Decide

ℹ️ First, set expectations: print one line with this skill's version and its most recent change - the top of metadata.changelog - e.g. decide v1.0 — recent: initial decision-structuring skill. Then proceed.

Purpose

/advise produces a better map; /decide produces a choice. When the question is "what should we do", frameworks alone under-deliver - the output must be a recommendation produced by an explicit decision rule, with the losing options accounted for and tripwires attached. The KB skeleton is decision science (Kahneman / Gilbert / Tetlock / Thaler / Kay-King, bound by Ergodicity); this skill is where that skeleton actually decides instead of only describing.

Problem

$ARGUMENTS

State Dependencies

Never writes. A decision brief may be saved to Brain/05-Meta/Reports/ only on explicit request (Step 8).

Composes

• reasoning-checks - the shared discipline contract (applied per its applicability matrix, this skill runs the full battery)

Process

Step 1: Decision Guard (route before structuring)

Confirm this is actually a decision: two or more real courses of action, stakes, and someone who must choose. If it is understanding-seeking ("help me think about X") → /advise or /think-about-it. If it is a company-direction question canon already covers → /canon-advise governs; never relitigate a decided direction - /decide may still structure a new decision that canon leaves open (run one canon-mounted search BRAIN_READ_SCOPE=core,Canon when the subject smells like company direction; if a decision/spec note decides it, say so and stop).

Step 2: Frame the Decision

One block, before any retrieval:

• Decision statement - the choice in one sentence
• Decider + deadline - who chooses, by when, and what forces the timing (a decision with no deadline may be a defer candidate by default)
• Irreversibility horizon - what becomes hard to undo, and when

Step 3: Parallel Retrieval (one batch)

Same fast-path discipline as /advise - no subagents, static search, one parallel batch. Read role: reasoning (contract: scope-mount): two passes per term, core as the spine and the reasoning mount as the evidence layer; run the scope-mount trigger check first and append ,company / ,thinkers / ,Books/<slug> to the wide pass on a hit.

BRAIN_READ_SCOPE=core                          resources/local-brain-search/run_search.sh "[domain term 1]" --limit 3 --json
BRAIN_READ_SCOPE=core,Books,document-insights  resources/local-brain-search/run_search.sh "[domain term 1]" --limit 5 --json
BRAIN_READ_SCOPE=core                          resources/local-brain-search/run_search.sh "[domain term 2]" --limit 3 --json
BRAIN_READ_SCOPE=core,Books,document-insights  resources/local-brain-search/run_search.sh "[domain term 2]" --limit 5 --json
BRAIN_READ_SCOPE=core                          resources/local-brain-search/run_search.sh "[decision-structure term: ergodicity / optionality / reversibility / regret / explore exploit - match to the decision's shape]" --limit 3 --json
BRAIN_READ_SCOPE=core,Books,document-insights  resources/local-brain-search/run_search.sh "[the same decision-structure term]" --limit 5 --json
resources/local-brain-search/run_connections.sh --hubs --json   # fingerprint - always core; for the attractor check

Then read the 2-4 most relevant notes in parallel (frontmatter provenance: rides along for the checks).

Step 4: Expand the Option Set

The stated options are almost never the full set. Always add and assess:

• Status quo - explicitly, with its own costs (doing nothing is a choice)
• Defer - wait for information; a real option with a price (cost of delay) and a payoff (uncertainty resolved)
• Pilot / staged commitment - buy information while moving
• Hybrids - combinations the framing hid

Mark which options preserve optionality and which foreclose it.

Step 5: Classify the Decision (this selects the machinery)

Step 6: Consequence Table + the Matching Rule

Build a compact table: options × the 2-4 uncertainties that actually drive the outcome. Probabilities only where estimable and reference-class anchored (Check 4); payoffs in natural units, not scores.

Then apply the rule the classification selected - in this order:

1. Ergodic filter first - eliminate any option with ruin exposure regardless of its EV ("EV-positive but you can't survive the bad branch" is a losing bet by the user's own core framework). Name what it eliminated.
2. Risk + repeated/reversible → expected-value decomposition - show the components, not just the total.
3. Radical uncertainty → robustness + minimax regret - which option is acceptable across all live scenarios; which minimizes the worst regret. Do NOT fake point probabilities to force an EV number - saying "this is not probabilizable" is the Kay-King discipline, not a cop-out.
4. Value of information - if defer/pilot resolves a driving uncertainty and the delay cost is tolerable, the real option can beat both stated options.
5. Regret cross-check - long-lens (would the 10-year view flip this?). A cross-check, never the primary rule.

Step 7: Reasoning Checks (required - full battery)

Apply .claude/skills/reasoning-checks/SKILL.md:

• Epistemic Inversion on the recommendation - the pre-mortem is made for decisions; specific falsifier required
• Reference Class - required in practice (a decision embeds forecasts; anchor every estimated probability/magnitude)
• Attractor Check - against the hubs fetched in Step 3. Scope nuance: the check applies to the situation framing/diagnosis, not to the decision machinery itself - decision-science hubs (Decision Making, Ergodicity, Superforecasting) are this skill's tools by construction and do not count as attractor pull. What counts: the diagnosis leaning on 2+ top hubs.
• Provenance Base - from the notes read in Step 3

Step 8: Decision Brief (output)

## Decision: [one-line statement]

**Type:** [reversible? one-shot/repeated? ruin exposure? risk/radical uncertainty] → **Rule applied:** [ergodic filter → EV | robustness/minimax regret | value-of-information]

**Option set considered:** [stated + added options; which were eliminated and by what]

**Consequence table:** [options × driving uncertainties]

**Recommendation:** [the choice, produced by the named rule - and why the losing options lose]

**Tripwires (revisit if):**
- [observable condition → what it changes]
- [date-based checkpoint if the decision was defer/pilot]

**KB grounding:** [[Note 1]], [[Note 2]] - [how each shaped the structure]

[reasoning-checks blocks: Epistemic Inversion · Reference Class · Attractor Check · Provenance Base]

On explicit request only: save the brief to Brain/05-Meta/Reports/decision-[slug]-YYYY-MM-DD.md (provenance: ai-inferred, standard frontmatter) - decision briefs are terminal deliverables, exactly what Reports/ is for. Never auto-save.

Rules

• NO subagent spawning - fast path, all inline
• Parallel batches - searches + hubs in one round, reads in one round; checks reuse those results
• The recommendation must name its rule - "this is an EV call" vs "this is a robustness call" is the whole point of the mode-switch; a recommendation with no named rule is /advise output wearing a costume
• Never fake probabilities under radical uncertainty - the regime classification is honest or the machinery is theater
• Ruin trumps EV - the ergodic filter is not optional when an absorbing barrier is live
• Tripwires always - a decision brief without revisit conditions is a prediction, not a decision
• If the KB lacks relevant frameworks, say so and structure the decision from general principles, labelled as such