audience-fit-check

audience-fit-check is a skill for Claude Code from unifapi-agent/agents. It costs 97 tokens per session (2,107 once invoked), scanned A, original, MIT.

A research tool for checking whether one creator’s audience matches a brand or product and whether the creator presents brand-safety risks. It examines public content and people who engage with it, not just follower totals.

In plain words
What is it for?
Use it to vet influencers and other creators, compare their audience with target customers, review public content, and perform checks before outreach or paid partnerships.
Why use it?
Follower counts do not show whether an audience is relevant or genuine. This helps reduce the risk of working with a creator whose audience is unsuitable or whose content could harm the brand.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the unifapi plugin — 47 skills, 1 MCP server shipped together

Good fit Use it to vet influencers and other creators, compare their audience with target customers, review public content, and perform checks before outreach or paid partnerships.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/unifapi-agent/agents/audience-fit-check
Install

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.

Any agent
npx skills add unifapi-agent/agents --skill audience-fit-check
Clone the repo
git clone --depth 1 https://github.com/unifapi-agent/agents

Made for: Claude Code.

Or install unifapi, the plugin that ships this one along with the rest of its 47 skills, 1 MCP server.

Wrote 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.

agentmods badge for audience-fit-check

README.md
[![agentmods](https://agentmods.dev/badge/skills/unifapi-agent/agents/audience-fit-check/github.svg)](https://agentmods.dev/skills/unifapi-agent/agents/audience-fit-check)
Your own site
<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.

agentmods 80×15 button for audience-fit-check

Your own site · 80×15
<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>
Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,107 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 534ca5b2a744, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

skills/influencer-marketing-agent/audience-fit-check/SKILL.md · 122 lines

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.

Read the full file on GitHub · 122 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 122 lines · 97 tokens per session scan A 534ca5b2a744

Subscribe to this mod's changes

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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