classify

name: classify
description: Assign labels or categories to items based on characteristics. Use when categorizing entities, tagging content, identifying types, or labeling data according to a taxonomy.
argument-hint: "[item] [taxonomy] [multi-label]"
disable-model-invocation: false
user-invocable: true
allowed-tools: Read, Grep
context: fork
agent: explore
layer: UNDERSTAND

Intent

Assign one or more labels from a defined taxonomy to items based on their observed characteristics. This capability bridges detection and reasoning by providing semantic categorization.

Success criteria:
- Item assigned at least one label from taxonomy
- Label assignment supported by evidence
- Confidence scores reflect classification certainty
- Ambiguous cases explicitly flagged

Compatible schemas:
- docs/schemas/classification_output.yaml

Inputs

Procedure

1) Examine the item: Gather characteristics relevant to classification
- Identify distinguishing features
- Note structural patterns, content type, metadata
- Collect evidence for each observed characteristic

2) Understand the taxonomy: Clarify the classification scheme
- If taxonomy provided, use those labels exclusively
- If no taxonomy, infer appropriate categories from context
- Define clear boundaries between categories

3) Match characteristics to labels: Evaluate fit for each potential label
- Score how well item characteristics match each category
- Consider edge cases and borderline classifications
- Note which features drive each potential classification

4) Assign labels: Select the most appropriate label(s)
- For single-label: choose highest confidence match
- For multi-label: include all labels above confidence threshold
- Flag if no label is a strong match

5) Ground classification: Document evidence supporting each label
- Reference specific characteristics that drove classification
- Note any characteristics that contradict the assignment

Output Contract

Return a structured object:

labels:
  - label: string  # Assigned category/label
    confidence: number  # 0.0-1.0 for this specific label
    evidence: array[string]  # Characteristics supporting this label
probabilities:  # Optional: full probability distribution
  label_1: number
  label_2: number
primary_label: string  # Highest confidence label
ambiguous: boolean  # True if classification is uncertain
confidence: 0..1  # Overall classification confidence
evidence_anchors: ["file:line", "characteristic:value"]
assumptions: []

Field Definitions

Examples

Example 1: Classify Code File

Input:

item: "src/services/payment_processor.rb"
taxonomy: ["service", "model", "controller", "helper", "concern"]

Output:

labels:
  - label: "service"
    confidence: 0.92
    evidence:
      - "Located in services/ directory"
      - "Class name ends with 'Processor'"
      - "Contains business logic methods without persistence"
probabilities:
  service: 0.92
  model: 0.03
  controller: 0.02
  helper: 0.02
  concern: 0.01
primary_label: "service"
ambiguous: false
confidence: 0.92
evidence_anchors:
  - "src/services/payment_processor.rb:1"
  - "src/services/payment_processor.rb:15-45"
assumptions:
  - "Directory structure follows Rails conventions"

Example 2: Classify with Ambiguity

Input:

item: "User reported issue: 'App crashes on login'"
taxonomy: ["bug", "feature-request", "question", "documentation"]
multi_label: true

Output:

labels:
  - label: "bug"
    confidence: 0.75
    evidence:
      - "Reports crash behavior"
      - "Describes unexpected failure"
  - label: "question"
    confidence: 0.40
    evidence:
      - "Lacks reproduction steps"
      - "May be user error or configuration"
probabilities:
  bug: 0.75
  question: 0.40
  feature-request: 0.05
  documentation: 0.10
primary_label: "bug"
ambiguous: true
confidence: 0.65
evidence_anchors:
  - "issue:title"
  - "issue:body"
assumptions:
  - "Crash is not expected behavior"
  - "User has attempted normal login flow"

Verification

• [ ] At least one label assigned with confidence > 0.3
• [ ] Evidence exists for each assigned label
• [ ] Labels are from specified taxonomy (if provided)
• [ ] Ambiguous flag set when confidence < 0.7
• [ ] Probabilities sum to ~1.0 (if provided)

Verification tools: Read (to verify evidence references)

Safety Constraints

• mutation: false
• requires_checkpoint: false
• requires_approval: false
• risk: low

Capability-specific rules:
- Do not invent labels outside the provided taxonomy
- Flag uncertainty rather than forcing low-confidence classifications
- Do not access data beyond what's needed for classification
- Note when item characteristics are insufficient for reliable classification

Composition Patterns

Commonly follows:
- detect - Classify items after detecting their presence
- observe - Classify based on observed characteristics
- retrieve - Classify retrieved items

Commonly precedes:
- compare - Classification enables comparison within categories
- plan - Classified items inform planning decisions
- generate - Classification guides content generation

Anti-patterns:
- Never use classify for binary detection (use detect)
- Avoid classify when precise measurement needed (use measure)

Workflow references:
- See workflow_catalog.yaml#capability_gap_analysis for classification usage