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

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

Hard gates

Run these in order after the workflow above and before returning output. Each step has an objective pass condition.

Explicit [ADR] inventory — Capture every [ADR] segment from the full source (verbatim in working notes). Pass: a second pass over the same source adds no new [ADR] blocks.
De-duplicate — Merge or drop inferred rows that repeat an explicit [ADR] decision (see Merge Related Decisions). Pass: at most one row per distinct decision.
Schema validity — Serialized JSON matches Output Format and Field Definitions. Pass: parse succeeds; every decisions[] item has non-empty title, problem, chosen_option; confidence ∈ {high,medium,low}; alternatives_discussed is an array (use [] if none); other optional fields per table.
Low-confidence audit — For any confidence: "low", source_context states what was missing, weak, or contradictory. Pass: a reader can see why the rating is not higher.

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