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Generate structured wiki documentation from brand campaign outputs and business artifacts using parallel agent dispatch.
Execution Context
This skill runs AFTER the brandmint pipeline completes, not as part of it.
It reads JSON outputs from .brandmint/outputs/ and generates wiki markdown.
Always use parallel agents (Task tool) for document generation — never generate pages sequentially.
If running from bm launch, ensure all waves have completed first.
Wiki Documentation Generator
Transform campaign outputs and business documents into structured, interconnected wiki documentation using parallel agent dispatch for maximum efficiency.
Workflow Overview
Phase 1: Inventory → Scan input documents, classify by type
Phase 2: Dispatch Agents → Parallel processing of document categories
Phase 3: Cross-link → Build navigation and internal references
Phase 4: Output → Generate wiki-ready markdown structure
Phase 1: Inventory Source Documents
Scan the input location for source documents. Classify each by type:
This produces wiki-asset-map.json which maps each visual asset to its target wiki page, section, and role (hero, inline, gallery, or meta). Pass this mapping to agents in Phase 2 so they embed images in the correct locations.
Phase 2: Dispatch Parallel Agents
Each document category becomes an independent agent task. Process 3-5 categories in parallel using the dispatching-parallel-agents pattern.
Outputs: brand/visual-assets.md — a gallery page listing ALL visual assets organized by category
Schema: See references/doc-schemas.md → Visual Asset Library
Agent Task Template
## Task: Generate {Category} Wiki Documentation**Input Sources:**
{List of source files with paths}
**Output Requirements:**1. Extract key information following the schema in references/doc-schemas.md
2. Generate markdown files with proper frontmatter
3. Include cross-references to related documents (use relative paths)
4. Maintain heading hierarchy (H1 = page title, H2 = sections)
5. If `wiki-asset-map.json` is available, embed images for your pages using `` syntax. Place hero images after the H1. Place inline images within their mapped sections.
**Output Location:** /home/claude/wiki-output/{category}/
**Quality Checklist:**- [ ] Source content synthesized (no copy-paste dumps)
- [ ] Frontmatter includes: title, description, category, tags
- [ ] Internal links use relative paths
- [ ] Code/technical content in fenced blocks
Return: Summary of generated files and any content gaps found.
Phase 3: Cross-Link and Structure
After agents return, build navigation and cross-references.
---title:"Page Title"description:"One-line description for SEO and navigation"category:"product|brand|audience|marketing|project"tags: ["relevant", "keywords"]
sources: ["mds.md", "positioning.md"]
lastUpdated:"2025-01-24"---
Scenario-Based Depth
Adapt output based on project context:
Scenario
Pages
Sections Included
Brand Genesis ($3K)
5-8
Foundation + Brand basics
Crowdfunding Lean
12-15
+ Personas + Marketing basics
Crowdfunding Full
20-25
+ Full marketing + Project
DTC Launch
15-18
All sections, moderate depth
Enterprise GTM
25-30
All sections, maximum depth
Integration with Astro
After generating wiki docs, build with markdown-to-astro-wiki:
# Wiki output location
/home/claude/wiki-output/
# Initialize Astro project
./scripts/init-astro-wiki.sh project-wiki
# Process markdown WITH images (if generated/ directory exists)
./scripts/process-markdown.sh /home/claude/wiki-output ./project-wiki/src/content/docs --images /path/to/brand/generated/
# Buildcd project-wiki && bun run build
The --images flag copies all visual assets (PNG, JPG, WebP) from the generated directory into public/images/ so that  references in markdown resolve correctly.
Error Handling
Missing sources: Generate placeholder with TODO markers
Conflicting info: Flag in output, cite both sources
Incomplete sources: Extract available content, note gaps
Resources
references/doc-schemas.md - Detailed schemas for each wiki page type
references/frontmatter-templates.md - Frontmatter examples by category
scripts/inventory-sources.py - Scan and classify input documents (text + visual)
scripts/map-assets-to-wiki.py - Map visual assets to wiki pages
scripts/validate-wiki.py - Check output structure and links