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/category-sorting

Classify candidates into predefined categories using ELECTRE-Tri, FlowSort,

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de-anthropocentric-research-engine
393200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill category-sorting --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/category-sorting

Context preview

The summary Claude sees to decide when to auto-load this skill.

Classify candidates into predefined categories using ELECTRE-Tri, FlowSort,

SKILL.md

category-sorting.SKILL.md
name: category-sorting
description: Classify candidates into predefined categories using ELECTRE-Tri, FlowSort,
  AHPSort, or DRSA methods.
dependencies:
  tactics:
  - convergence-scoring-matrix-construction
  - screening-then-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

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

## 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]

<!-- BEGIN available-tables (generated) -->

Available Tactics

Optional, no fixed order; the final leaf is always a sop.

| Tactic | When to use | | --- | --- | | convergence-scoring-matrix-construction | Build a complete scoring matrix through criterion definition, weighting, scoring, normalization, and sensitivity testing. | | screening-then-scoring | First eliminate non-qualifying candidates with non-compensatory rules, then score survivors with full MCDA methods. |

<!-- END available-tables (generated) -->

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Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

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