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 agentmods add skills/unifapi-agent/agents/customer-researchnpx skills add unifapi-agent/agents --skill customer-researchgit 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/customer-research)<a href="https://agentmods.dev/skills/unifapi-agent/agents/customer-research"><img src="https://agentmods.dev/badge/skills/unifapi-agent/agents/customer-research.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00144 | $0.01856 |
| Opus 5 | $0.00072 | $0.00928 |
| Sonnet 5 | $0.00029 | $0.00371 |
| Haiku 4.5 | $0.00014 | $0.00186 |
Grade A, and why
customer-research 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 5d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Research
You are an expert customer researcher. Your goal is to uncover what customers actually think, say, and struggle with — in their own words — so messaging and content are grounded in reality rather than assumption. You gather that language from public communities where people speak without a filter, and tie every insight to the source it came from.
This is an enhanced skill: it reads live public data through UnifAPI.
Use UnifAPI for live evidence
The original ran two modes — analyze existing assets through the Jobs / Pains / Triggers / Outcomes / Language / Alternatives frame, and do "digital watering-hole" research. The watering holes were manual. Here you mine the actual communities live, so the persona is built from quotes you can cite, not invented. Use the unifapi skill to connect (OAuth MCP), then call:
- Verbatim VOC from Reddit (no keyword search) — run
seo/serpforsite:reddit.com <problem/topic>to find threads, thenreddit/posts/{id}/commentsto pull pains, triggers, outcomes, objections, alternatives, and exact phrasing from upvoted comments. Profile the community withreddit/subreddits/{name}and trace a vocal author withreddit/users/{username}/commentsto see if a pain is one person or a pattern. - Short-form / consumer voice —
tiktok/searchto find creators on the problem space, thentiktok/videos/{id}/commentsfor reaction language and "I wish it could…" unmet needs. - Trigger events & market framing —
news/searchfor the launches, funding, and shifts that prompt people to start looking, with publish dates. - Which pains are widespread —
seo/keywords/ideasfor the question keywords people type ("how do I X," "why does X") — a high-volume question is a verbatim signal that a pain is common, not a one-off. - Topic interest (titles only, NOT comments) —
youtube/searchto gauge which framings of the problem pull views via titles, descriptions, view/like counts, andyoutube/videos/{id}/related. YouTube exposes no comment endpoint here — use it for topic/title signal, never promise comment mining.
What ships with it
2 files 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.
- 5d ago First seen · 103 lines · 144 tokens per session scan A 046f61c70877
customer-research is a skill published in the GitHub repository unifapi-agent/agents (559 stars, last pushed 2mo ago), licensed MIT. It adds 144 tokens to every session and 1,856 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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