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 stanislavnianko/product-discovery-claude-skills --skill support-data-analysisgit clone --depth 1 https://github.com/stanislavnianko/product-discovery-claude-skillsWrote 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/stanislavnianko/product-discovery-claude-skills/support-data-analysis)<a href="https://agentmods.dev/skills/stanislavnianko/product-discovery-claude-skills/support-data-analysis"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/support-data-analysis/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/stanislavnianko/product-discovery-claude-skills/support-data-analysis"><img src="https://agentmods.dev/badge/skills/stanislavnianko/product-discovery-claude-skills/support-data-analysis.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 | $0.00063 | $0.01096 |
| Opus 5 | $0.00032 | $0.00548 |
| Sonnet 5 | $0.00013 | $0.00219 |
| Haiku 4.5 | $0.00006 | $0.00110 |
Grade A, and why
support-data-analysis 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Support Data Analysis
Part of the discovery-phase skill pack ·
evidencegroup · readsdiscovery-context.md(runprofile-builderfirst if missing).
The most underused evidence source in outsourcing discovery. Users complain in tickets, chats, and NPS comments without any selection bias from interview recruitment. The data already exists — just nobody read it systematically.
Step 1 — Read discovery context
Read discovery-context.md (sections 4. Access & Data → Data we can request, 6. Constraints — PII, regulatory). If no data sources are listed in section 4, ask the BA which data the client could share (tickets / NPS / chat / call recordings / analytics) and update the context file.
If discovery-context.md is missing, ask the BA inline: "(a) which data sources are realistically available (tickets / NPS / chat / calls / analytics / none); (b) any regulatory constraints (HIPAA / GDPR / SOC2 / none)?" — tag the output [ASSUMED ACCESS]. Never block; recommend profile-builder for high-stakes work.
Step 2 — Request data from client
Send the BA a templated ask to forward to the client SME:
"We'd like to ground discovery in real user signal. Could you share:
- Support tickets from the last 90 days mentioning
<keywords related to problem>(anonymized PII OK)- NPS verbatims from the last 6 months (the comments, not the scores)
- 5-10 sales call recordings or transcripts where this problem came up
- Product analytics for the relevant flow (event funnel, drop-off rates)
Send what you have; don't gate on completeness."
Step 3 — Pre-clean PII
Before analysis, scan for and redact: emails, phone numbers, full names, account IDs, credit card fragments. Use a script if volume is high. Note in the artifact that PII was redacted.
If regulatory constraints (GDPR, HIPAA) prohibit handling raw data, ask the client to do the redaction before sending. Document the chain-of-custody.
Step 4 — Code the data
For each data source, tag entries with:
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.
- 11d ago First seen · 92 lines · 63 tokens per session scan A eb26f3a76797
support-data-analysis is a skill published in the GitHub repository stanislavnianko/product-discovery-claude-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 1,096 once invoked, about $0.0003 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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