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.
npx skills add S3YED/appie-kit --skill creator-brand-intelligencegit clone --depth 1 https://github.com/S3YED/appie-kitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/s3yed/appie-kit/creator-brand-intelligence)<a href="https://agentmods.dev/skills/s3yed/appie-kit/creator-brand-intelligence"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/creator-brand-intelligence/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/s3yed/appie-kit/creator-brand-intelligence"><img src="https://agentmods.dev/badge/skills/s3yed/appie-kit/creator-brand-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00096 | $0.01741 |
| Opus 5 | $0.00048 | $0.00870 |
| Sonnet 5 | $0.00019 | $0.00348 |
| Haiku 4.5 | $0.00010 | $0.00174 |
Grade A, and why
creator-brand-intelligence 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Creator Brand Intelligence
Research, audit, and build outreach for brand partnership opportunities tailored to a creator's niche, audience, and existing portfolio.
When to Activate
- User asks what brands they should work with or what categories they're missing
- User wants an outreach list with emails and contact methods
- User wants to know which brands do paid recurring deals in their niche
- User says "find me brands to partner with", "brand deal opportunities", "sponsorship list", "collab outreach"
- Auditing existing brand portfolio for gaps
- Researching specific brand categories (men's clothing, SaaS tools, fintech, etc.)
Workflow
Phase 1: Portfolio Audit
Start by mapping what the creator already has:
- Extract existing brand portfolio from whatever source is available (Cognify knowledge graph, memory, website, the user themselves)
- Categorize by industry — fashion, beauty, tech, travel, fitness, entertainment, etc.
- Identify the creator's core niches — what content do they make? Who is their audience? What's their personal brand?
- Map gaps — categories that fit their niche but have zero brand deals
| Question | Why it matters |
|---|---|
| What content do you create? | Determines which brands are a natural fit |
| Who is your audience? | Brands care about demo match |
| What's your vibe/aesthetic? | Premium, streetwear, business, casual? |
| Where do you post? | Some brands only do IG, others do YT or TikTok |
| Past deals (even organic)? | Shows what categories already work |
Phase 2: Category Discovery
For each gap category, identify sub-niches and brand candidates:
Common creator categories:
- SaaS & Creator Tools: Notion, Canva, Descript, Riverside, ConvertKit, Kajabi, Airtable, Adobe
- Fintech/Finance: Revolut, Wise, Stripe, PayPal, crypto platforms
- Travel (digital nomads): Airbnb, Booking.com, Skyscanner, travel insurance
- Camera & Gear: DJI, RØDE, Peak Design, Sony, Elgato
- Productivity/Desk Setup: Logitech, Secretlab, Herman Miller, monitors
- Health & Wellness: Calm, Headspace, Oura Ring, Eight Sleep, supplements
- AI Tools: ChatGPT, Midjourney, Opus Clip, Notion AI
- Watches & Accessories: Daniel Wellington, TAG Heuer, Tissot
- Coffee/Energy: Celsius, Nespresso, matcha brands
- Education Platforms: Kajabi, Teachable, Thinkific, Podia
- Men's Clothing: Represent, Carhartt WIP, AllSaints, Ralph Lauren, Tommy Hilfiger, Diesel, Fear of God, Kith, Aime Leon Dore, Uniqlo, Zara Men, Scotch & Soda, Paul Smith, Nordstrom
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.
- 9d ago First seen · 129 lines · 96 tokens per session scan A c8b737f88f33
creator-brand-intelligence is a skill published in the GitHub repository S3YED/appie-kit (9 stars, last pushed 17d ago), licensed MIT. It adds 96 tokens to every session and 1,741 once invoked, about $0.0005 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 2026-09-03.
Other skills, from other repositories
browser-edge-cases
SOP for debugging browser automation failures on complex websites. Use when browser tools fail on specific sites like LinkedIn, Twitter/X, SPAs, or sites with Shadow DOM.
aws-patterns
Lambda best practices, S3 event patterns, SQS/SNS fanout, and DynamoDB access patterns for serverless AWS architectures.
review
Review the changes since a fixed point (commit, branch, tag, or merge-base) along two axes — Standards (does the code follow this repo's documented coding standards?) and Spec (does the code match what the originating issue/PRD asked for?). Runs both reviews in parallel sub-agents and reports them side by side. Use…
agents-md-protocol
Create or review an AGENTS.md file so coding agents get stable repo-local instructions: environment setup, testing, style, security boundaries, PR policy, and handoff rules. Use when a repo lacks durable agent guidance or when a custom harness needs a predictable context file.
investment-memo-generator
Investment memo creation combining financial analysis, document generation, and structured templates. Use when creating investment memos, pitch decks, deal summaries, or investment committee materials.
python-memory-safe-scripts
Memory-safe Python script patterns for long-running processes under systemd MemoryMax constraints. Covers allocator purge (mimalloc/glibc malloctrim), HTTP response lifecycle, DataFrame cleanup, thread-local connection reuse, and periodic GC cadence. Battle-tested through 5 OOM optimization cycles on production GPU…