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 adologyai/content-intelligence-plugin --skill brand-buildergit clone --depth 1 https://github.com/adologyai/content-intelligence-pluginWrote 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/adologyai/content-intelligence-plugin/brand-builder)<a href="https://agentmods.dev/skills/adologyai/content-intelligence-plugin/brand-builder"><img src="https://agentmods.dev/badge/skills/adologyai/content-intelligence-plugin/brand-builder/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/adologyai/content-intelligence-plugin/brand-builder"><img src="https://agentmods.dev/badge/skills/adologyai/content-intelligence-plugin/brand-builder.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 | $0.00082 | $0.01677 |
| Opus 5.5 | $0.00033 | $0.00671 |
| Sonnet 5.5 | $0.00016 | $0.00335 |
| Haiku 4.5 | $0.00008 | $0.00168 |
Grade A, and why
brand-builder 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 2mo 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building a Tracked Universe
Two layers, and keeping them straight is most of the job.
A portfolio declares what a brand tracks — the focal brand, competitors, influencers, searches, discussions. You edit it with author_portfolio_context.
A project is the working scope analysis reads from. A fresh project reads the portfolio's whole tracked universe; update_project_scope narrows or grows it. You analyze in projects, never in the portfolio directly.
Step 1: Resolve the brands
lookup_brands({ query }) searches the central brand directory and returns each match's category, website, and whatever handles are on file — Instagram, TikTok, YouTube, X, LinkedIn, and the Meta Ad Library page id — already normalized to the shape author_portfolio_context accepts. A brand may have only one or two channels on file.
It costs nothing and changes nothing, so run it for every name the user gives you, in parallel. When a brand is not in the directory, ask the user for the handles directly; explicit handles work exactly as well as directory ones.
Step 2: Land in a portfolio
list_portfolios first — the brand may already have a workspace. create_portfolio({ name, websiteUrl }) starts a new one; if a portfolio with that name exists it returns the existing id with reused: true, so never call it twice for the same brand.
Step 3: Author the tracked universe
Read before you write: read_portfolio_context({ portfolioId }) returns the current items and brand details. Then author_portfolio_context merge-writes — upsertItems (matched by id), removeItemIds, and a shallow details patch (category, industry, targetAudience, brandVoice).
Every item needs an id and a kind, and kind is one bare token: brand, influencer, search, discussion, trend-term, niche, or seo. Everything else about the item is its own field.
- A brand also needs
role—ownfor the focal brand,competitor,adjacent, orinspiration— plus ahandlesobject keyed by platform (instagram,tiktok,facebook,youtube,adLibrary), or{}when you found none. - An influencer carries the same
handlesobject. - A search is a keyword; a discussion is a subreddit name without the
r/prefix.
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.
- 2mo ago First seen · 96 lines · 82 tokens per session scan A 28599b9211a8
brand-builder is a skill published in the GitHub repository adologyai/content-intelligence-plugin (2 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 82 tokens to every session and 1,677 once invoked, about $0.0003 per session on Opus 5.5. 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.
brand-builder compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| brand-builderadologyai/content-intelligence-plugin | Skill | 2 | 1mo ago | Apache-2.0 | A | 82 | Claude Code |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | today | MIT | A | 200 | Claude Code, Codex |
| playwright-climicrosoft/playwright | Skill | 99k | today | Apache-2.0 | A | 19 | Claude Code |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| chat-pet-sprite-creationmicrosoft/vscode | Skill | 195k | today | MIT | A | 53 | Claude Code, Codex |