/category-sorting
Classify candidates into predefined categories using ELECTRE-Tri, FlowSort,
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill category-sorting --agent claude-codeHow 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.mdname: 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) -->
Read more
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) -->
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.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
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Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill

