Comparative Feasibility Ranking

SkillDev tools

Compare feasibility across multiple candidates using multi-dimensional

Available today. Use it from your connected AI after setup.

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

MetricTarget
Candidates compared>= 2
Dimensions in radar>= 5
Weight justifications1 per dimension

State Ledger

KeyTypeDescription
candidates[]arrayAll candidates being compared
dimension_weights{}mapDimension -> weight mapping
radar_data[]arrayPer-candidate radar scores
feasibility_index[]arrayWeighted composite scores
ranking[]arrayFinal ranked list

Available Tactics

TacticWhen
multi-dimensional-readiness-scanTo generate per-candidate radar data for comparison
staged-gate-evaluationTo compare gate-passage likelihood across candidates

Available SOPs

SOPPurpose
radar-synthesisProduce radar data for each candidate
feasibility-synthesisProduce final comparative matrix

Execution Guidance

  1. Ensure all candidates have been assessed on the same dimensions
  2. Normalize scores to a common scale (1-9 recommended)
  3. Assign dimension weights based on context (stakeholder priorities, strategic fit)
  4. Calculate weighted feasibility index for each candidate
  5. Produce comparative radar visualization data
  6. 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.

TacticWhen to use
multi-dimensional-readiness-scanAssess readiness across multiple dimensions, synthesize into radar visualization, and identify bottleneck dimensions.
staged-gate-evaluationDefine 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.

SOPWhen to use
feasibility-synthesisSynthesize all assessments into a feasibility matrix, recommendation, and risk summary.
radar-synthesisSynthesize 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