Brand artifact

Brand Artifact Skill

Synthesizes research files into structured brand artifacts that drive the content engine. The coach persona — used for angle validation — is always derived from community research, never hardcoded.

Commands

# Generate brand docs from existing research
bun scripts/brand-artifact.ts init <slug>

# Propose updates from new research (human reviews diffs)
bun scripts/brand-artifact.ts update <slug> --from <research-file>

# Display artifact(s)
bun scripts/brand-artifact.ts show <slug>
bun scripts/brand-artifact.ts show <slug> community-profile

# Extract coach persona for a platform (for content angle loop)
bun scripts/brand-artifact.ts coach <slug> <platform>

# List all brands + artifact status
bun scripts/brand-artifact.ts list

Artifact Files

File	Generated by	Updated by	Purpose
community-profile.md	LLM (from research)	Research updates	Per-platform audience + coach personas
voice-guide.md	LLM (from research)	Performance data	Tone, examples, anti-examples
competitive-context.md	LLM (from research)	Research updates	Honest positioning per competitor
product-state.md	Template	Manual only	What's live, pricing, accuracy checklist
performance-log.md	Template	After each publish	What landed, what didn't
research-refs.md	Template	After each update	Research source index

The Core Principle

Coach personas derive from research, never from assumptions.

community-profile.md contains one coach persona per platform. Each persona:

• Cites which research file confirmed its patterns
• Distinguishes [RESEARCH-CONFIRMED] from [INFERRED]
• Sounds like a real community member, not a marketing brief

The content angle loop extracts this persona via coach <slug> <platform> and injects it into the validation agent.

The Evolution Loop

New research runs
      ↓
bun scripts/brand-artifact.ts update <slug> --from <file>
      ↓
Proposed diffs written as *.proposed-update.md
      ↓
Human reviews → applies changes → deletes proposed file
      ↓
research-refs.md updated with new source

After content publishes:

Performance data collected (upvotes, engagement, SEO ranking)
      ↓
Manual entry in performance-log.md
      ↓
What worked → promote to voice-guide.md positive examples
What flopped → add to voice-guide.md anti-examples
Persistent patterns → update community-profile.md coach persona

File Locations

Path	Purpose
scripts/brand-artifact.ts	Main CLI
docs/brand-artifacts/<slug>/	Brand artifact files
docs/brand-configs/<slug>.json	Brand config (source for init)
docs/market-research/	Research files (source for init + update)

What to Fill Manually

product-state.md is the one file the LLM cannot fill accurately. Before any content publishes, a human must verify:

• Features list matches the actual product
• Pricing / free tier is current and accurate
• Code examples in drafts actually run
• Any "coming soon" claims are removed

Related Skills

• brand-audit — runs before init (produces the audit data that feeds product-state.md)
• community-research — powers the research that feeds init and update (use your preferred search/discovery API, e.g. Reddit JSON API, Algolia, SerpAPI, or a search/discovery service of your choice)
• conversation-monitor — ongoing community monitoring that generates update inputs