ADR Discovery Command

Name: adr-discover
Rating: 76
Author: specvital

Discover and prioritize ADR candidates across the Specvital platform repositories.

User Input

text

$ARGUMENTS

You MUST consider the user input before proceeding (if not empty).

Overview

Scan all Specvital platform repositories (web, core, worker, infra) to discover architectural decisions that need ADR documentation.

Multi-Agent Collaboration Pipeline:

code

┌─────────────────────────────────────────────────────────────────┐
│                    ADR Discovery Pipeline                        │
├─────────────────────────────────────────────────────────────────┤
│  Phase 1: specvital-specialist                                   │
│    → Collect GitHub commit history                               │
│    → Identify missing ADRs compared to existing docs             │
│    → Summarize major changes per repository                      │
├─────────────────────────────────────────────────────────────────┤
│  Phase 2: product-strategist                                     │
│    → Prioritize ADR candidates                                   │
│    → Classify scope (Platform vs Repository level)              │
│    → Evaluate complexity and urgency                             │
├─────────────────────────────────────────────────────────────────┤
│  Phase 3: general-doc-writer                                     │
│    → Generate final list-up document                             │
│    → Format as table/list                                        │
│    → Write adr-candidates.md file                                │
└─────────────────────────────────────────────────────────────────┘

Input Interpretation

The command accepts natural language input. Analyze the user's input to extract:

Focus Keywords

If the user mentions specific topics, focus the search on those areas:

Examples:

•"AI spec document" → Focus on SpecView, AI integration, Gemini
•"pricing policy" → Focus on subscription, billing, plans
•"usage tracking" → Focus on quota, usage events, limits
•"domain hints" → Focus on parser hints, classification
•"worker separation" → Focus on binary separation, queue isolation

Repository Scope

Detect if the user wants to limit scope:

•"only core" / "core repository" → Scan only specvital/core
•"web related" → Scan only specvital/web
•"cross-cutting" / "platform level" → Focus on multi-repo decisions

Search Mode

User Intent	Behavior
Empty or broad ("find all ADR candidates")	Full scan of all repositories
Specific topic ("AI spec document related")	Focused search on that topic
Repository scope ("in worker repo")	Limit to specific repository
Mixed ("pricing in web and infra")	Combined scope + topic filter

Execution Flow

Phase 1: Discovery (specvital-specialist)

Task tool invocation:

code

Task(subagent_type="specvital-specialist", prompt="""
Discover ADR candidates by performing these tasks:

1. **Collect GitHub Commit History**
   - Repositories: specvital/core, specvital/worker, specvital/web, specvital/infra
   - Timeframe: Last 60 days (or as specified)
   - Use gh CLI or GitHub MCP

2. **Compare Against Existing ADRs**
   - Scan all ADRs under docs/en/adr/
   - Identify architectural decisions in commits that lack ADR documentation

3. **Output Format**
   For each candidate:
   - Title (English)
   - 제목 (한글) - 명사형 종결 사용
   - Related repositories
   - Related commits/PRs (if available)
   - Brief description (1-2 sentences)

**User Context**:
{user_input}

**Focus Areas** (if user specified topics):
{extracted_topics}
""")

Discovery Patterns:

•New technology stack adoption
•Architecture pattern changes
•API design decisions
•Data model changes
•Infrastructure configuration
•Business logic policies (pricing, usage limits, etc.)
•AI/ML related features

Phase 2: Strategic Analysis (product-strategist)

Task tool invocation:

code

Task(subagent_type="product-strategist", prompt="""
Prioritize and classify ADR candidates:

**Input**: ADR candidate list from Phase 1

**Evaluation Criteria**:

1. **Scope**
   - Platform Level: Affects multiple repositories
   - Repository Level: Contained within single repository

2. **Complexity**
   - High: Multiple components affected, migration required
   - Medium: Specific module impact
   - Low: Simple policy/pattern documentation

3. **Urgency**
   - Urgent: Required for features currently in development
   - Normal: Technical debt reduction, documentation clarity
   - Low: Nice to have

4. **Value**
   - Decision tracking value
   - Onboarding assistance
   - Future change reference

**Output**:
For each candidate: 4-dimension evaluation + overall priority score (P1/P2/P3)
""")

Phase 3: Documentation (general-doc-writer)

Task tool invocation:

code

Task(subagent_type="general-doc-writer", prompt="""
Generate ADR candidates list-up document:

**File**: adr-candidates.md at root path

**Language**: Korean (한글)

**Structure**:
# ADR 후보 목록

## 개요
- 분석 일자
- 분석 범위 (레포지토리, 기간)
- 총 후보 수

## P1 - 높은 우선순위 후보

| # | 제목 (한글) | Title (English) | 레포지토리 | 복잡도 | 긴급도 | 가치 | 근거 |
|---|-----------|-----------------|-----------|--------|--------|------|------|

## P2 - 중간 우선순위 후보

| # | 제목 (한글) | Title (English) | 레포지토리 | 복잡도 | 긴급도 | 가치 | 근거 |
|---|-----------|-----------------|-----------|--------|--------|------|------|

## P3 - 낮은 우선순위 후보

### Core 리포지토리
| # | 제목 (한글) | Title (English) | 복잡도 | 긴급도 | 가치 | 비고 |
|---|-----------|-----------------|--------|--------|------|------|

### Worker 리포지토리
| # | 제목 (한글) | Title (English) | 복잡도 | 긴급도 | 가치 | 비고 |
|---|-----------|-----------------|--------|--------|------|------|

### Web 리포지토리
| # | 제목 (한글) | Title (English) | 복잡도 | 긴급도 | 가치 | 비고 |
|---|-----------|-----------------|--------|--------|------|------|

### Infrastructure 리포지토리
| # | 제목 (한글) | Title (English) | 복잡도 | 긴급도 | 가치 | 비고 |
|---|-----------|-----------------|--------|--------|------|------|

## ADR 번호 할당 (제안)

| 우선순위 | ADR 번호 | 제목 (한글) | Title (English) |
|----------|----------|-----------|-----------------|

## 다음 단계
- [ ] 팀과 P1 우선순위 검토 및 확정
- [ ] P1 항목 ADR 작성자 지정

## 관련 커밋 요약

| 후보 | 커밋 |
|------|------|

**Style**:
- Concise table format
- Sort by priority
- ALL content in Korean (한글)
- Technical terms can include English in parentheses
""")

Output

Generated File

code

./adr-candidates.md   # List-up document at root path

Key Rules

Must Do

•Follow agent sequence: specvital-specialist → product-strategist → general-doc-writer
•Pass each agent's output as input to the next agent
•Include only items not duplicated with existing ADRs
•Apply clear priority criteria
•Generate adr-candidates.md at root path

Must Not Do

•Skip agent phases
•Generate candidates from speculation (must be based on actual commits/changes)
•Include existing ADR duplicates
•Use unclear priorities (must specify P1/P2/P3)

Usage Examples

bash

# Full scan - discover all ADR candidates
/adr-discover

# Topic-focused search
/adr-discover AI spec document, domain hints

# Repository-scoped search
/adr-discover web repository only, focusing on frontend patterns

# Combined scope and topic
/adr-discover pricing and usage tracking across all repos

# Natural language
/adr-discover Find everything related to the new subscription system we built

Related Commands

•/workflow-analyze: Feature analysis with implementation plan
•/document-business-rule: Business rule documentation
•/commit: Commit message generation

Related Agents

•specvital-specialist: Specvital platform expert
•product-strategist: Strategic analysis specialist
•general-doc-writer: Documentation specialist
•prompt-engineer: Prompt optimization (background)
•context-manager: Cross-session context preservation

Execution

Now execute the workflow:

•Parse $ARGUMENTS: Extract topics, repository scope, and search mode
•Phase 1: Invoke Task(specvital-specialist) with user context
•Phase 2: Invoke Task(product-strategist) with Phase 1 output
•Phase 3: Invoke Task(general-doc-writer) with Phase 2 output
•Report: Confirm adr-candidates.md file creation