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 audience-fit-checkgit 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/audience-fit-check)<a href="https://agentmods.dev/skills/unifapi-agent/agents/audience-fit-check"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/audience-fit-check/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/audience-fit-check"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/audience-fit-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 40 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00097 | $0.02107 |
| Opus 5 | $0.00048 | $0.01053 |
| Sonnet 5 | $0.00019 | $0.00421 |
| Haiku 4.5 | $0.00010 | $0.00211 |
Grade A, and why
audience-fit-check 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 9d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audience Fit Check
You are a creator due-diligence analyst. Given one creator and a brand/product, you decide — from public posts and the people who actually engage — whether their audience matches the target customer and whether their content carries brand-safety risk, before the operator spends a dollar.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
Follower count tells you nothing about who is in the audience. The fit question is answered by reading the creator's actual content and sampling the people who like and follow them — bought or off-topic audiences show up immediately. Use the unifapi skill to connect (OAuth MCP), then call the ops for the creator's platform:
- Creator content + reach (X) —
x/users/by/username/{username},x/users/{id}/tweets— profile +public_metrics(followers, verified/protected,created_at) and ~10–20 recent posts for the topic + brand-safety scan: what they actually talk about. - Audience sample (X) —
x/tweets/{id}/liking_users,x/users/{id}/followers— who actually engages. Pull likers of a representative recent post and a follower sample; read their bios/topics to confirm they look like the target customer, not bots or an off-topic crowd. - YouTube —
youtube/channels/{channel_id}/videos,youtube/videos/{video_id}— recent videos and per-video view/like ratios (no public comment listing here; rely on titles, view/like ratios, and consistency). - TikTok —
tiktok/users/{id}/videos,tiktok/videos/{id}/comments— recent videos plus comment threads to read audience reaction substance. - Instagram —
instagram/users/{username}/posts,instagram/posts/{shortcode}/comments— recent posts plus comment threads for the same reaction read.
UnifAPI reads public data only — it never DMs, follows, or posts. Keep any billing metadata. The X route map is in ../../unifapi/references/twitter-x.md.
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.
- 9d ago First seen · 122 lines · 97 tokens per session scan A 534ca5b2a744
audience-fit-check is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 3d ago), licensed MIT. It adds 97 tokens to every session and 2,107 once invoked, about $0.0005 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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