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 event4u-app/agent-config --skill customer-researchgit clone --depth 1 https://github.com/event4u-app/agent-configWrote 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/event4u-app/agent-config/customer-research)<a href="https://agentmods.dev/skills/event4u-app/agent-config/customer-research"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/customer-research/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/event4u-app/agent-config/customer-research"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/customer-research.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00048 | $0.01611 |
| Opus 5 | $0.00024 | $0.00805 |
| Sonnet 5 | $0.00010 | $0.00322 |
| Haiku 4.5 | $0.00005 | $0.00161 |
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 7d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
customer-research
When to use
- A backlog item is fuzzy because no one has talked to a current user about the underlying job in the last quarter.
- A churn or refund spike needs a switch-event explanation, not a feature gap list.
- A product owner is about to write AC for a feature that has not been validated against a real user job.
Do NOT use for quantitative funnel diagnosis (see funnel-analysis),
RICE-style ranking (see rice-prioritization), or surveying at scale —
this skill is about depth-5-to-10 interviews, not statistics.
Cognition cluster
- Mental model 2 — Jobs-to-be-Done. Frames every question against
the switch event: what caused the user to fire the previous
solution? See
docs/contracts/mental-models.md§ 2. - Mental model 3 — Pareto principle. A research week that
produces 12 distinct insights is usually re-discovering the same
three. See
mental-models.md§ 3.
Procedure
Step 0: Frame the job
- Write one sentence: "Users hire <thing> to make progress in <situation>, when motivated by <pressure>, expecting <outcome>." If you cannot finish the sentence, the discovery slice is not yet shaped — stop and route to
po-discovery. - Read the product slot of the context-spine (if the consumer project has filled it) for bounded scope, and the team slot for the senior PO / researcher handoff target. Skip if absent — note in the brief.
- Identify one competing solution the user might fire. Multiple competitors per session blurs the switch event.
Step 1: Recruit the right 5–8
- Recruit switchers (joined in last 60 days) and leavers (cancelled in last 60 days). Long-tenure power-users go in a separate bucket — they explain habits, not jobs.
- 5 minimum, 8 maximum. Saturation hits around 6 in a tightly scoped job — the Pareto cut. Beyond 8 is research theatre.
- Avoid friends, employees, beta-program over-talkers — selection bias is the failure mode that survives the AC.
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.
- 7d ago First seen · 126 lines · 48 tokens per session scan A 3ca9f5c375c5
customer-research is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 48 tokens to every session and 1,611 once invoked, about $0.0002 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 2026-09-03.
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