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BD SVG绘图工作台-智能生图

用「编写 SVG」的方式生成图片——让没有多模态生图能力的模型也能产出任意宽高比、任意复杂度的矢量图，并导出为 SVG / PNG / 动图(GIF/APNG/WebP/MP4)，或内嵌进 HTML。 适用场景：用户要「生成一张图 / 画个图 / 做张配图 / 做海报 / 信息图 / 图表 / 流程图 / 架构图 / 图标 / 封面 / OG 图」，要求指定宽高比(16:9 / 1:1 / 9:16 等)或导出 PNG；也包括「做个动图 / GIF / 加载动画 / loading 动效」。 能力：① 按指令编写复杂 SVG（图表/插画/流程图/海报）；② SVG→PNG 高清渲染(Chrome 优先，cairosvg/resvg 兜底)；③ 多帧/模板 SVG→GIF/APNG/WebP/MP4 动图；④ 任意宽高比与 DPR。 关键词：生图、画图、SVG、矢量图、配图、海报、信息图、图表、流程图、架构图、图标、封面、OG image、转 PNG、动图、GIF、动画、loading。

ClawHub Agent Skills author: dqsjqian v1.2.0 MIT-0 9 files body ≈ 1 201 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

用「编写 SVG」的方式生成图片——让没有多模态生图能力的模型也能产出任意宽高比、任意复杂度的矢量图，并导出为 SVG / PNG / 动图(GIF/APNG/WebP/MP4)，或内嵌进 HTML。 适用场景：用户要「生成一张图 / 画个图 / 做张配图 / 做海报 / 信息图 / 图表 / 流程图 / 架构图…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "description_zh"
  • note frontmatter-key unknown frontmatter key "description_en"
  • note frontmatter-key unknown frontmatter key "disable"
  • note frontmatter-key unknown frontmatter key "agent_created"

Process rating: all ten parameters 46/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (SVG绘图工作台-智能生图) differs from the folder (svg-studio)
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 30 steps
  • 100Execution cost. Instruction body is 1201 tokens
  • 100Running it twice. No mutating operations
  • low 10 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (7 tags): a typed call is more reliable

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 449: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
The skill does what it says, but it needs Review because normal use can automatically install unpinned Python packages and render SVGs in an unsandboxed headless browser.
LLM: suspicious (high) · VirusTotal: · 2 Sept 2026