Overview
What it does
Compiles a structured brand, products, claims, and winners context pack any external AI tool can consume so its output is never generic.
When to use it
Any time you (or your team) are about to use an external AI tool — Claude, ChatGPT, a video generator — and you want its output grounded in your actual brand, products, claims, and winners instead of generic guesses. Compile once, feed everywhere, stop re-explaining your brand in every new thread.
Pairs with MCP
This is the moat at the protocol layer — Uplifted compiles a deterministic context pack from the graph and serves it over MCP, so every external tool instantly knows your business.
Best for
Marketers, creative ops, AI-tool power users.
Install
Drop this entire block into a SKILL.md file inside your Claude project’s .claude/skills/brand-context-pack/ folder. Claude auto-invokes it when you ask to ground another AI tool in your brand or to compile a context pack.
---
name: brand-context-pack
description: Use this skill to compile a deterministic, structured context pack — brand, products, approved claims, current winners, tone — that any external AI tool can consume via MCP so its output is grounded in the business, never generic.
---
# Brand Context Pack for AI
You compile the brand's verified context into a pack any AI tool can consume as ground truth.
## Data you need
- From Uplifted via MCP: brand voice/tone rules, product/SKU catalog, approved claims (with provenance), current winning patterns, key audiences, and visual/lockup rules
- The target tool and use case (so the pack is scoped — a video generator needs different context than a copy tool)
## How to compile
1. Pull the verified essentials: who the brand is, what it sells, what it's allowed to claim, what's currently working, and what it must not do.
2. Keep it deterministic and structured (clear fields), not prose — so the consuming tool can't drift or hallucinate around it.
3. Scope to the use case: include only the context the target tool needs, plus the hard guardrails it must respect.
4. Attach provenance/confidence so the downstream tool (and the user) can trust each element.
5. Output both a human-readable summary and a machine-readable block.
## Output format
BRAND CONTEXT PACK — [Brand] · for [target tool / use case]
SUMMARY (human-readable) — the brand in a paragraph, the do's, the don'ts
STRUCTURED BLOCK (machine-readable) — fields: brand, voice, products[], approved_claims[] (with source), current_winners[] (with metric), audiences[], guardrails[] (must / must-not)
PROVENANCE — where each element comes from + confidence
HOW TO USE — one line on feeding this to the target tool
## Guidelines
- Deterministic and structured beats eloquent — the point is to prevent drift, not to read nicely.
- Only include verified elements; anything unverified is labeled as such or left out.
- Scope tightly to the use case; a bloated pack is as useless as no pack.Prompt
Compiles a structured pack of your brand, products, claims, and winners that any external AI tool can ingest so its output is never generic. Paste it in before prompting another tool to keep results on-brand.
Compile a brand context pack I can feed into another AI tool so its output is grounded in my business.
Data from Uplifted via MCP: brand voice/tone, product catalog, approved claims (with provenance), current winning patterns, key audiences, lockup rules. Target tool / use case: {{e.g., ChatGPT for ad copy / a video generator}}. {{confirm MCP}}
1. Pull the verified essentials (who the brand is, what it sells, what it can claim, what's working, what it must not do).
2. Keep it deterministic and structured, not prose.
3. Scope to the use case + the hard guardrails.
4. Attach provenance/confidence.
5. Output a human-readable summary AND a machine-readable block.
Output: SUMMARY, STRUCTURED BLOCK (brand, voice, products[], approved_claims[] with source, current_winners[] with metric, audiences[], guardrails[]), PROVENANCE, and a one-line "how to use."
Structured beats eloquent. Only verified elements. Scope tightly.
EXPECTED OUTPUT:
- A scoped, structured context pack any AI tool can consume as ground truth
- Provenance on every element so it's trustworthy
- A human summary plus a machine-readable block