AC word-converter
Universal Word document converter powered by MinerU API. Convert .docx and .doc files to Markdown, HTML, LaTeX, DOCX, or JSON using mineru-open-api CLI. Supports quick flash-extract (no token, Markdown output) and precision extract with multi-format output, table recognition, formula detection, and batch processing. Use when asked to 'convert Word document', 'transform docx to markdown', 'Word to PDF', 'Word to LaTeX', 'change Word format', 'batch convert Word files', 'Word文档格式转换', '把Word转成其他格式', 'docx转markdown', 'Word批量转换', 'how do I convert my Word file to another format', 'is there a tool to convert docx'. Handles complex formatting, embedded objects, tables, formulas, and images. Ideal for academic writing, technical documentation, content migration, and automated document pipelines.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 8 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 282 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
- +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 798: enough signal without eating the budget
- +4Structure: 5 headings
- +3Step-by-step instructions: 8 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.