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 alebgl77/claude-inc --skill customer-researchgit clone --depth 1 https://github.com/alebgl77/claude-incWrote 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/alebgl77/claude-inc/customer-research)<a href="https://agentmods.dev/skills/alebgl77/claude-inc/customer-research"><img src="https://agentmods.dev/badge/skills/alebgl77/claude-inc/customer-research.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.00699 |
| Opus 5 | $0.00039 | $0.00349 |
| Sonnet 5 | $0.00015 | $0.00140 |
| Haiku 4.5 | $0.00008 | $0.00070 |
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 8d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Research — Voice of Customer
"Synthesise user voice"
When to use
- "Here are 200 reviews — what do customers actually care about?"
- "Synthesize these interview notes / support tickets"
- "What objections keep coming up?"
- Before positioning, landing copy, or ads — copy written without voice-of-customer is guessing
- Works on pasted text or files; optional upgrade: web search to mine public reviews and Reddit threads
Workflow
- Ingest the corpus: pasted text, CSV/exports, or files. Note the source mix and any sampling bias (e.g. only angry customers write tickets).
- Extract verbatims into four buckets: pains, desires, objections, buying triggers. Keep the customer's exact words — never paraphrase at this stage.
- Cluster into themes per bucket; count frequency so loud-but-rare doesn't beat quiet-but-common.
- Write JTBD statements for the top clusters: "When {situation}, I want to {motivation}, so I can {outcome}."
- Build the message-market map: their words → your copy blocks (headline candidates, bullet candidates, objection-handling lines) — quoted or lightly compressed, never marketing-speak.
- Flag the gaps: pains competitors' messaging ignores, plus anything surprising that contradicts current positioning.
- Hand off: name the top theme a copywriter should lead with, and the one objection every asset must answer.
Output format
## Voice of Customer — {corpus, n items, sources}
### Themes by frequency
| Bucket | Theme | Freq | Best verbatim |
|--------|-------|------|---------------|
### JTBD (top 3)
1. When ..., I want to ..., so I can ...
### Message-market map
| They say (verbatim) | Use it as |
|---------------------|-----------|
| "..." | Headline / bullet / objection-handler |
### Gaps & surprises
- ...
### Handoff
Lead with: {theme}. Must answer: {objection}.
Quality bar
- Every theme backed by ≥ 2 verbatims, quoted exactly
- Frequencies counted, not vibed
- Sampling bias of the corpus stated up front
- JTBD statements contain a real situation, not a demographic
- Map entries are usable copy blocks, not categories
- At least one finding that challenges the current positioning (or explicit "none found")
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.
- 8d ago First seen · 65 lines · 77 tokens per session scan A 85e27a83ffc5
customer-research is a skill published in the GitHub repository alebgl77/claude-inc (14 stars, last pushed 5d ago), licensed MIT. It adds 77 tokens to every session and 699 once invoked, about $0.0004 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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