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AC render-pdf-doc

Render academic Markdown documents (English or Korean) to publication-quality PDF via pandoc + xelatex. Targets non-bibliography artifacts: research proposals, IRB cover letters, briefing handouts, anchor docs (Q&A grids), and reference tables. Auto-infers pipe-table column widths from content (label column shrinks to fit, data columns share remaining width). CJK-aware font fallback for Korean text (Apple SD Gothic Neo on macOS, Noto Sans CJK KR on Linux). NOT for: manuscripts with bibliography (use /manage-refs render_pandoc.sh), Word form filling (/fill-protocol), figures (/make-figures).

Aperivue/medsci-skills Agent Skills author: Aperivue MIT 21 files · 7 scripts body ≈ 2 416 tokens Open the sourcegithub.com↗ analyzed 32 h ago

Render academic Markdown documents (English or Korean) to publication-quality PDF via pandoc + xelatex.

As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorWordSoftware developmentResearchtype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
C
59/100
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

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "triggers"
    • note frontmatter-key unknown frontmatter key "tools"

    Process rating: all ten parameters 59/100

    • 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. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 85Steps. 28 steps, 1 vague phrases
    • 100Tools and files. Tools declared in frontmatter
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2416 tokens
    • 100Progress reporting. Reports progress
    • low 10 top-level sections: this looks like several domains in one skill

    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
    • -5TODO / placeholder text left in the skill
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 597: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 28 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.