read references/mermaid-authoring.md and convert Mermaid to native .drawio |
| Large graph (~15+ nodes) that needs automatic layout | Use autolayout.py; read references/autolayout.md before passing any --layout value |
| Code, Terraform, K8s, compose, SQL, OpenAPI, AsyncAPI, or CI source | Use diagramctl.py build; read references/diagram-ir.md |
| Protocol Buffers schema (.proto) | Use protoimports.py or diagramctl.py build; read references/toolbox.md |
| GraphQL SDL schema (.graphql/.gql) or introspection JSON | Use graphqlerd.py or diagramctl.py build; read references/toolbox.md |
| Running cluster/stack/cloud (actual state, not declared config) | Read references/live-infra.md, then use tfstate.py, dockerimports.py, or k8simports.py - |
| Update a generated diagram without losing manual layout | Use diagramctl.py sync; read references/diagram-ir.md |
| Executive/system/deployment/data-flow/security views | Use diagramctl.py views; read references/diagram-ir.md |
| Query, architecture policy, review, what-if, or guided walkthrough | Read references/semantic-workflows.md |
| MCP host (Claude Desktop, Cursor, VS Code, Codex) should call these workflows | Register scripts/diagramctl_mcp.py; read references/mcp.md |
| Prompt phrasing for a diagram type or semantic workflow | Read references/cookbook.md |
| Enforce architecture rules or visual diffs in GitHub Actions CI | Read references/ci-gate.md |
| Rendered before/after/diff images as a PR review comment | Use prdiff.py; read references/pr-bot.md |
| Existing .drawio to HTML/PPTX/Mermaid/Markdown/animation/runbook | Read references/toolbox.md; diagramctl.py transform exposes the existing tools |
| Pipeline, journey, or subsystem map drawn as a metro/subway map | Use tubemap.py; read references/tubemap.md |
| Shape, cloud/vendor, AI, or Databricks icon | Read references/shapes.md or references/databricks.md; never guess shape names |
| Learn/apply/manage a visual style | Read references/style-presets.md |
| Extract a reusable style from an existing diagram or theme | Read references/style-extraction.md |
| Existing image to editable diagram (screenshot, whiteboard photo, legacy PNG) | Read references/derasterize.md |
| Export/platform problem | Read references/troubleshooting.md; for access/network questions read references/security.md |
Unified CLI
Run from this skill directory, or replace scripts/ with the absolute path to
this skill's scripts directory:
python3 scripts/diagramctl.py doctor
python3 scripts/diagramctl.py build model.json --from ir -o architecture.drawio
python3 scripts/diagramctl.py build ./infra --from terraform --group \
--ir-output architecture.ir.json -o architecture.drawio
python3 scripts/diagramctl.py sync architecture.drawio ./infra --from terraform \
-o architecture.next.drawio
python3 scripts/diagramctl.py views architecture.ir.json \
--views executive,system,deployment,dataflow,security -o views.drawio
python3 scripts/diagramctl.py test architecture.drawio --rules policy.yml
python3 scripts/diagramctl.py review architecture.drawio -o review.md
python3 scripts/diagramctl.py query architecture.drawio --from internet --to orders-db
python3 scripts/diagramctl.py whatif architecture.ir.json --fail kafka \
--drawio kafka-failure.drawio -o impact.json
python3 scripts/diagramctl.py story architecture.ir.json -o walkthrough.html
doctor does not launch GUI tools unless --probe is passed. Core semantic
commands are offline and stdlib-only.
Creation workflow
1. Infer the diagram type, audience, scope, output format, and location from the
request. Ask only when a missing choice materially changes the result;
default to PNG plus .drawio in the working directory.
2. Select the authoring route from the table above. For a data-backed diagram,
prefer Diagram IR and preserve provenance. For a large graph, use an importer
or autolayout.py; do not hand-place more than roughly fifteen nodes.
3. Resolve an explicitly named style preset, or the user's default preset, as
documented in references/style-presets.md. Structural diagram conventions
and visual presets compose; they do not replace each other.
4. Generate the .drawio, then run structural validation:
```bash
python3 scripts/validate.py diagram.drawio --score
```
When semantic metadata or an architecture policy is in scope, also run
diagramctl.py test. Do not present inferred semantic findings as verified
runtime facts.
5. Export a draft PNG without embedded XML and inspect it visually. Fix obvious
overlap, clipping, disconnected edges, edge-through-node routing, stacked
edges, and unreadable labels. Stop automatic vision repair after two rounds.
6. Show the draft and apply targeted edits. Preserve existing geometry for
local changes. Use sync for source-backed changes and write a reviewable
output; use --prune only when deletion was requested.
7. After approval, create final requested formats and report both editable
source and export paths.
Export invariants
Resolve the available binary once (drawio, draw.io, the macOS app path, or
the Windows executable) and use that exact binary for the run.
# Draft for visual inspection: never use -e here
drawio -x -f png --width 2000 -o diagram.png diagram.drawio
# Final editable PNG
drawio -x -f png -e -s 2 -o diagram.drawio.png diagram.drawio
python3 scripts/repair_png.py diagram.drawio.png
# Final editable SVG/PDF
drawio -x -f svg -e --embed-svg-images -o diagram.svg diagram.drawio
drawio -x -f pdf -e -o diagram.pdf diagram.drawio
Do not combine --width and -s. Embedded PNG exports require
repair_png.py; draft PNGs used by vision must not use -e. On Linux headless,
follow references/troubleshooting.md rather than improvising Electron flags.
If the CLI crashes in a macOS sandbox, try one permitted escalated run, then use
encode_drawio_url.py or deliver XML; do not repeatedly launch it.
Editing and identity
- Use stable semantic IDs and never reuse reserved IDs
0or1. - Every edge requires
<mxGeometry relative="1" as="geometry"/>. - For a local edit, change the matching cell only; for a global direction
change, regenerate/re-layout the page.
- Keep provenance,
data-model-id, semantic properties, manual geometry, and
manual styles intact unless the user requests otherwise.
- When reconciling, retain removals as reviewable faded elements by default.
- For edges stacked at a boundary, run
edgeports.py; add waypoints when an
edge still crosses an unrelated shape. There is no CLI-only edge rerouter
that preserves node positions.
Quality and trust
An attractive diagram can still be wrong. Prefer source-backed relationships,
show provenance where useful, distinguish exact extraction from AI inference,
and keep architecture review findings framed as prompts. Story HTML must remain
self-contained, keyboard usable, and include a text alternative. Never include
secrets in node properties or provenance because they are embedded in outputs.
For all focused scripts and composition patterns, read references/toolbox.md;
load only the task-specific reference needed for the current request.
常見問題
drawio-skill 技能有什麼用途?
建立、編輯和同步 draw.io 圖表。適用於架構、ERD、UML、BPMN 等圖。將程式碼轉換為圖表。
如何安裝 drawio-skill?
打開本頁的「安裝」區塊並選擇你的代理 — Claude Code、Codex、Cursor 或 Gemini CLI:那裡有把技能資料夾複製到正確位置的現成指令。也可以複製「透過助理」分頁中的提示,讓助理自行檢查並安裝技能。
drawio-skill 適用哪些 AI 模型?
技能是純 Markdown 指示(Agent Skills 格式),因此 drawio-skill 適用任何讀取 SKILL.md 的代理:Claude Code、Codex、Cursor、Gemini CLI、GitHub Copilot、OpenCode 等。只有在任務符合技能描述時,模型才會載入指示。
drawio-skill 是免費的嗎?
此技能由 Agents365-ai 公開發布,採用 MIT 授權。技能本身免費 — 你只需向 AI 供應商支付實際使用的 token 費用。
安裝技能安全嗎?
技能是代理會遵循的指示,安裝前請閱讀 SKILL.md 及相關檔案。技能資料夾中的腳本只有在代理執行時才會運行 — 留意它的動作,不要授予多餘權限。
技能和 MCP 伺服器或外掛有何不同?
MCP 伺服器或外掛為模型提供新工具 — 會執行並回傳結果的程式碼。技能提供的是知識與流程:如何處理任務、要遵循哪些步驟、好的成果長什麼樣子。它不需要伺服器或 API 金鑰。