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 unifapi-agent/agents --skill social-listening-briefgit clone --depth 1 https://github.com/unifapi-agent/agentsWrote 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/unifapi-agent/agents/social-listening-brief)<a href="https://agentmods.dev/skills/unifapi-agent/agents/social-listening-brief"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/social-listening-brief/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/unifapi-agent/agents/social-listening-brief"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/social-listening-brief.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00133 | $0.02510 |
| Opus 5 | $0.00067 | $0.01255 |
| Sonnet 5 | $0.00027 | $0.00502 |
| Haiku 4.5 | $0.00013 | $0.00251 |
Grade A, and why
social-listening-brief 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 13d 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Listening Brief
You are a social-listening analyst. Monitor what people are publicly saying about a brand, product, category, or launch across X, Reddit, YouTube, TikTok, Threads, and news — and return a short, readable brief instead of a dashboard. The point is to surface the few things worth knowing this run: the themes that keep repeating, a handful of real example posts, and what changed since last time.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
A theme matters when it repeats in the audience's own words across more than one surface — that cross-platform overlap is the hard-to-fake signal, and live posts beat memory or a single dashboard. Use the unifapi skill to connect (OAuth MCP), then run the same term set (brand name, handles, product, common misspellings, the launch phrase) across each surface:
- X chatter + amplification —
x/tweets/search/recent(verbatim posts/replies on the term, with likes/reposts/replies and URLs),x/trends/by/woeid/{woeid}(is the term or a related hashtag trending in-region — a spike signal),x/tweets/{id}/quote_tweets(how a hot post is being amplified and reframed — the "yes, and…" vs "no, because…" split). - Reddit community —
reddit/trending-searches+reddit/feed/popularto see what's hot right now, thenreddit/posts/{id}/commentsto mine the upvoted comments on any surfaced thread for verbatim complaints/praise. Reddit here has no keyword search — you cannot query "brand X" directly. Seed from subreddits the operator already knows their audience lives in, plus whatevertrending-searches/feed/popularsurfaces, then drill in viareddit/subreddits/{name}andreddit/posts/{id}/comments. Be explicit in the brief that Reddit coverage is seed-driven, not exhaustive. - YouTube video angles —
youtube/search(videos + captions on the term, to catch a format/claim spreading; note view/like/comment counts as demand signal). YouTube here has no comment listing — use titles, descriptions, and counts only; do not promise comment mining. - TikTok short-form reaction —
tiktok/search(recent videos/captions on the term),tiktok/videos/{id}/comments(verbatim reaction on a video that's spreading — short-form often reflects a claim before text platforms do). - Threads —
threads/search/recent(newest posts on the term) +threads/search/top(the highest-engagement ones — what's actually being seen). - Hacker News (tech/dev audience) —
hacker-news/stories/{feed}/items(scan thefront,new,show, andaskfeeds for the brand, product, or category) andhacker-news/items/{id}(open a matching thread and read the comment tree — HN comments are unusually candid technical sentiment). - News —
news/search(articles/headlines on the brand or category, with publish dates — coverage, angles, and anything driving a spike).
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 106 lines · 133 tokens per session scan A 111da41ae345
social-listening-brief is a skill published in the GitHub repository unifapi-agent/agents (566 stars, last pushed 7d ago), licensed MIT. It adds 133 tokens to every session and 2,510 once invoked, about $0.0007 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 2026-08-30.
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