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AB markdown-to-word-mermaid

Export Markdown containing Mermaid code fences to Word (.docx) with embedded SVG diagrams and PNG fallbacks. Use when diagram sharpness, Office-compatible labels, or page-aware placement matters. Not for Word-to-Markdown conversion, editable Office shapes, or general slide creation.

github/awesome-copilot Claude Code author: github 6 files · 2 scripts body ≈ 1 966 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Export Markdown containing Mermaid code fences to Word (.docx) with embedded SVG diagrams and PNG fallbacks. Use when diagram sharpness, Office-compatible…

As a process B 65/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

ProcedureWordSoftware developmentData and analyticsAI and agentstype and topics are labelled automatically from the skill text
Runs in: GitHub Copilot / VS Code Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
99
Quality 40%
96
Run on models
none yet
Process rating
B
65/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Result and completion w 14
40
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Obfuscation obf-hex-escape-chain scripts/markdown_to_word.py:42
      Escaped/char-code string obfuscation (quoted — discussed, not commanded)
      b"\x08\x06\x00\x00\x00\x1f\x15\xc4\x89\x00\x00\x00\x0bIDAT"
      quoted

    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 65/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python, node) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 31 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1966 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 283: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 31 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented
    • +1License stated

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