social-listening
Skill7 starsChanged 5 months agoMITView on GitHub
Monitor what people say about a brand, product, person, or keyword across platforms — collect mentions, classify sentiment, cluster themes, and deliver a cited digest. Use when the user says "what are people saying about X", "monitor mentions", "brand sentiment", "track this keyword", or wants social listening.
- Reads credentials
7 other projects use a version of this.
Adopted by
Most recently taken up by
- DoddleHQ/doddle-social-media-skillstheir own version, first seen Nov 8, 2026
- Nooticr/nooticr-mcptheir own version, first seen Oct 21, 2026
- getcargohq/gtm-skillstheir own version, first seen Oct 18, 2026
- calven-ai/marketing-as-codetheir own version, first seen Oct 7, 2026
- Ootto-AI/claude-content-skillstheir own version, first seen Aug 23, 2026
- CodeAlive-AI/ceo-ai-ostheir own version, first seen May 18, 2026
Used with
Projects with it also use meeting-prep.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
OpenAISkillmeeting-prep
Use when creating Public Equity Investing meeting prep briefs. Do not use for private diligence, IB, FP&A, legal, or scheduling-only tasks.
Also in 3 projects that use this
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 7
- vivy-yi/xiaohongshu-skills★ 455same name
- Ootto-AI/claude-content-skills★ 417same name
- calven-ai/marketing-as-code★ 10same name
- CodeAlive-AI/ceo-ai-os★ 9same name
- getcargohq/gtm-skills★ 2same name
- DoddleHQ/doddle-social-media-skills★ 0same name
- Nooticr/nooticr-mcp★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository18 other files it gives its agents.
- bulk-data-exportSkill
- comments-analysisSkill
- competitor-analysisSkill
- creator-analyticsSkill
- douyinSkill
- hashtag-researchSkill
- instagramSkill
- social-media-downloaderSkill
- tikhub-endpoint-discoverySkill
- tikhub-mcpSkill
- tikhub-onboardingSkill
- tikhub-python-sdkSkill
- tikhub-rest-apiSkill
- tiktokSkill
- trend-researchSkill
- twitter-threadsSkill
- xiaohongshuSkill
- youtubeSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 6 edited versions we read keep these.
1
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 41
Every theme/claim in the digest cites a real source URL.
“Common” is measured: kept by at least 6 in 10 of the 6 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Social Listening
Collect mentions of a brand/keyword across platforms, then analyze sentiment and themes.
Setup gate
[ -z "${TIKHUB_API_KEY:-}" ] && echo "Set TIKHUB_API_KEY first (see tikhub-onboarding)."Workflow
- Define the query: brand/keyword(s), platforms to cover, time window, target volume (e.g. ~100 mentions).
- Search each platform using its search endpoint (one query → paginate to the target volume):
- TikTok
app/v3/fetch_video_search_result, Douyinsearch/fetch_general_search_v2(POST body), Instagramv2/general_search, Twitterweb/fetch_search_timeline, YouTubeweb_v2/get_general_search, Xiaohongshuapp_v2/search_notes. - Find exact paths via
tikhub-find-endpoint "search" --platform <slug>.
- TikTok
- Collect posts/comments into one list (author, text, platform, url, timestamp, engagement).
- Classify sentiment (positive / neutral / negative) per mention — reason over the text.
- Cluster themes (recurring topics, complaints, praise) and pull representative quotes.
- Deliver a digest: volume, sentiment breakdown, top themes with cited example posts (link each claim to a source URL), and 2–3 recommendations.
Cost awareness — IMPORTANT
This is the most call-heavy skill: every search page on every platform is a billed call.
State an estimated call count and cost before running
(GET /api/v1/tikhub/user/calculate_price?endpoint=<path>&request_per_day=<pages>), cap pages per
platform, and check balance with get_user_daily_usage.
Verification gate
- Mentions actually match the query (filter false positives).
- Every theme/claim in the digest cites a real source URL.
- Sentiment labels are justified by the quoted text.
Red flags
- Unbounded multi-platform pagination — runs up credits fast; always cap and warn.
- Reporting sentiment without citations.
- Counting unrelated keyword collisions as mentions.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.