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