SKILLEMALL.ai

AC boardraw

Turn a natural-language description into a real Excalidraw whiteboard file (.excalidraw) — flowcharts, mind maps, architecture/system diagrams, org charts, wireframes, sticky-note brainstorms, sequence diagrams. Always use this skill whenever the user asks to draw, sketch, diagram, whiteboard, mind-map, or map something out, or mentions "excalidraw", "boardraw", "白板", "画一个", "流程图", "思维导图", "架构图", "组织架构图", "线框图", "泳道图", "看板", or wants a file they can open in Excalidraw or Boardraw. Do NOT hand-write raw .excalidraw JSON directly — the schema has fragile id/seed/binding requirements — always build it with the bundled scripts/excalidraw_builder.py helper instead.

ClawHub Agent Skills author: darren v0.1.0 MIT-0 6 files body ≈ 1 701 tokens Open the sourceclawhub.ai analyzed 2 d ago

Turn a natural-language description into a real Excalidraw whiteboard file (.excalidraw) — flowcharts, mind maps, architecture/system diagrams, org charts…

As a process C 58/100 · Has gaps — weak spots: result and completion, inputs and preconditions, progress reporting

IntegrationDesignSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
96
Run on models
none yet
Process rating
C
58/100
Has gaps
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: 5. 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 58/100

    • 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
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 26 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1701 tokens
    • 100Running it twice. No mutating operations

    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 2 example trigger phrases
    • +3Description length 668: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 26 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 2 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.

    External checks

    ClawHub: suspicious
    The skill is mostly aligned with generating editable diagrams, but it defaults toward uploading diagram content to boardraw.com and using an API key with under-scoped disclosure and credential handling.
    LLM: suspicious (high) · 20 Aug 2026