New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Scenario Planning

by cbrock84 in cbrock84/headcount

Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than…

MITAuto-check passedData & Analytics

Install Scenario Planning

skills CLI
$ npx skills add cbrock84/headcount --skill scenario-planning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install cbrock84/headcount scenario-planning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/cbrock84/headcount.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/corporate-strategy/skills/scenario-planning .claude/skills/scenario-planning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scenario-planning
GitHub stars
2k
Token cost
~846 tokens
SKILL.md length
432 words
Files
1
Skills in repo
178
Repo updated
First seen
Licence
MIT

At a glance

Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than…

  • Tasks that involve Load testing
  • SKILL.md covers Separate what you know from…, Build scenarios from the…, Stress-test the plan against… and Early-warning indicators, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve Forecasting and time series

What it does

Scenario Planning is an agent skill from cbrock84/headcount. Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.

Its SKILL.md is about 850 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Load testing and Forecasting and time series. The repository describes itself as: An agent organization structured as a company — 15+ departments, 125+ skills, each independently installable, citing the standards and regulators that settle the question. Runs… The licence is MIT.

When your agent uses it

  • Tasks that involve Load testing
  • Tasks that involve Forecasting and time series

Example prompts

  • “Use the scenario-planning skill to plan under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting…”
  • “/scenario-planning”

What it can do on your machine

Read from SKILL.md and the folder at commit 98d1c17. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scenario Planning loads about 846 tokens when it runs. Until then it costs about 113 tokens; SKILL.md has 432 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~846

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from cbrock84/headcount at commit 98d1c17, republished under its MIT licence (© cbrock84). 432 words, ~846 tokens.

Download SKILL.mdSave it as .claude/skills/scenario-planning/SKILL.md (or your agent's skills folder).
name
scenario-planning
description
Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one. Use this when a decision depends on something unknowable, when a plan assumes conditions that may not hold, before a large irreversible commitment, or when a market, regulatory, or technology shift could invalidate the strategy.

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.
Show full SKILL.md (150 more words)Show less

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.

Assign each indicator an owner and a review cadence. Scenario work that produces no monitoring is a workshop, not a plan.

Revisit on the trigger, not the calendar

Most scenario planning is done once and filed. Its value comes from being revisited when an indicator fires — that is the moment the earlier thinking pays, because the options were identified before anyone was under pressure.

Never

  • Assign probabilities to scenarios and then plan only for the likeliest. That is forecasting with extra steps.
  • Build a scenario nobody in the room believes possible. It will be ignored, and the exercise loses credibility.
  • Let the exercise end without naming what to do on Monday in every scenario.

© cbrock84, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/corporate-strategy/skills/scenario-planning of cbrock84/headcount.

Open the folder on GitHubat commit 98d1c17

Compare with similar skills

Scenario Planning next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scenario Planning compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scenario Planning this skillcbrock84/headcount2k—~846Automated safety check: PassMIT
Forecast Premortemnicepkg/ai-workflow285—~4.1kAutomated safety check: PassMIT
K6 Trend Analysisgrafana/skills282—~4.8kAutomated safety check: PassApache-2.0
TimesFM Forecastinggoogle-research/timesfm34k—~4.7kAutomated safety check: PassApache-2.0
StatsmodelszLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
Timesfm ForecastingzLanqing/codex-claude-academic-skills4.7k3 repos~7.5kAutomated safety check: NotesApache-2.0

Similar skills

  • Forecast Premortem

    nicepkg/ai-workflow

    A skill your agent uses to stress-test predictions by assuming they failed and working backward to identify why.

    285 GitHub stars~4.1k tokensUpdated 8 mo ago
    Data & AnalyticsAuto-check passed

  • K6 Trend Analysis

    grafana/skills

    Official

    Analyze Grafana Cloud k6 test run trends over time. An agent skill from grafana/skills.

    282 GitHub stars~4.8k tokensUpdated yesterday
    DevOps & CloudAuto-check passed

  • TimesFM Forecasting

    google-research/timesfm

    Forecasts any univariate time series zero-shot with Google's TimesFM model, returning point forecasts and calibrated prediction intervals without training.

    34k GitHub stars~4.7k tokensUpdated 10 days ago
    Data & AnalyticsAuto-check passed

  • Statsmodels

    zLanqing/codex-claude-academic-skills

    Statistical models library for Python. An agent skill from zLanqing/codex-claude-academic-skills.

    4.7k GitHub starsUsed in 15 repos~4.9k tokens
    Data & AnalyticsAuto-check passed

  • Timesfm Forecasting

    zLanqing/codex-claude-academic-skills

    Zero-shot time series forecasting with Google's TimesFM foundation model.

    4.7k GitHub starsUsed in 3 repos~7.5k tokens
    Data & AnalyticsAuto-check: notes

  • Find Hypertable Candidates

    timescale/pg-aiguide

    A skill your agent uses to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.

    1.9k GitHub starsUsed in 1 repo~2.6k tokens
    Data & AnalyticsAuto-check passed

More from cbrock84/headcount

All 178 skills in this repo

  • Agent Hierarchy

    cbrock84/headcount

    Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a…

    2k GitHub stars~1.2k tokensUpdated 22 days ago
    Auto-check passed

  • Access And Identity

    cbrock84/headcount

    Designs and audits who can reach what — authentication, authorization models, privileged access, service credentials, and joiner-mover-leaver process.

    2k GitHub stars~1.1k tokensUpdated 22 days ago
    Auto-check passed

  • Account Based Marketing

    cbrock84/headcount

    Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group…

    2k GitHub stars~1.2k tokensUpdated 22 days ago
    Auto-check passed

  • Activation

    cbrock84/headcount

    Gets new users from signup to first real value — signup flow, onboarding, time-to-value, and the early experience that determines whether someone becomes a user or a lapsed account.

    2k GitHub stars~865 tokensUpdated 22 days ago
    Auto-check passed

  • AI ML Governance

    cbrock84/headcount

    Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire.

    2k GitHub stars~1k tokensUpdated 22 days ago
    Auto-check passed

  • AI Research Analyst

    cbrock84/headcount

    Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit.

    2k GitHub stars~916 tokensUpdated 22 days ago
    Auto-check passed

Categories

Questions about Scenario Planning

What does Scenario Planning do?

Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than…. Scenario Planning is an agent skill from cbrock84/headcount. Plans under genuine uncertainty — building scenarios, identifying which assumptions are load-bearing, setting early-warning indicators, and stress-testing a plan against futures rather than forecasting one.

When should I use Scenario Planning?

Scenario Planning fits situations like: tasks that involve Load testing; tasks that involve Forecasting and time series.

How do I install Scenario Planning in Claude Code?

Run `npx skills add cbrock84/headcount --skill scenario-planning -a claude-code`. Or copy the skill folder (plugins/corporate-strategy/skills/scenario-planning in cbrock84/headcount) into .claude/skills/scenario-planning in your project. Claude Code loads it when a task matches its description.

How do I install Scenario Planning in Codex?

Run `npx skills add cbrock84/headcount --skill scenario-planning -a codex`. Or copy the skill folder (plugins/corporate-strategy/skills/scenario-planning in cbrock84/headcount) into .agents/skills/scenario-planning in your project. Codex loads it when a task matches its description.

Can I use Scenario Planning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add cbrock84/headcount --skill scenario-planning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scenario-planning, .gemini/skills/scenario-planning, .github/skills/scenario-planning and .opencode/skills/scenario-planning in your project.

What does Scenario Planning need to run?

SKILL.md names no scripts, command-line tools or credentials: Scenario Planning is instructions for the agent only.

Does Scenario Planning access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scenario Planning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scenario Planning use?

Scenario Planning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scenario Planning use?

About 846 tokens (SKILL.md is roughly 3.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scenario Planning?

Skills that share tags, products or a category with Scenario Planning: Forecast Premortem (nicepkg/ai-workflow, 285 stars), K6 Trend Analysis (grafana/skills, 282 stars), TimesFM Forecasting (google-research/timesfm, 34k stars) and Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scenario Planning?

cbrock84 (a GitHub user) maintains it in cbrock84/headcount, which has 2,022 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on September 17, 2026.

Source: cbrock84/headcount on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.