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/deciqai/knowledge-skills/decision-treenpx skills add deciqAI/knowledge-skills --skill decision-treegit 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/decision-tree)<a href="https://agentmods.dev/skills/deciqai/knowledge-skills/decision-tree"><img src="https://agentmods.dev/badge/skills/deciqai/knowledge-skills/decision-tree.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 5.1 | $0.00112 | $0.01988 |
| Opus 5 | $0.00056 | $0.00994 |
| Sonnet 5 | $0.00022 | $0.00398 |
| Haiku 4.5 | $0.00011 | $0.00199 |
Grade A, and why
decision-tree 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 6d 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.
Decision Tree
Overview
A decision tree maps a multi-stage decision: decision nodes (squares) for choices you control, chance nodes (circles) for outcomes you don't, probabilities on every branch, payoffs at the leaves — then rollback right-to-left to get expected value at the root. First systematized by John F. Magee (HBR, 1964); formalized by Howard Raiffa (1968). Its biggest value: converting "I feel we should expand" into "what probability do you assign to high demand?" — making every assumption explicit and contestable.
Composes with expected-value-and-kelly (EV scaffold + bet sizing), probabilistic-thinking (calibration per node), inversion (rollback = working outcomes backward), mece (branches must be MECE so probabilities sum to 1.0).
When to Use
- Decision has sequential stages (decide → learn → decide again)
- Outcomes uncertain; probabilities can be estimated (even roughly)
- Payoffs quantifiable (NPV, revenue, cost, lives saved)
- Multiple stakeholders need a shared visual model to align on assumptions
- Sizing a big irreversible bet under AI uncertainty — build vs. buy vs. wait on AI capex, a fab investment, or committing while AI adoption / AI valuations are unproven
Not when: one-shot choice with no stages; probabilities unestimable; payoffs purely qualitative; branch set too large (use scenario planning instead).
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete multi-stage 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: a decision tree converts "I feel" into "what probability do you assign?" — making assumptions explicit so they can be argued about.
- Check fit. Sequential stages? Uncertain outcomes? Quantifiable payoffs? If yes to all three, a tree applies.
- Elicit their real case. What's the initial choice? What uncertain outcomes follow? What payoffs result?
[WAIT — do not advance until user responds]
- Run The Process one step at a time with their input — draw structure, assign probabilities, assign payoffs, roll back.
[WAIT — do not advance until user responds]
- Close by naming the insight: the threshold at which the recommendation flips, and whether to gather more data.
[WAIT — do not advance until user responds]
What ships with it
4 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.
- 6d ago First seen · 123 lines · 112 tokens per session scan A 82debb014e3e
decision-tree is a skill published in the GitHub repository deciqAI/knowledge-skills (10 stars, last pushed 4d ago), licensed MIT. It adds 112 tokens to every session and 1,988 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 2026-08-31.
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