comparative-feasibility-ranking
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Compare feasibility across multiple candidates using multi-dimensional radar and weighted feasibility index.
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- Distinctive: line 50
Ensure all candidates have been assessed on the same dimensions
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Comparative Feasibility Ranking
Purpose: Produce a defensible ranking of candidates by feasibility. Uses multi-dimensional radar charts to visualize relative strengths and a weighted feasibility index to collapse multiple dimensions into a single comparable score.
When to use:
- Multiple candidates have been assessed and need to be compared
- Stakeholders need a clear ranking to prioritize resource allocation
- You need to identify which candidates are most implementable given current constraints
Budget
| Metric | Target |
| Candidates compared | >= 2 |
| Dimensions in radar | >= 5 |
| Weight justifications | 1 per dimension |
State Ledger
| Key | Type | Description |
| candidates[] | array | All candidates being compared |
| dimension_weights{} | map | Dimension -> weight mapping |
| radar_data[] | array | Per-candidate radar scores |
| feasibility_index[] | array | Weighted composite scores |
| ranking[] | array | Final ranked list |
Available Tactics
| Tactic | When |
| multi-dimensional-readiness-scan | To generate per-candidate radar data for comparison |
| staged-gate-evaluation | To compare gate-passage likelihood across candidates |
Available SOPs
| SOP | Purpose |
| radar-synthesis | Produce radar data for each candidate |
| feasibility-synthesis | Produce final comparative matrix |
Execution Guidance
- Ensure all candidates have been assessed on the same dimensions
- Normalize scores to a common scale (1-9 recommended)
- Assign dimension weights based on context (stakeholder priorities, strategic fit)
- Calculate weighted feasibility index for each candidate
- Produce comparative radar visualization data
- Rank candidates and identify clear tiers (strong/moderate/weak feasibility)
Output Format
comparative_ranking:
dimensions: [technical, market, regulatory, resource, organizational]
weights: {technical: 0.3, market: 0.25, regulatory: 0.2, resource: 0.15, organizational: 0.1}
candidates:
- {name, scores: {...}, weighted_index: 0.X, rank: N, tier: strong|moderate|weak}
radar_data: [{candidate, dimension_scores: [...]}]
recommendation: <top candidate(s) with rationale>
caveats: [...]Does it work?
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