htmlify-v2

A tool that turns notes, Markdown, plans, specifications, or other readable content into a complete HTML document that opens in a web browser.

In plain words
What is it for?
Use it to create reports, project plans, specifications, findings, or other polished documents from files or conversation text.
Why use it?
It saves you from formatting raw content by hand and gives you a document that can be shared or printed to PDF without extra software.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/jakkaj/tools/htmlify-v2
Any agent
npx skills add jakkaj/tools --skill htmlify-v2
Clone the repo
git clone --depth 1 https://github.com/jakkaj/tools

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,228 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00038 $0.04228
Opus 5 $0.00019 $0.02114
Sonnet 5 $0.00008 $0.00846
Haiku 4.5 $0.00004 $0.00423

Measured 2d ago against content hash e9e27225f200, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

htmlify-v2 scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/SDD/htmlify-v2/SKILL.md · 414 lines

How it starts

The opening of the file, as written. The whole thing — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Please deep think / ultrathink as this is a complex task.

htmlify-v2

Static HTML document generator — takes any content the user points to (markdown files, notes, specs, plans, conversation context) and produces a polished, self-contained HTML file. Zero dependencies, opens directly in a browser, ready to share or print to PDF.

User input:

$ARGUMENTS

Expected usage patterns:
```bash
/htmlify <source>                           # HTMLify a file or path
/htmlify <source> --output path/to/out.html # Specify output path
/htmlify                                    # HTMLify from conversation context
/htmlify --dark                             # Use dark variant
```

Where `<source>` can be:
- A file path (markdown, text, any readable file)
- A directory (concatenates relevant files)
- Omitted — uses the current conversation context or the user's description

Purpose

Produce beautiful, zero-dependency static HTML documents from raw content. The output should look like a polished report you'd share with stakeholders — not a raw markdown render. Think Notion page or Linear changelog — clean, readable, professional.

Flow

1) Parse Arguments

  • Determine the source content (file path, directory, or conversation context)
  • Determine the output path (default: same directory as source, with .html extension; for context-based, use ./scratch/ or current directory)
  • Determine theme (default: light; --dark for dark variant)

2) Load & Analyse Source Content

  • Read the source content fully
  • Identify the document structure:
    • Title and subtitle
    • Sections and their hierarchy
    • Tables, lists, code blocks
    • Diagrams (mermaid candidates)
    • Key findings, recommendations, callouts
    • Metrics or summary data
  • Determine which components best represent each content element

3) Generate HTML

Rules:

  • Use semantic HTML throughout — real HTML elements, NOT markdown rendered at runtime
  • First line of the output file MUST be: <!-- Generated by htmlify-v2 -->
  • All CSS is inlined in a <style> block — single self-contained file
  • Select appropriate accent colours from the palette to visually distinguish sections
  • Use callout variants to match tone (see palette below)
  • Use tables for structured comparisons — don't force everything into cards
  • Add anchor navigation bar if the document has 3+ sections
  • Include mermaid diagrams where content describes flows, architectures, or relationships
  • Add mermaid.js CDN script ONLY if mermaid diagrams are present
  • Preserve the substance and detail of the source — don't summarise or truncate

Read the full file on GitHub · 414 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 414 lines · 38 tokens per session scan A e9e27225f200

Subscribe to this mod's changes

htmlify-v2 is a skill published in the GitHub repository jakkaj/tools (5 stars, last pushed 16d ago), licensed MIT. It adds 38 tokens to every session and 4,228 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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