SKILLEMALL.ai

BD aios-make-chart-image

当 OpenClaw 或 AIOS agent 需要把 JSON、Markdown 表格或 ECharts option 渲染成图表图片时,必须使用本技能。使用内置 JavaScript 脚本解析数据、生成 ECharts 配置,并导出 PNG、SVG、JPEG 或 WebP 图片;不要临时手写浏览器截图流程。

ClawHub Agent Skills author: 宁伟 v1.0.1 MIT-0 6 files body ≈ 768 tokens Open the sourceclawhub.ai analyzed 2 d ago

当 OpenClaw 或 AIOS agent 需要把 JSON、Markdown 表格或 ECharts option 渲染成图表图片时,必须使用本技能。使用内置 JavaScript 脚本解析数据、生成 ECharts 配置,并导出 PNG、SVG、JPEG 或 WebP 图片;不要临时手写浏览器截图流程。

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorSoftware developmentAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
95
Quality 40%
75
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token package-lock.json:18
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…U5f+Jr/W5tZ…4rk/SNOQiFWmaR/VKF4…6Pg==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:120
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…byA+l3XtsAj+Q8tf…oOo+X6HZ…Q8A==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:177
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…bSm+x6ETixtKZBh/qbRE…8Sr/Wcyx…yGA==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:234
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…UzJ+En8KcVm9Lk5+uGUQ…GXw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:272
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…595+ujv0…pwV/GONa…zQo/1O6zRIkh0m/8+5Bjr…SZw==",
    detector

Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 43/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 12 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 36 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 768 tokens
  • low The response is described with custom markup (6 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 156: enough signal without eating the budget
  • +4Structure: 8 headings
  • +3Step-by-step instructions: 36 items
  • +4Has examples (5 code blocks)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This skill does what it claims: it renders user-provided chart data into image files with sender-scoped workspace isolation.
LLM: benign (high) · VirusTotal: · 9 Jul 2026