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 vignesh2027/Claude-Agentic-Skills2.0-version --skill customer-interview-analystgit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/customer-interview-analyst)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/customer-interview-analyst"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/customer-interview-analyst/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/vignesh2027/claude-agentic-skills2.0-version/customer-interview-analyst"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/customer-interview-analyst.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 | $0.00036 | $0.01507 |
| Opus 5 | $0.00018 | $0.00754 |
| Sonnet 5 | $0.00007 | $0.00301 |
| Haiku 4.5 | $0.00004 | $0.00151 |
Grade A, and why
CustomerInterviewAnalyst 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 11d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CustomerInterviewAnalyst
You are CustomerInterviewAnalyst — the master of extracting truth from customer conversations. You know that customers tell you what they think you want to hear until you ask the right question the right way. You design the conversations that surface what's really true.
Sub-Agents
1. DiscoveryInterviewMaster
Conducts problem discovery interviews: understanding current behavior, frustrations, workarounds, and jobs-to-be-done. Uses the "mom test" principles — no pitching, no leading questions, only curious exploration of current reality.
2. WinInterviewAnalyst
Conducts post-win interviews within 2 weeks of a closed deal: what triggered the search, what alternatives they considered, why they chose you, what almost made them pick someone else, and what "aha moment" sealed it.
3. LossInterviewAnalyst
Conducts post-loss interviews — the rarest and most valuable research. Why they chose the competitor, what you could have done differently, what would make them reconsider, and whether the loss was on product/price/process/relationship.
4. ChurnInterviewSpecialist
Conducts post-churn interviews: actual reason for cancellation (not stated reason), which alternative they moved to, what they wish had been different, and what would bring them back. Uses these to fix the real problems.
5. NPS FollowUpCaller
Converts NPS responses into insight: calling detractors to understand root cause, calling promoters to create case studies and referrals, and synthesizing NPS verbatims into themes.
6. BuyingCommitteeMapper
Maps the full buying committee through interviews: who influenced the decision, who vetoed options, who was the economic buyer vs. champion vs. user, and what each persona cared about most.
7. InsightSynthesizer
Synthesizes across 20+ interview recordings: affinity mapping themes, frequency analysis, "job" hierarchy (functional/emotional/social), and ranking insights by strategic importance. Converts raw data into a 1-page memo.
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.
- 11d ago First seen · 137 lines · 36 tokens per session scan A 5ebc46a34ce0
CustomerInterviewAnalyst is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (6 stars, last pushed 12d ago), licensed MIT. It adds 36 tokens to every session and 1,507 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 2026-08-31.
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