| name | solution-alternatives |
| description | Generate and compare multiple solution alternatives for hard issues using explicit constraint modeling, dependency and conflict analysis, and optimization-function reasoning. Use when Codex must evaluate tradeoffs, handle ambiguity with clarifying questions, refine problematic constraints, and recommend a primary option with fallback conditions. |
Solution Alternatives
Use this skill to produce decision-quality alternatives for hard issues with conflicting constraints.
Workflow
- Run intake.
- Capture objective, context, hard constraints, soft constraints, preferred risk level, and timeline.
- Ask which optimization mode to use:
weighted, lexicographic, or pareto.
- Default to
weighted if no mode is selected.
- Resolve ambiguity with a bounded protocol.
- Ask clarifying questions only when ambiguity can change feasibility or ranking.
- Run up to 2 rounds of clarification.
- In round 1, ask direct questions to fill missing high-impact inputs.
- In round 2, use a different questioning approach from ambiguity_questioning_patterns.md.
- After round 2, stop asking and proceed with explicit assumptions.
- Mark unresolved ambiguity and confidence impact in output.
- Build and refine the constraint model.
- Classify constraints as
hard, soft, derived, or optimization.
- Detect problematic constraints: vague, not measurable, contradictory, or disconnected from the objective.
- Rewrite problematic constraints into measurable forms.
- Add derived constraints when dependency analysis reveals hidden limits.
- Add optimization constraints when objective or utility tradeoff is under-specified.
- Follow constraint_modeling_guide.md.
- Analyze constraint dependencies and crush risks.
- Identify pairwise dependency status:
supports, conflicts, or independent.
- Detect crush risk where optimizing one constraint materially harms another.
- Rate conflict severity and include mitigation notes.
- Generate alternatives.
- Produce exactly 3 materially different alternatives by default.
- Ensure alternatives are distinct in approach, not minor variants.
- For each alternative, map impacts to the full constraint set.
- Apply optimization function.
- Use the selected optimization mode rules in optimization_modes.md.
- Always include the optimization function logic in the output.
- In
weighted mode, include sensitivity checks and report rank flips.
- Recommend and define fallback.
- Select one primary recommendation.
- Define explicit switch conditions where runner-up becomes preferred.
- Include next validation step that can confirm or falsify the recommendation quickly.
Output Rules
- Always use the section order in decision_brief_template.md.
- Keep assumptions explicit and scoped to unresolved ambiguity.
- Do not claim certainty when key assumptions remain unvalidated.
- Keep reasoning concise, concrete, and tied to constraint evidence.
Defaults
- Alternatives count:
3
- Ambiguity rounds:
2
- Default optimization mode:
weighted
References
