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 TommyBez/skillsboard --skill customer-researchgit clone --depth 1 https://github.com/TommyBez/skillsboardWrote 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/tommybez/skillsboard/customer-research)<a href="https://agentmods.dev/skills/tommybez/skillsboard/customer-research"><img src="https://agentmods.dev/badge/skills/tommybez/skillsboard/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/tommybez/skillsboard/customer-research"><img src="https://agentmods.dev/badge/skills/tommybez/skillsboard/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 | $0.00179 | $0.02820 |
| Opus 5.5 | $0.00072 | $0.01128 |
| Sonnet 5.5 | $0.00036 | $0.00564 |
| Haiku 4.5 | $0.00018 | $0.00282 |
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 1mo 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.
This is a copy
92% identical to customer-research — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 285 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 help uncover what customers actually think, feel, say, and struggle with — so that everything from positioning to product to copy is grounded in reality rather than assumption.
Before Starting
Check for product marketing context first:
If .agents/product-marketing.md exists (or .claude/product-marketing.md, or the legacy product-marketing-context.md filename, in older setups), read it before asking questions. Use that context to skip questions already answered.
Two Modes of Research
Mode 1: Analyze Existing Assets
You have raw research material (transcripts, surveys, reviews, tickets). Your job is to extract signal.
Mode 2: Go Find Research
You need to gather intel from online sources (Reddit, G2, forums, communities, review sites). Your job is to know where to look and what to extract.
Most engagements combine both. Establish which mode applies before proceeding.
Mode 1: Analyzing Existing Research Assets
Asset Types
Customer interview / sales call transcripts
- Extract: pains, triggers, desired outcomes, language used, objections, alternatives considered
- Look for: the moment they decided to look for a solution, what they tried before, what success looks like to them
Survey results
- Segment responses by customer tier, use case, or tenure before drawing conclusions
- Flag: what open-ended answers say vs. what multiple-choice answers say (they often conflict)
- Identify: the 20% of responses that contain the most useful signal
Customer support conversations
- Mine for: recurring complaints, confusion points, feature requests, and "I wish it could…" language
- Categorize tickets before analyzing — don't treat all tickets as equal signal
- Separate bugs from confusion from missing features from expectation mismatches
Win/loss interviews and churned customer notes
- Wins: what tipped the decision? What almost made them choose a competitor?
- Losses and churn: was it price, features, fit, timing, or something else?
- Segment by reason — don't average across different churn causes
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.
- 1mo ago First seen · 285 lines · 179 tokens per session scan A f44c116c4690
customer-research is a skill published in the GitHub repository TommyBez/skillsboard (9 stars, last pushed 20d ago), licensed MIT. It adds 179 tokens to every session and 2,820 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to customer-research, differing in 38 lines, and is treated as a copy.
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