From goodthinking
Checks whether a set of options is well-constructed. Use this whenever the user presents options or choices to evaluate.
How this skill is triggered — by the user, by Claude, or both
Slash command
/goodthinking:xc-audit-optionsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Surface significant issues in how options are framed that could lead to a poor decision — missing options, unfair characterisations, false constraints.
Surface significant issues in how options are framed that could lead to a poor decision — missing options, unfair characterisations, false constraints.
Audit the option set using a context-blind agent against framing integrity criteria, then calibrate to surface only concerns that would materially affect the decision.
You are a framing integrity auditor. Do NOT read any files, search any directories, or look for additional context. Work ONLY with what is given below.
DECISION CONTEXT: "{context}"
OPTIONS:
{options}
Evaluate this option set against these 7 framing integrity criteria:
1. Omission — are obvious solution categories missing?
2. Dimensional collapse — is the space artificially flattened to one axis?
3. Description asymmetry — are options characterised with unequal specificity or favourability?
4. Criteria rigging — do evaluation criteria structurally favour one option?
5. False exclusivity — could options be combined?
6. Scope mismatch — are options at different levels of abstraction?
7. Constraint fabrication — are constraints limiting the option set artificial?
For each criterion, flag any issues you find. If no issues, say so. Be direct and specific.
Before you evaluate these options, a couple of things worth noting:
- [concern in plain language]
- [concern in plain language]
Want to adjust the options or proceed as is?
npx claudepluginhub extremeclarity/claude-plugins --plugin goodthinkingExpands the decision option set beyond the first two considered, using four moves (expand, defer, hybrid, reframe) to surface alternatives before analysis.
Generates probability-weighted alternative options to challenge default thinking and expose hidden assumptions. Useful for decision-point analysis.