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 outbound-prospectinggit 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/outbound-prospecting)<a href="https://agentmods.dev/skills/cbrock84/headcount/outbound-prospecting"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/outbound-prospecting/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/cbrock84/headcount/outbound-prospecting"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/outbound-prospecting.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00074 | $0.00853 |
| Opus 5.5 | $0.00030 | $0.00341 |
| Sonnet 5.5 | $0.00015 | $0.00171 |
| Haiku 4.5 | $0.00007 | $0.00085 |
Grade A, and why
outbound-prospecting 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Outbound prospecting
Reply rates are set by list quality far more than by copy. Most outbound problems are targeting problems being solved as writing problems.
Build the list before writing anything
Define the qualifying signal — the observable fact that means this account probably has the problem you solve, right now. Hiring for a role, using a specific tool, a recent funding or expansion announcement, a public complaint about the thing you fix.
Without a signal you are sending to a demographic, and a demographic has no reason to reply.
Then qualify each account against: do they have the problem, can they afford it, can this person act, and is there a reason for now. Missing the last one is why good-fit prospects go quiet.
Writing
- Under a hundred words. Longer gets skimmed and deleted.
- Open with the signal, specifically. Show you looked. Generic personalization tokens are worse than none — they signal automation while pretending otherwise.
- One problem, in their language, not your feature.
- Ask for something small. A specific question or a fifteen-minute call. "Interested in learning more" asks the recipient to do the work of defining the next step.
- No attachments, minimal links in a first message. Both hurt deliverability and trust.
Sequencing
Three to five touches over two to three weeks. Each one adds something new — a different angle, a relevant case, a useful resource. Never "just bumping this to the top of your inbox," which communicates that the first message was not worth reading either.
Multi-channel works when the channels are coordinated and the sender is a person. It reads as harassment when the same message arrives everywhere at once.
Stop after the sequence ends. Persistence past that converts nothing and costs reputation.
Deliverability
Domain warmed, authentication configured, volume per mailbox kept low, list validated. A technically broken send makes perfect copy irrelevant, and the damage to a sending domain takes months to repair.
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.
- 1mo ago Changed · +7 lines 1769cf040066
- 1mo ago First seen · 75 lines · 74 tokens per session scan A cafed49accef
outbound-prospecting is a skill published in the GitHub repository cbrock84/headcount (2,077 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 853 once invoked, about $0.0003 per session on Opus 5.5. 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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