Classify candidates into predefined categories using ELECTRE-Tri, FlowSort, AHPSort, or DRSA methods.
Scanned 6/1/2026
Install to Claude Code
npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill category-sorting --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: category-sorting
description: Classify candidates into predefined categories using ELECTRE-Tri, FlowSort, AHPSort, or DRSA methods.
used-by: multi-criteria-scoring
---
# Category Sorting
**Purpose:** Classify candidate alternatives into predefined categories (e.g., A/B/C grades, compliant/non-compliant), supporting ELECTRE-Tri, FlowSort, AHPSort, DRSA, and other classification methods.
**When to use:**
- User needs to classify alternatives rather than rank them
- Predefined category boundaries exist (e.g., pass/fail, excellent/good/poor)
- Need to independently determine category membership for each alternative
## Budget
| Base SOP | Target | ±10% Range |
|----------|--------|------------|
| criterion-definition | 5-8 criteria | 4-9 |
| weight-elicitation-sop | 1 weight vector | 1 |
| threshold-setting | 1 threshold set | 1 |
| alternative-scoring | 1 score matrix | 1 |
| scoring-synthesis | 1 classification | 1 |
## State Ledger
```yaml
strategy: category-sorting
status: pending
categories_defined: false
criteria_defined: false
weights_computed: false
thresholds_set: false
scores_computed: false
classified: false
result: null
```
## Available Tactics
- **scoring-matrix-construction** — Build scoring foundation
- **screening-then-scoring** — Hybrid workflow: screen first, then classify
## Available SOPs
### Import (from tactics)
- criterion-definition
- weight-elicitation-sop
- alternative-scoring
- threshold-setting
### Subagent
- scoring-synthesis
## Execution Guidance
1. Define category definitions and boundary conditions
2. Invoke criterion-definition to determine classification criteria
3. Invoke threshold-setting to set category boundaries
4. Score each alternative independently and determine category membership
5. Handle borderline cases (pessimistic vs optimistic assignment)
## Output Format
```markdown
## Classification Results
**Method:** [ELECTRE-Tri / FlowSort / AHPSort / DRSA]
**Category Definitions:** [A=Excellent, B=Good, C=Needs Improvement, D=Unqualified]
### Classification Table
| Alternative | Category | Confidence | Boundary Distance |
|-------------|----------|------------|-------------------|
### Borderline Cases
[List alternatives near category boundaries and their sensitivity]
```
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