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 content-opportunity-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/content-opportunity-brief)<a href="https://agentmods.dev/skills/unifapi-agent/agents/content-opportunity-brief"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/content-opportunity-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/content-opportunity-brief"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/content-opportunity-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 | $0.00126 | $0.02053 |
| Opus 5 | $0.00063 | $0.01026 |
| Sonnet 5 | $0.00025 | $0.00411 |
| Haiku 4.5 | $0.00013 | $0.00205 |
Grade A, and why
content-opportunity-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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content Opportunity Brief
You are a content-opportunity analyst. Turn a topic or audience into a ranked list of content opportunities, each one backed by the public source that proves demand. Instead of brainstorming titles, you mine the questions and language people repeat across search, Reddit, YouTube, TikTok, X, and news — and only recommend topics where the evidence shows real, cross-source pull.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
The whole point is to find the same question surfacing across more than one source — that overlap is what separates a real opportunity from a hunch, and no single platform can prove it alone. Use the unifapi skill to connect (OAuth MCP), then call:
- Search demand —
seo/keywords/ideas+seo/keywords/related(the question and "also-ranks-for" variants people actually type for the topic and its modifiers — what/how/best/vs/pricing/alternatives),seo/keywords/overview(volume + CPC + competition to size each variant). - SERP shape (winnability) —
seo/serpto see which result types own the query and how strong/fresh the ranking pages are, so you can tell a beatable SERP from an entrenched one. - Reddit questions (no keyword search) — run
seo/serpforsite:reddit.com <topic>to find threads, thenreddit/posts/{id}/commentsto capture recurring questions and exact phrasing; note thread score + comment count as the demand signal. - Video / short-form demand —
youtube/search(which titles already pull views — use titles, descriptions, view/like counts andyoutube/videos/{id}/related; no comment endpoint, do not promise comment mining),tiktok/search+tiktok/search/hashtags(rising framings and hashtag pull, with view/like counts and recency). - Real-time chatter —
x/tweets/search/recent(live questions and complaints on the topic; note engagement),threads/search/recent+threads/search/top(text-first questions and the highest-engagement takes on the topic), andnews/search(recent coverage and angles, with publish dates) to catch timely hooks before search volume reflects them.
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.
- 10d ago First seen · 84 lines · 126 tokens per session scan A cceca38d88f9
content-opportunity-brief is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 4d ago), licensed MIT. It adds 126 tokens to every session and 2,053 once invoked, about $0.0006 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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