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AC industry-evolution-3d

Turn any domain's "industry / technology / people evolution history" into an interactive 3D spatiotemporal graph web page — a world map as the base, a vertical time axis (year → height), nodes positioned by real lat/lon + year, hover-to-bring-to-front with detail cards. Use when the user wants "a timeline map / evolution graph / industry-layout visualization of domain X", or mentions an interactive web page combining "geo coordinates + time + nodes + hover cards". Feed it a JSON dataset and it generates a self-contained HTML.

ClawHub Agent Skills author: ooof v1.0.2 MIT-0 6 files body ≈ 1 425 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Turn any domain's "industry / technology / people evolution history" into an interactive 3D spatiotemporal graph web page — a world map as the base, a…

As a process C 57/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

ProcedureData and analyticsSoftware developmentInfrastructuretype 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
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "agent_created"

    Process rating: all ten parameters 57/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 18 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1425 tokens

    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

    • +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
    • +5Description quotes 3 example trigger phrases
    • +3Description length 531: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 18 items
    • +4Has examples (2 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.

    External checks

    ClawHub: suspicious
    The skill is mostly a coherent 3D visualization generator, but it under-discloses network behavior in a page it advertises as self-contained and offline-openable.
    LLM: suspicious (high) · 8 Aug 2026