CM DocKit: Knowledge Systematization Engine

A professional knowledge systematization engine powered by codebase analysis and UX design principles. One source scan = one complete knowledge base — Personas, JTBD, Process Flows, Technical Docs, SOPs, API Reference. Supports plain Markdown output or a premium VitePress site. Includes SEO optimization, sitemap generation, and AI/LLM-readable content.

When to Activate

• User asks to "create documentation", "generate docs"
• User mentions "SOP", "user guide", "manual"
• User wants technical docs from a codebase
• User runs

  /DocKit Master

Document Types

skills/persona-builder.md

skills/jtbd-analyzer.md

skills/flow-mapper.md

skills/tech-docs.md

skills/sop-guide.md

skills/api-reference.md

skills/seo-checklist.md

skills/content-writing.md

skills/llm-optimization.md

Output Formats

workflows/export-markdown.md

.md

docs/

workflows/setup-vitepress.md

Procedure

Step 1: Gather Input (Single Consolidated Prompt)

CRITICAL: Ask ALL questions in ONE message. Do NOT ask one at a time. Present the following intake form to the user, using this 📚 DocKit Master — Configuration

Please answer the following questions so I can automatically create an execution plan:

knowledge

tech

sop

api

all

all

markdown

vitepress

vitepress

full

focused

full

focused

yes

no

no

yes

no

no

yes

no

yes

yes

no

yes

You can answer briefly, e.g.: "all, vitepress, full, yes, no, yes, yes"

🌏 Smart language rules:

1. Auto-detect: Determine default language from the user's chat language
   • User chats in Vietnamese → default

     vi

   • User chats in Chinese → default

     zh

   • User chats in Japanese → default

     ja

   • User chats in English → default

     en

   • (Applies similarly for any other language)
2. Multi-language (

   yes

   ): Automatically add English (

   en

   ) as secondary language
   • Example: Vietnamese user + multi-language →

     vi

     +

     en

   • Example: Chinese user + multi-language →

     zh

     +

     en

   • If user already chats in English + multi-language → ask which secondary language
3. Override: User can override by specifying explicitly, e.g.: "write in Japanese"

Step 1b: Auto-Generate Execution Plan

After receiving answers, immediately create an execution plan (do NOT ask more questions).

Map the answers to this execution config:

DOC_TYPE     = [knowledge | tech | sop | api | all]
FORMAT       = [markdown | vitepress]
SCOPE        = [full | focused]
FOCUS_TARGET = [directory/module name if focused, else null]
LANGUAGE     = [vi | en | vi+en]
I18N         = [yes | no] (only relevant for vitepress)
RECORD       = [yes | no]
PROJECT_PATH = [absolute path]
SEO          = [yes | no] (default: yes)
LLM_OPTIMIZE = [yes | no] (default: yes)

Then present the plan to user as a checklist artifact, like:

## 🚀 Execution Plan

- [ ] Scan code: [full/focused → target]
- [ ] Generate documents: [type] in [language]
- [ ] Export format: [markdown/vitepress]
- [ ] [If vitepress + i18n] Configure multi-language
- [ ] [If record] Record video walkthrough
- [ ] [If seo] Run SEO checklist + generate sitemap
- [ ] [If llm_optimize] Apply LLM optimization rules
- [ ] Review and deliver

After presenting the plan → proceed to Step 2 immediately (auto-execute). Do NOT wait for approval unless the plan has ambiguity.

Step 2: Analyze Codebase

Read and follow

skills/analyze-codebase.md

Output: structured analysis saved to

docs/analysis.md

_analysis.md

• Project type, languages, frameworks
• Directory structure and architecture layers
• Entry points, routes, database schema
• Key business logic modules
• Dependencies overview
• Test coverage

Step 3: Apply Content Guidelines

MANDATORY — Read

skills/content-guidelines.md

Key rules to enforce:

• Filenames: kebab-case, no underscores, no dots
• Frontmatter: Every

  .md

  file must have

  title

  ,

  description

  ,

  keywords

  ,

  robots

• Quick Reference: Every doc starts with a summary box
• Progressive Disclosure: Use

  <details>

  for advanced content
• Admonitions: Use

  :::tip

  ,

  :::info

  ,

  :::warning

  ,

  :::danger

  for callouts
• Mermaid: NO hardcoded colors — VitePress auto-adapts to light/dark
• Code Groups: Use

  :::code-group

  for multi-platform examples
• Internal Links: ≥2 cross-links per page

Step 3b: Apply SEO & LLM Guidelines (If enabled)

If SEO = yes: Read

skills/content-writing.md

• Keyword placement (title, H1, first paragraph, H2s, meta)
• Inverted pyramid structure (answer first, details later)
• Active voice (≥80%), transition words (≥30%)
• FAQ in schema-ready format for rich snippets

If LLM_OPTIMIZE = yes: Read

skills/llm-optimization.md

• Clean heading hierarchy (no skipped levels)
• Text descriptions alongside all Mermaid diagrams
• Self-contained sections (≤500 words per H2)
• Consistent terminology (glossary section in index)
• UTF-8 clean output

Step 4: Generate Documents

Based on the chosen type, read and follow the corresponding skill file:

• knowledge → Run 3 skills sequentially:

  1. Read

     skills/persona-builder.md

     →

     docs/personas/

     (Buyer & User Personas)
  2. Read

     skills/jtbd-analyzer.md

     →

     docs/jtbd/

     (JTBD Canvases)
  3. Read

     skills/flow-mapper.md

     →

     docs/flows/

     (Workflow, Sequence, Lifecycle, Journey)

• tech → Read

  skills/tech-docs.md

  , generate:

  • docs/architecture.md

    — System architecture + ADR

  • docs/database.md

    — Database schema & data model

  • docs/deployment.md

    — Deployment & infrastructure

  • docs/data-flow.md

    — Data flow diagrams

• sop → Auto-run

  knowledge

  first if not yet generated, then:
  • Read

    skills/sop-guide.md

    , generate:

  • docs/sop/

    — One

    .md

    per feature/module
  • Each file: Persona Context → Process Flow → Steps → Journey → Troubleshooting → FAQ

• api → Read

  skills/api-reference.md

  , generate:

  • docs/api/

    — Organized by resource
  • Each file: Quick Ref → Endpoints table → Multi-language examples

• all → Run

  knowledge

  →

  tech

  →

  sop

  →

  api

  sequentially

Step 5: Export

Based on the chosen format, read and follow the corresponding workflow:

• markdown → Read

  workflows/export-markdown.md

  • Create

    docs/README.md

    as index
  • Organize into clean folder structure

• vitepress → Read

  workflows/setup-vitepress.md

  • Scaffold VitePress with premium template
  • Auto-sidebar from folder structure
  • Built-in Mermaid, search, dark mode
  • Build and verify

Step 5b: Generate Sitemap (If SEO = yes)

Read and follow

workflows/generate-sitemap.md

• VitePress: Sitemap auto-generated via

  sitemap

  config option. Generate

  robots.txt

  , extract

  sitemap-urls.txt

• Markdown: Generate

  docs/sitemap.md

  (link index) +

  docs/sitemap-urls.txt

• Both formats produce a NotebookLM-ready URL list for AI research

Step 5c: Run SEO Audit (If SEO = yes)

Read

skills/seo-checklist.md

• Title (50–60 chars, keyword) ✔️
• Meta description (150–160 chars) ✔️
• Single H1, no skipped levels ✔️
• ≥2 internal links ✔️
• Robots directive set ✔️
• All images have alt text ✔️

Step 6: Summary

Present to user:

• List of generated files with sizes
• How to view/serve the docs
• Next steps (customize, deploy, etc.)

If generated docs > 30 files, also suggest:

💡 **Pro Tip: Deep Search**

The documentation set just created has [X] files. You can index them using
[qmd](https://github.com/tobi/qmd) for semantic search by AI
across all future sessions:

\`\`\`bash
npm install -g @tobilu/qmd
qmd collection add ./docs --name project-docs
qmd context add qmd://project-docs "Project documentation for [project-name]"
qmd embed
\`\`\`

See also: `cm-deep-search` skill.

CLI Quick Start

For a fast interactive experience, users can run the doc generation script from the skill root:

# Run from the cm-dockit skill directory
bash scripts/doc-gen.sh

Note: The

scripts/

directory and

doc-gen.sh

script need to be created. For now, trigger this skill by invoking

cm-dockit

directly via the AI assistant.

UX Principles Applied

Constraints

• All Mermaid diagrams use NO hardcoded inline styles — VitePress theming handles light/dark
• Every technical claim cites

  (file_path:line_number)

• SOP docs use

  <details>

  for troubleshooting (progressive disclosure)
• All generated files include YAML frontmatter with

  title

  ,

  description

• Pure Markdown — no MDX, no special escaping needed
• No underscore-prefixed filenames — breaks auto-sidebar detection
• VitePress output must pass

  npx vitepress build

  without errors
• SEO default:

  robots: "index, follow"

  unless page is internal/draft
• ≥2 internal links per page (never orphan pages)
• Text fallback for every Mermaid diagram (LLM readability)
• Self-contained sections — each H2 makes sense read alone

• sitemap-urls.txt

  generated for NotebookLM import

CM DocKit Development Rules

If you are an AI agent asked to modify or upgrade this skill (CM DocKit):

1. Test Gate Enforcement: You MUST run the backend test suite located in the

   cm-dockit

   skill directory by executing

   $ npm run test:gate

   or

   $ vitest

   . Do NOT claim "task completed" unless tests pass.
2. Boilerplate Integrity: If modifying

   templates/vitepress-premium

   , ensure the frontend test suite (

   tests/frontend.test.ts

   ) still works.
3. No Direct Copying: Never hardcode file-copy commands that copy

   [project_root]/docs/

   content into

   docs-site/

   . Always rely on

   srcDir: '../docs'

   in

   config.mts

   .