md-document
Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via /cs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.
- License 许可
- MIT
- 最近更新
- 2026-10-10
- Stars
- ★ 0
- Forks
- 0
01 前言 Front Matter
- name
- md-document
- tags
- [ "markdown", "html", "documentation", "single-file", "toc", "scrollspy", "search", "code-copy", "design-system" ]
- author
- Alireza Rezvani
- license
- MIT
- version
- 2.10.1
- description
- Converts long-form markdown (specs, RFCs, reports, plans, explainers) into a single-file, lightly-interactive HTML document with sticky TOC, scrollspy, search filter, code-copy buttons, and design-system-driven brand tokens. Triggers when the markdown-html-orchestrator classifies an input as DOCUMENT, or when invoked directly via /cs:md-document. Reads the design-system config via config_loader.py and inlines the user's 12 derived CSS custom properties; refuses to render if onboarding hasn't run. Single-file output — Google Fonts + Prism.js CDN are the only externals; no framework runtime, no build step. Use after orchestrator routing or after design-system onboarding is confirmed.
- compatible_tools
- [ "claude-code", "codex-cli", "cursor", "antigravity", "opencode", "gemini-cli" ]
03 SKILL.md 正文
md-document — Long-form Markdown to HTML
The general-purpose converter — handles the 90% case Shihipar describes (specs, plans, RFCs, reports, explainers). Three stdlib tools pipeline together:
markdown_parser.py → html_renderer.py → interactivity_injector.py
(md → JSON AST) (AST + tokens → HTML) (HTML + JS behavior)
Output is one .html file with sticky TOC, search filter, scrollspy, code-copy buttons, and the user's 12 derived brand tokens. Externals limited to Google Fonts CSS + Prism.js CDN.
When to invoke
| Symptom | Action |
|---|---|
markdown-html-orchestrator routes input as DOCUMENT |
Invoke this skill |
User runs /cs:md-document <path>.md directly |
Invoke this skill |
| User says "convert this spec/report/RFC/plan to HTML" | Invoke this skill |
Input is a code review (has ```diff blocks) |
Route to md-review instead |
Input is a slide deck (clear --- boundaries) |
Route to md-slides instead |
| Input is < 100 lines | Refuse (Shihipar threshold — markdown still wins) |
| Design-system not onboarded | Refuse, surface /cs:design-system |
Pipeline
# 1. Parse markdown → JSON AST
python3 markdown-html/skills/md-document/scripts/markdown_parser.py \
--input <path>.md --output sections.json
# 2. Render AST + design-system config → single-file HTML
python3 markdown-html/skills/md-document/scripts/html_renderer.py \
--sections sections.json --output document.html
# 3. Inject lightweight JS (search, copycode, smoothscroll, scrollspy)
python3 markdown-html/skills/md-document/scripts/interactivity_injector.py \
--file document.html \
--features search,copycode,smoothscroll,scrollspy
Or all-in-one (sample render):
python3 markdown-html/skills/md-document/scripts/html_renderer.py --sample \
| python3 markdown-html/skills/md-document/scripts/interactivity_injector.py \
--file /dev/stdin --output document.html
What gets rendered
CommonMark subset sufficient for agent-generated artifacts:
- Headings H1-H6 (every H2+ gets an anchor id and TOC entry)
- Paragraphs with inline bold / italic /
code/ links / - Fenced code blocks (
```python) with Prism.js highlighting on demand - GFM tables with per-column alignment
- GFM callouts (
> [!NOTE],> [!TIP],> [!IMPORTANT],> [!WARNING],> [!CAUTION]) - Blockquotes, ordered + unordered lists (single-level), horizontal rules
Out of scope: nested lists, HTML inlines, footnotes, definition lists, task list checkboxes (rendered as plain text), reference-style links.
Hard rules
- Refuses input < 100 lines. Markdown wins below the threshold (Shihipar).
- Refuses without onboarding.
config_loader.setup_completed()must returnTrue. Otherwise surface/cs:design-system. - Single-file output. All CSS + JS inline. Only externals are
fonts.googleapis.comandcdn.jsdelivr.net(Prism). Anything else is a regression. - Customization must change behavior.
design_style=editorialproduces 720px-wide layout with 1.75 line-height;playfulrounds the callouts and adds shadow;technicalis dense with 0.875rem code. Smoke-tested. - WCAG-compliant tokens. Inherits the design-system's WCAG AA palette — body text ≥ 4.5:1 contrast, links iteratively walked to 4.5:1.
- Idempotent injection. Re-injecting interactivity is a no-op (marker check). Re-rendering with a different design_style works cleanly.
Forcing-question library (Matt Pocock grill discipline)
- What's the document for — skim, decide, or deep-read? Recommended: name it; density follows. Canon: Shihipar; Tufte Envisioning Information.
- Sticky-sidebar TOC or collapsible-top? Recommended: sticky-sidebar for > 800 words / 4+ H2s; collapsible-top for shorter mobile-first docs. Canon: NN/g TOC Best Practices (2023).
- All four interactive features, or a subset? Recommended: all four — none of them cost more than ~1 KB. Canon: Wattenberger Why React isn't great for actually building websites.
- Code theme — light, dark, or auto? Recommended: auto (follows OS
prefers-color-scheme). Canon: WCAG 2.2 §1.4.3. - Does the document have a clear H1 title? Recommended: yes — H1 becomes the page
<title>and is excluded from the TOC.
Distinct from
md-review— that converter renders diff blocks + severity-tagged margin annotations. This one renders prose + tables + code + callouts.md-slides— that converter splits on---boundaries into slides. This one renders one continuous document.marketing/landing/— that generates landing pages from scratch (no markdown input). This converts existing markdown.
Output artifact
{default_output_dir}/doc-{slug}.html (path resolved by orchestrator's output_path_resolver.py; collision suffix -2, -3, … by default).
References
- Shihipar — Claude Code HTML output (Medium, 2026)
- Tufte — Envisioning Information (1990), ch. 2 "Micro/Macro Readings"
- NN/g — Table of Contents Best Practices (2023)
- WCAG 2.2 — §1.4.3 contrast, §2.4.5 multiple ways
- Wattenberger — Why React isn't great for actually building websites
- See
references/for full citations
04 同仓库更多 Skills
syllabus
Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.
pulse
Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.
patent
Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search / freedom-to-operate / competitive landscape / acquisition diligence / litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.
notebooklm
Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio/Video Overviews, Mind Maps, Reports incl. Briefing Doc/Study Guide/FAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.
litreview
Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER / Decomposition / hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5/10/20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed/OpenAlex (or Consensus) query.
grants
NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.