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AB plume-infographic

Plume AI Infographic Generation Service. Triggered when users want to convert topics, long-form text, or reference images into infographics. Supports: topic infographics, long-form text to infographic, reference image infographics (sketch/style transfer/product embed/content rewrite), batch infographics, retry. Activate when user mentions: infographic, knowledge poster, visualize article, diagram, summary chart, timeline, turn this article into a graphic, create a visual about XX, use this infographic's style as reference, use this product image for infographic, replace the content of this infographic, create a series of infographics, split long text into multi-page infographics, 信息图, 知识图谱海报, 把文章可视化, 图解, 总结图, 时间线图, 把这篇文章转成图, 围绕XX主题做一张图解, 参照这张信息图的风格, 用这张产品图做信息图, 把这张信息图的内容换成, 做一组系列信息图, 把长文拆成多页信息图.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 12 files body ≈ 3 933 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Plume AI Infographic Generation Service.

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorWriting and documentstype 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
A
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
82
Run on models
none yet
Process rating
B
68/100
Nearly there
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

    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: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 68/100

    • 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
    • 30Running it twice. 22 mutating operations with no state check
    • 85Steps. 26 steps, 2 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100When it triggers. States when to use and when not to
    • 100Failures and branches. 4 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3933 tokens
    • low The response is described with custom markup (5 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)
    • +3Description length 806: 120–800 characters recommended
    • +3Output format is not stated: the model decides each time
    • -32 of 4 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (3 of 6)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 82.