headcount is an organization of independently installable Claude Code plugins, each grouping skills for a department such as finance, security, or demand generation. Claude Code users install the departments they need and invoke their skills for specialized work; the catalogue entries are skills and related agent tooling from that organization.
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 cbrock84/headcount --skill product-discoverygit clone --depth 1 https://github.com/cbrock84/headcountWrote 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/cbrock84/headcount/product-discovery)<a href="https://agentmods.dev/skills/cbrock84/headcount/product-discovery"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/product-discovery.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 | $0.00094 | $0.00858 |
| Opus 5 | $0.00047 | $0.00429 |
| Sonnet 5 | $0.00019 | $0.00172 |
| Haiku 4.5 | $0.00009 | $0.00086 |
Grade A, and why
product-discovery 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 4d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product discovery
Discovery is how you find out you were wrong while it is still cheap. A process that never kills anything is not discovery, it is a preparation ritual with research attached.
Start from the assumption that would sink this if it were false
Every idea rests on a stack: that the problem exists, that people care enough to change what they do, that your approach solves it, that they would pay, that you can build and deliver it. They are not equally uncertain, and testing them in order of comfort is how teams spend six weeks confirming the safe one.
Write the assumptions down, mark the one that would be most damaging to be wrong about, and test that one first. Usually it is the second: the problem is real, and people are living with it comfortably enough not to move.
Recruit the people who have the problem, not the people who are easy to reach
Interviewing your friendliest customers produces reliable encouragement. Talk to people who churned, people who evaluated and chose something else, and people who solved it another way — those three groups carry most of the information.
Five to eight conversations in a segment usually exhausts the new material. If you are still hearing new things at eight, the segment is too broad.
Interview about the past, not about the future
People are poor at predicting their own behavior and generous when asked to react to an idea. They are reliable narrators of what they actually did.
- Ask about the last time it happened. What triggered it, what they tried, what it cost them, what they did instead.
- Follow the workaround. A spreadsheet someone maintains by hand every week is stronger evidence of a real problem than any amount of enthusiasm about a proposed feature.
- Do not describe your solution until the end, and treat everything said after that point as weaker evidence.
- Silence is a tool. Most of the useful material arrives after the pause you were tempted to fill.
"Would you use this?" and "would you pay for this?" produce answers that do not predict anything. What predicts is whether they have already spent money or time on the problem.
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.
- 4d ago First seen · 75 lines · 94 tokens per session scan A 751d96d2a65f
product-discovery is a skill published in the GitHub repository cbrock84/headcount (1,300 stars, last pushed 5d ago), licensed MIT. It adds 94 tokens to every session and 858 once invoked, about $0.0005 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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