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AC chartgen

Use this skill when the user wants to create visualizations (charts, dashboards, diagrams, Gantt, PPT), analyze data (Excel/CSV upload, cross-file analysis, trends, outliers) or generate reports. Also use when the user mentions ChartGen or uploads spreadsheet files.

ClawHub Agent Skills author: ChartGen AI v1.0.6 MIT-0 4 files body ≈ 2 058 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency

GeneratorData and analyticstype 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

The same skill appears in 7 more places: ClawHub, ClawHub, ClawHub, ClawHub, ClawHub, ClawHub, ClawHub

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 12 mutating operations with no state check
    • 40Consistency. Frontmatter name (chartgen) differs from the folder (analysis-data)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 39 steps
    • 100Failures and branches. 6 branches, has a failure section
    • 100Execution cost. Instruction body is 2058 tokens
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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 266: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 39 items
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.

    External checks

    ClawHub: suspicious
    The skill mostly matches its ChartGen purpose, but one helper path can fetch result files from arbitrary HTTPS URLs despite the stated chartgen.ai-only network boundary.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026