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name: adr-decision-extraction description: Extract architectural decisions from conversations. Identifies problem-solution pairs, trade-off discussions, and explicit choices. Use when analyzing session transcripts for ADR generation.

ADR Decision Extraction

Extract architectural decisions from conversation context for ADR generation.

Detection Signals

Signal Type	Examples
Explicit markers	[ADR], "decided:", "the decision is"
Choice patterns	"let's go with X", "we'll use Y", "choosing Z"
Trade-off discussions	"X vs Y", "pros/cons", "considering alternatives"
Problem-solution pairs	"the problem is... so we'll..."

Extraction Rules

Explicit Tags (Guaranteed Inclusion)

Text marked with [ADR] is always extracted:

[ADR] Using PostgreSQL for user data storage due to ACID requirements

These receive confidence: "high" automatically.

AI-Detected Decisions

Patterns detected without explicit tags require confidence assessment:

Confidence	Criteria
high	Clear statement of choice with rationale
medium	Implied decision from action taken
low	Contextual inference, may need verification

Output Format

{
  "decisions": [
    {
      "title": "Use PostgreSQL for user data",
      "problem": "Need ACID transactions for financial records",
      "chosen_option": "PostgreSQL",
      "alternatives_discussed": ["MongoDB", "SQLite"],
      "drivers": ["ACID compliance", "team familiarity"],
      "confidence": "high",
      "source_context": "Discussion about database selection in planning phase"
    }
  ]
}

Field Definitions

Field	Required	Description
title	Yes	Concise decision summary
problem	Yes	Problem or context driving the decision
chosen_option	Yes	The selected solution or approach
alternatives_discussed	No	Other options mentioned (empty array if none)
drivers	No	Factors influencing the decision
confidence	Yes	high, medium, or low
source_context	No	Brief description of where decision appeared

Extraction Workflow

1. Scan for explicit markers - Find all [ADR] tagged content
2. Identify choice patterns - Look for decision language
3. Extract trade-off discussions - Capture alternatives and reasoning
4. Assess confidence - Rate each non-explicit decision
5. Capture context - Note surrounding discussion for ADR writer

Pattern Examples

High Confidence

"We decided to use Redis for caching because of its sub-millisecond latency
and native TTL support. Memcached was considered but lacks persistence."

Extracts:

• Title: Use Redis for caching
• Problem: Need fast caching with TTL
• Chosen: Redis
• Alternatives: Memcached
• Drivers: sub-millisecond latency, native TTL, persistence
• Confidence: high

Medium Confidence

"Let's go with TypeScript for the frontend since we're already using it
in the backend."

Extracts:

• Title: Use TypeScript for frontend
• Problem: Language choice for frontend
• Chosen: TypeScript
• Alternatives: (none stated)
• Drivers: consistency with backend
• Confidence: medium

Low Confidence

"The API seems to be working well with REST endpoints."

Extracts:

• Title: REST API architecture
• Problem: API design approach
• Chosen: REST
• Alternatives: (none stated)
• Drivers: (none stated)
• Confidence: low

Best Practices

Context Capture

Always capture sufficient context for the ADR writer:

• What was the discussion about?
• Who was involved (if known)?
• What prompted the decision?

Merge Related Decisions

If multiple statements relate to the same decision, consolidate them:

• Combine alternatives from different mentions
• Aggregate drivers
• Use highest confidence level

Flag Ambiguity

When decisions are unclear or contradictory:

• Note the ambiguity in source_context
• Set confidence to low
• Include all interpretations if multiple exist

When to Use This Skill

• Analyzing session transcripts for ADR generation
• Reviewing conversation history for documentation
• Extracting decisions from design discussions
• Preparing input for ADR writing tools