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 mergers-and-acquisitionsgit 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/mergers-and-acquisitions)<a href="https://agentmods.dev/skills/cbrock84/headcount/mergers-and-acquisitions"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/mergers-and-acquisitions/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/mergers-and-acquisitions"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/mergers-and-acquisitions.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 | $0.00066 | $0.00775 |
| Opus 5.5 | $0.00026 | $0.00310 |
| Sonnet 5.5 | $0.00013 | $0.00155 |
| Haiku 4.5 | $0.00007 | $0.00077 |
Grade A, and why
mergers-and-acquisitions 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mergers and acquisitions
Deal execution requires qualified legal, tax, and accounting advisers. This structures the commercial thinking and identifies what needs specialist work; it does not substitute for it.
The thesis comes first, and in writing
Before looking at any target: what would an acquisition get us that we cannot build or partner our way to, and why is buying better?
Legitimate theses are specific — a capability that would take three years to build, access to a customer base we cannot reach, consolidation economics in a fragmenting market, a team with scarce expertise.
Illegitimate theses, all common: growth for its own sake, defensive panic, the target became available, and the belief that two struggling businesses combine into a healthy one.
Write the thesis before the target. A thesis reverse-engineered to fit an available company will justify anything.
Screening
Score candidates against the thesis, not against how impressive they are. The best target is frequently the boring one that fits precisely.
Assess cultural and operating-model fit early rather than as a soft afterthought. Integration failure is the most common way deals destroy value, and its causes are visible before signing — incompatible decision-making, different customer commitments, a founder who will not stay.
Valuation framing
Two numbers matter and they are different: what it is worth to you given the synergies you can actually realize, and what you would pay, which must be lower.
Be brutal about synergies. Cost synergies are real and estimable; revenue synergies are usually optimistic and rarely arrive on schedule. Model the deal without revenue synergies and see whether it still works — if it only works with them, it probably does not work.
Name your walk-away price before negotiating, and treat it as binding. Deal momentum is a powerful force and it is not evidence.
Diligence
Commercial diligence answers whether the thesis is true: are the customers real, is the retention as claimed, does the growth come from where they say. Financial, legal, and technical diligence run alongside with specialists.
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 8e2a8fb4cf4d
- 1mo ago First seen · 73 lines · 66 tokens per session scan A 1f4472787dbf
mergers-and-acquisitions is a skill published in the GitHub repository cbrock84/headcount (2,088 stars, last pushed 7d ago), licensed MIT. It adds 66 tokens to every session and 775 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 2026-08-30.
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