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 deciqAI/knowledge-skills --skill scenario-planninggit clone --depth 1 https://github.com/deciqAI/knowledge-skillsWrote 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/deciqai/knowledge-skills/scenario-planning)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/scenario-planning"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/scenario-planning/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/deciqai/knowledge-skills/scenario-planning"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/scenario-planning.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 | $0.00113 | $0.02256 |
| Opus 5 | $0.00056 | $0.01128 |
| Sonnet 5 | $0.00023 | $0.00451 |
| Haiku 4.5 | $0.00011 | $0.00226 |
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 9d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scenario Planning
Overview
Scenario planning accepts that certain futures are genuinely unknowable and prepares for several of them rather than betting on one forecast. Pierre Wack formalized this at Shell in the early 1970s; Shell's pre-built Scenario B let it survive the 1973 oil shock while competitors were unprepared. Schwartz: "The goal is not to predict the future but to make decisions that are robust across a variety of possible futures." (The Art of the Long View, 1991, p. 9.)
Composition: probabilistic-thinking before (base-rate grounding); second-order-thinking inside each scenario (chain reactions); inversion alongside (stress-test current strategy).
When to Use
Apply when: decision is large and hard to reverse; 3+ year horizon with a genuinely bi-directional driver; non-consensus outcome would be catastrophic; macro forces (geopolitics, regulation, technology) are pivotal; a bet hinges on whether AI capex / AI valuations sustain or correct, or on how AI adoption and chip-supply policy unfold; or you are weighing how deep an AI-vendor commitment or multi-year enterprise AI-adoption bet to make while pricing, compute supply, and vendor viability are unsettled.
When NOT to use: Tactical/short-reversibility decisions; single measurable driver (use sensitivity analysis); team lacks authority to change strategy; as a substitute for execution.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete decision → run The Process directly.
- Coach mode: user is unfamiliar or has no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line what-it-is: scenario planning builds 3–4 different plausible futures and asks "can our strategy survive all of them?" instead of betting on the most likely one.
- Check fit against When to Use / When NOT to use. If decision is short-horizon and reversible, redirect to a simpler tool.
- Elicit their real decision. "I want to think about the future" is not a case. "Should we build a factory in Eastern Europe given supply-chain uncertainty?" is. Get specific.
[WAIT — do not advance until user responds]
- Walk The Process one step per exchange: focal question first, then drivers, then the two critical uncertainties — ask at each gate rather than generating on their behalf.
[WAIT — do not advance until user responds]
- Close by naming the one scenario the team had not taken seriously before, and the one pre-emptive action it suggests.
[WAIT — do not advance until user responds]
What ships with it
3 files 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.
- 9d ago First seen · 123 lines · 113 tokens per session scan A 3a7ab33f8f0e
scenario-planning is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 10d ago), licensed MIT. It adds 113 tokens to every session and 2,256 once invoked, about $0.0006 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 2026-09-03.
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