Explore options

Skill: explore-options

Generate multiple candidate product shapes before the loop commits to one. This is the discovery loop's divergence stage (pre-G1.5), and it exists because every other phase of the gate ladder is convergent — left alone the loop locks onto the first coherent framing and commits early (myopic-greedy commitment, the loop's headline risk). The Double Diamond and Design Sprint treat forced divergence as non-optional; this skill is that forcing function.

It is prompt-only (CHARTER Principle 3): no engine, no scorer, no candidate generator script — the agent following this body writes the candidates as blackboard slots. No new agent, no new reviewer.

When to invoke

1. There is a framed intent to diverge on (from frame-intent) — you are generating solution shapes for a stated outcome, not shaping the outcome itself.
2. The loop has not yet converged — divergence runs before G1.5. If the team already committed and wants to re-open, that is the explore-alternatives verdict routing back here.
3. You want breadth, not a single answer. If the shape is genuinely obvious and the appetite is tiny, say so — manufacturing five candidates for a one-shape problem is waste.

The two axes

Generate N candidate shapes (4–5 is the useful range) across two axes — this is what stops the candidates from being trivial variations of one idea:

• Altitude — narrow-slice ↔ whole-domain. The myopic default picks the narrow slice (a kitchen "draft-and-approve" assistant); force the higher altitude (the whole household — calendar, travel, vendors, budget) and the deeper sub-domain (meal → recipe → ingredient → store sourcing).
• Mechanic — the interaction model: draft-and-approve / coordination-layer / knowledge-graph-first / ambient-capture (illustrative, not closed). The same outcome under a different mechanic is a different product.

Procedure

1. Generate the candidate set. For each candidate, write a blackboard intent-variant slot under the diverging parent (the plan-tree's candidates array — see the discovery-loop asset). Each candidate carries:
   • altitude and mechanic (where it sits on the two axes);
   • a one-line shape (what the product is under this framing);
   • its riskiest assumption — the one that, if wrong, sinks it (front it with what would have to be true).
2. Reuse, don't reinvent. Pressure and rank with the skills that already exist — you are generating; they select and stress:
   • compare-hypotheses' ACH matrix to select among the shapes;
   • devils-advocate to pressure each candidate;
   • de-risk-intent to risk the chosen one (and to seed each candidate's riskiest assumption);
   • the discovery loop's scenario-variation self-coverage module to widen the set along persona / state / scale / adversarial edges.
3. Frame an explicit compare-and-choose. Divergence ends in a selection, not a pile. Recommend one shape and say why, but retain the not-chosen as rejected / parked with rationale — never deleted, so they stay revivable (the loop's persistence + decision-archaeology's revival check). The altitude bet is a value/scope call — surface it at G1.5, do not resolve it silently.

What you write

The candidate set + the selection on the plan-tree node (the discovery loop's plan-tree asset candidates + selection). A candidate slot:

- id: cand.<slug>
  altitude: narrow-slice | whole-domain | <a point between>
  mechanic: draft-and-approve | coordination-layer | knowledge-graph-first | ambient-capture | <other>
  shape: <one line — what the product is under this framing>
  riskiest_assumption: <what would have to be true>
  status: selected | rejected | parked
  rationale: <why selected / retained-not-chosen>

Anti-patterns to refuse

• Generating trivial variations of one idea. If every candidate sits at the same altitude with the same mechanic, you diverged on the label, not the shape. Span both axes.
• Deleting the not-chosen. Retain rejected/parked candidates with rationale — they are revivable, and deleting them re-creates the myopic commitment divergence exists to prevent.
• Resolving the altitude bet silently. Altitude is a value/scope call — surface it at G1.5, with the candidates as the referent.
• Re-implementing selection or critique. Reuse compare-hypotheses / devils-advocate / de-risk-intent; this skill generates.
• Building a generator engine. Prompt-only — the agent writes the candidates; there is no scorer or candidate-synthesis script.