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From Adology — Content Intelligence
Guides knowledge set construction including feed management and brand discovery. Use when creating knowledge sets, adding or removing brands, influencers, search terms, or discussion feeds. Triggers on: "create knowledge set", "add brand", "add influencer", "track competitor", "monitor", "build a KS".
How this skill is triggered — by the user, by Claude, or both
Slash command
/content-intelligence:brand-builderThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Start by running `discover_brands` to find brands by name, category, or URL. This gives you a picture of the competitive landscape before building anything.
Start by running discover_brands to find brands by name, category, or URL. This gives you a picture of the competitive landscape before building anything.
After discovery, call whoami to check the user's available credits. Before calling trigger_fetch, estimate the cost based on the number of feeds being fetched. Inform the user of the expected credit usage before proceeding. Never trigger a fetch without the user understanding the cost.
Use create_knowledge_set with a descriptive name ("DTC Skincare Competitors", "Q2 Campaign Benchmarks", "Protein Brand Landscape").
When adding 3+ feeds, use batch_add_feeds instead of individual add_feed calls. This is faster and reduces round-trips. Only use individual add_feed for single additions or when you need to handle each feed's response separately.
User gives a brand name:
discover_brands to find it and resolve platform handleslist_knowledge_sets to see if the brand already exists in another KSUser gives a URL:
add_feed -- no discovery step neededUser gives a category (e.g., "energy drinks"):
discover_brands by category to get a list of brandsCall trigger_fetch to start scraping. This runs asynchronously via Temporal workflows. You can use the feedNames parameter to selectively refresh specific feeds instead of the entire Knowledge Set.
Check progress with get_workflow_status. Scraping typically takes a few minutes per feed depending on content volume.
After feeds are added, call get_suggestions to identify gaps in the Knowledge Set:
Review the suggestions with the user and add recommended feeds using batch_add_feeds.
The full sequence:
discover_brandswhoamicreate_knowledge_setbatch_add_feeds with all selected brands, creators, search terms, discussionstrigger_fetch (after confirming credit cost)get_workflow_status until completeget_suggestions to identify missing coveragebatch_add_feeds with suggestions, then trigger_fetch againTrack paid ads and organic social content. Sources: facebook-ad-library, tiktok-ad-library, instagram-profile, tiktok-profile, youtube-channel, facebook-page, twitter-profile, linkedin-profile, threads-profile.
Track a specific creator's content. Sources: instagram-profile, tiktok-profile, youtube-channel, twitter-profile, threads-profile.
Monitor keywords across platforms. Sources: tiktok-search, instagram-search, youtube-search.
Track Reddit subreddits. Provide the subreddit name without the r/ prefix.
Brand not found in discovery DB: Ask the user for the brand's social media handles directly. You can add feeds with explicit handles even if the brand is not in the discovery database.
Creator not in discovery database:
Creator discovery is not available via MCP. Ask the user for the creator's handle directly and add via add_feed with feedType: 'influencer'.
add_feed with invalid handle: The tool will return an error. Inform the user the handle could not be resolved. Ask them to verify the handle exists on the platform, check for typos, and try again.
npx claudepluginhub adologyai/content-intelligence-plugin --plugin content-intelligenceProvides UI/UX resources: 50+ styles, color palettes, font pairings, guidelines, charts for web/mobile across React, Next.js, Vue, Svelte, Tailwind, React Native, Flutter. Aids planning, building, reviewing interfaces.
Searches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.