Comparative Formulation
SkillDev tools'Strategy: Construct comparative research questions — systematic comparison
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 Formulation skill
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/comparative-formulation/SKILL.md and read by ahel’s review.
Construct comparative research questions — when research requires comparing A vs B, systematically construct a fair, meaningful comparison.
When to Use
- Need to compare two methods/conditions/groups
- The hypothesis involves "X is better than / different from Y"
- Need to ensure the fairness and validity of the comparison
Thinking Framework
Core logic: a good comparative research question requires clarifying four elements — what is compared (objects), along what dimension (metrics), under what conditions (controls), and what counts as "different" (threshold).
Comparison Design Principles
- Fairness: the comparison conditions are fair to both sides (not a strawman)
- Clear dimensions: along which dimension(s) the comparison is made
- Controlled variables: all conditions are the same except the compared objects
- Effect size: not just "whether there is a difference" but "how large a difference is meaningful"
Comparison Types
| Type | Example | Key considerations |
|---|---|---|
| Method comparison | Method A vs Method B | Implementation fairness, dataset selection |
| Condition comparison | With X vs Without X | Controlled variables, confounding factors |
| Group comparison | Group A vs Group B | Matching, selection bias |
| Temporal comparison | Before vs After | History effects, maturation effects |
Budget Gate
| Tier | Comparison design | Fairness argument | Output |
|---|---|---|---|
| S | Comparison objects + clear dimensions | Basic fairness statement | ≥1 comparative RQ |
| M | + controlled variables + effect size | Fairness argument + identification of potential bias | ≥2 comparative RQs |
| L | + multi-dimensional + sensitivity | Full fairness analysis + bias mitigation strategy | ≥3 comparative RQs |
Default Reference Flow
- Determine the comparison objects (what A and B are)
- Determine the comparison dimensions (along what metrics to compare)
- Determine the control conditions (what to keep constant)
- Argue fairness (whether the comparison is fair)
- Structure it with the PICO framework (the C component is core)
- FINER check
- Define success criteria (what counts as a "meaningful difference")
context-checkpoint
After the Strategy completes, context-checkpoint must be called, recording:
- Comparison objects and selection rationale
- Comparison dimensions
- Fairness argument
- Final comparative RQ
Available Tactics
Optional, no fixed order; the final leaf is always a sop.
| Tactic | When to use |
|---|---|
| framework-selection-and-application | Tactic: Select the most suitable RQ framework and apply it systematically |
Available SOPs
Optional, no fixed order; the final leaf is always a sop.
| SOP | When to use |
|---|---|
| finer-criteria-check | SOP: check research-question quality against each of the 5 FINER criteria |
| success-criteria-definition | SOP: Define measurable success criteria for a research question |
Signals
- GitHub stars
- 469
- Forks
- 37
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
comparative-formulation- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine