Comparative Feasibility Ranking
SkillDev toolsCompare feasibility across multiple candidates using multi-dimensional
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Comparative Feasibility Ranking skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/comparative-feasibility-ranking/SKILL.md and read by ahel’s review.
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: [...]
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| multi-dimensional-readiness-scan | Assess readiness across multiple dimensions, synthesize into radar visualization, and identify bottleneck dimensions. |
| staged-gate-evaluation | Define gate criteria for each stage, evaluate candidates at each gate, and render go/kill/recycle decisions with evidence. |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| feasibility-synthesis | Synthesize all assessments into a feasibility matrix, recommendation, and risk summary. |
| radar-synthesis | Synthesize multiple dimension scores into radar chart data and compute overall readiness. |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
comparative-feasibility-ranking- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine