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 scenario-planninggit 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/scenario-planning)<a href="https://agentmods.dev/skills/cbrock84/headcount/scenario-planning"><img src="https://agentmods.dev/badge/skills/cbrock84/headcount/scenario-planning.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 | $0.00078 | $0.00683 |
| Opus 5 | $0.00039 | $0.00342 |
| Sonnet 5 | $0.00016 | $0.00137 |
| Haiku 4.5 | $0.00008 | $0.00068 |
Grade A, and why
scenario-planning 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 8d 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 — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario planning
Forecasting produces one number and false confidence. Scenario planning produces a plan that survives being wrong, which is the realistic goal.
Separate what you know from what you are assuming
List the plan's assumptions explicitly, then sort them:
- Predetermined — things that will happen regardless. Demographics, contracted commitments, technology already deployed. Plan around them; do not spend analysis on them.
- Genuinely uncertain and load-bearing — the plan changes materially depending on how they resolve.
Almost every plan has two or three load-bearing uncertainties. Finding them is most of the value, and the exercise usually surfaces one nobody had articulated.
Build scenarios from the uncertainties, not from moods
The common failure is three scenarios named optimistic, base, and pessimistic — which is one scenario with the numbers scaled, and it teaches nothing.
Take the two most consequential uncertainties and build the quadrants. Each scenario should be internally coherent: if demand is high and supply is constrained, what else follows — pricing, competitor behavior, regulatory attention?
Give each a name that captures its logic. Names make scenarios usable in conversation, which is where they earn their keep.
Three or four scenarios. More cannot be held in mind; two collapses into best and worst.
Stress-test the plan against each
For every scenario: does the plan still work, what breaks first, and what would we wish we had done sooner?
The output is not a prediction. It is three things:
- Robust moves — sensible in every scenario. Do these now, with confidence.
- Contingent moves — right in some scenarios only. Prepare, do not commit.
- Options — small investments that buy the right to act later. Deliberately underrated, because they look like indecision and are actually the cheapest way to handle uncertainty.
Early-warning indicators
For each scenario, name the observable signal that would show it is arriving — and specify it precisely enough to be checked. "Regulatory pressure increases" is not observable. "A second jurisdiction opens a consultation" is.
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.
- 8d ago First seen · 71 lines · 78 tokens per session scan A 45b5fec715bf
scenario-planning is a skill published in the GitHub repository cbrock84/headcount (1,300 stars, last pushed 4d ago), licensed MIT. It adds 78 tokens to every session and 683 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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