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 agentmods add skills/cbrock84/headcount/ai-search-optimizationnpx skills add cbrock84/headcount --skill ai-search-optimizationgit 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/ai-search-optimization)<a href="https://agentmods.dev/skills/cbrock84/headcount/ai-search-optimization"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/ai-search-optimization.svg" alt="Measured on agentmods" 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 | $0.00079 | $0.00598 |
| Opus 5 | $0.00039 | $0.00299 |
| Sonnet 5 | $0.00016 | $0.00120 |
| Haiku 4.5 | $0.00008 | $0.00060 |
Grade A, and why
ai-search-optimization 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 yesterday.
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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI search optimization
Classical SEO optimizes to be clicked. This optimizes to be quoted — often with no click at all. That changes what a good page looks like.
What gets cited
- Self-contained passages. A retrieved chunk arrives without the surrounding page. Each section must make sense alone, with its subject named rather than pronominalized.
- Direct answers near the question. Bury the answer under three paragraphs of context and the passage retrieved will be the context.
- Specific, checkable facts — numbers, dates, named methods, stated conditions. Vague claims are neither retrievable nor quotable.
- Attributable expertise. Named authors, stated credentials, dated content, and cited sources. Anonymous undated content is weakly weighted.
- Structure that survives extraction — real headings, real lists, real tables. Layout implied by styling disappears.
Practical moves
- Answer the question in the first sentence under each heading, then elaborate.
- Write headings as the questions people actually ask.
- Define your own terms on your own pages, so the model's definition traces to you.
- Keep facts consistent across your site. Contradictions get resolved against you.
- Maintain the boring canonical pages — pricing, comparisons, specifications, FAQ. These are heavily retrieved and usually neglected.
Being represented accurately
Assistants assemble an answer about you from whatever is available, weighted toward third-party and structured sources. Where those are thin or stale, the answer will be wrong.
Audit periodically: ask several assistants what your company does, who it is for, what it costs, and how it compares. Note the errors and trace them to a source. The fix is almost always publishing or correcting the source, not the assistant.
Measuring
Click-through will fall on informational queries even as influence rises. Track citation and mention frequency, and downstream branded search and direct traffic, rather than judging this program on organic sessions — that metric will say you are losing while you are winning.
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.
- yesterday Changed · +6 lines 4f39648c3488
- 5d ago First seen · 47 lines · 79 tokens per session scan A 1a8b6daedbf5
ai-search-optimization is a skill published in the GitHub repository cbrock84/headcount (1,247 stars, last pushed 2d ago), licensed MIT. It adds 79 tokens to every session and 598 once invoked, about $0.0004 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 2026-08-30.
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