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 app-store-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/app-store-optimization)<a href="https://agentmods.dev/skills/cbrock84/headcount/app-store-optimization"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/app-store-optimization/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/app-store-optimization"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/app-store-optimization.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 | $0.00064 | $0.00776 |
| Opus 5.5 | $0.00026 | $0.00310 |
| Sonnet 5.5 | $0.00013 | $0.00155 |
| Haiku 4.5 | $0.00006 | $0.00078 |
Grade A, and why
app-store-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 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
App store optimization
Two levers, and they are separate problems: being found, and being installed once found. Diagnose which is failing before changing anything.
Being found
The stores index different fields, so the same metadata does not work on both.
- App name / title — the single heaviest field. Brand plus the primary descriptive term. Do not spend it on brand alone.
- Subtitle and keyword field — no repetition across fields; duplicated terms are wasted characters, not reinforcement.
- Long description — indexed on one store, effectively not on the other. Write it for the store that indexes it and for humans on the store that does not.
- Category — pick where you can rank, not where you technically belong.
Target terms with real intent. Ranking first for a term nobody searches is a vanity result.
Being installed
Most visitors decide from the first screenshot and the rating, without scrolling or reading.
- Screenshots — the first two carry the decision. Lead with the outcome or the core screen, with a caption stating the benefit. Never lead with an onboarding or login screen.
- Icon — recognizable at actual size, distinct from category conventions. Test at real scale on a device.
- Rating — the strongest single conversion factor. Prompt for review after a success moment, never on launch or mid-task.
- Video — only if it demonstrates something a screenshot cannot. A weak one costs installs.
Reviews
Respond to negative reviews specifically and without defensiveness, naming the fix and its version where there is one. Prospects read the responses as much as the complaints, and a pattern of real answers converts.
Watch review text for recurring themes — it is the cheapest continuous product research available.
Testing
Change one element at a time and let it run a full weekly cycle; app traffic is strongly day-of-week seasonal. Attributing a lift to the wrong change is worse than not testing.
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 63a65e20ac84
- 1mo ago Changed · +19 lines f74230dc43f7
- 1mo ago First seen · 49 lines · 64 tokens per session scan A 6d504463d270
app-store-optimization is a skill published in the GitHub repository cbrock84/headcount (2,069 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 776 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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