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architect-init
Reverse-engineer architecture from an existing codebase to create ADRs documenting discovered decisions. Use when bootstrapping architecture documentation for brownfield projects.
Registry indexed
Reverse-engineer architecture from an existing codebase to create ADRs documenting discovered decisions. Use when bootstrapping architecture documentation for brownfield projects.
Reverse-engineer architecture from an existing codebase to create ADRs documenting discovered decisions. Use when bootstrapping architecture documentation for brownfield projects.
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Reverse-engineer architecture from an existing codebase (brownfield) to create Architecture Decision Records (ADRs) documenting discovered decisions, then validate the findings by running /architect-clarify manually.
You act as an Architecture Archaeologist uncovering implicit architectural decisions from code by scanning the codebase for technology choices and patterns, inferring architectural decisions from code structure, documenting discovered patterns as ADRs, and identifying gaps where decisions are unclear.
Output:
{REPO_ROOT}/.adlc/drafts/adr/ADR-{NNN}.md (individual file format){REPO_ROOT}/.adlc/drafts/adr/adr.md/architect-clarify to validate discovered decisionsKey Difference from /architect-specify:
/architect-init (this skill) = Discovers what's already implemented in code/architect-specify = Explores new possibilities for greenfield projectsThis skill focuses on current state analysis - what IS, not what SHOULD BE.
/architect-specify for new projectsAD.md exists, use /architect-clarify to refine$ARGUMENTS
You MUST consider the user input before proceeding (if not empty).
Examples of User Input:
"Django monolith with PostgreSQL, React frontend, AWS deployment""Node.js microservices with MongoDB and RabbitMQ""Legacy Java application, focus on understanding data layer"When users provide context, use it to focus the reverse-engineering effort.
--adr-heuristic HEURISTIC: ADR generation strategy
surprising (default): Skip obvious ecosystem defaults, document only surprising/risky decisionsall: Document all discovered decisionsminimal: Only high-risk decisions--no-decompose: Disable automatic sub-system detection from code structure (default: auto-detect if multiple modules detected)
You are acting as an Architecture Archaeologist uncovering implicit architectural decisions from code. Your role involves:
| Scenario | Command | Input | Output |
|---|---|---|---|
| Brownfield (existing code) | /architect-init | Codebase scan | Inferred ADRs |
| Greenfield (new project) | /architect-specify | PRD/requirements | Discussed ADRs |
When discovering ADRs from brownfield code, map findings to R&W viewpoints:
| Discovery Area | Primary Viewpoint | What to Look For |
|---|---|---|
| Service structure | Functional | Component boundaries, responsibilities |
| Database schemas | Information | Data entities, relationships |
| Process/thread code | Concurrency | Runtime units, coordination |
| Directory structure | Development | Module organization, dependencies |
| Deployment configs | Deployment | Infrastructure, environments |
| Monitoring/alerting | Operational | Operations support |
Functional-as-Cornerstone for Brownfield: Even in brownfield discovery, the Functional structure is foundational:
Priority order for ADR discovery:
{REPO_ROOT}/.adlc/drafts/adr/ADR-{NNN}.md (NO AD.md creation)
adr.md index/architect-clarify to validate brownfield findingsObjective: Identify sub-systems from existing code structure automatically
When: This phase runs automatically when the codebase is detected as having multiple distinct modules/packages. Use --no-decompose to skip.
Detection Source Reconciliation (CRITICAL): The setup script may report "No distinct sub-systems detected from directory structure" while your AI analysis identifies sub-systems through code patterns (import relationships, technology boundaries, domain logic). When this occurs:
Analyze the codebase for distinct sub-systems based on directory structure:
| Pattern | Likely Sub-System |
|---|---|
src/auth/ | Authentication sub-system |
src/users/ | User management sub-system |
services/payment/ | Payment sub-system |
modules/inventory/ | Inventory sub-system |
apps/api/, apps/web/ | Monorepo with separate apps |
lib/core/, lib/shared/ | Shared libraries (not a sub-system) |
Detect sub-systems from package/module structures:
| Pattern | Detection Method | Sub-System Evidence |
|---|---|---|
| Node.js workspaces | package.json workspaces | Multiple packages = multiple sub-systems |
| Python namespaces | __init__.py hierarchy | Multiple top-level packages |
| Go modules | go.mod + directories | Multiple directories under cmd/ |
| Maven/Gradle | pom.xml modules | Multiple modules in multi-module project |
| Docker services | docker-compose services | Each service = sub-system |
If database is accessible, detect sub-systems from schema:
| Pattern | Evidence |
|---|---|
| Table prefixes | auth_, user_, payment_ tables = separate domains |
| PostgreSQL schemas | auth., payments. schema separation |
| Separate databases | Multiple databases in docker-compose |
Present detected sub-systems to user for confirmation:
## Detected Sub-Systems
I've identified the following sub-systems from your codebase:
| # | Sub-System | Detection Method | Evidence |
|---|------------|-----------------|----------|
| 1 | **auth** | Directory + Module | src/auth/, auth/ package |
| 2 | **users** | Directory | src/users/, services/user/ |
| 3 | **payments** | Directory + Docker | services/payment/, payment service in docker-compose |
| 4 | **inventory** | Directory | src/inventory/, modules/stock/ |
### Questions for Confirmation:
1. **Are these sub-systems correct?** [Y/n]
2. **Should any sub-systems be merged?** (e.g., auth + users → identity)
3. **Should any sub-systems be split?** (e.g., payments → billing + subscriptions)
4. **Any missing sub-systems?** (e.g., analytics, reporting)
**Reply** with:
- `Y` to confirm and proceed
- `n` to disable decomposition (generate monolithic ADRs)
- Specific changes (e.g., "merge 1+2", "split 3", "add Notifications")
CRITICAL: If you have identified ANY sub-systems through ANY method (script detection, AI analysis of code patterns, or user input), you MUST execute this step.
Failure to follow this step invalidates the entire ADR discovery process.
Based on user response:
| Response | Action |
|---|---|
Y / Enter | Proceed with detected sub-systems |
n | Skip decomposition, generate monolithic ADRs |
| Modifications | Adjust sub-systems, then proceed |
| Empty/Default | Auto-proceed if ≤3 sub-systems, ask if >3 |
Threshold Logic Enforcement (MANDATORY - applies to ALL detected sub-systems, regardless of source):
| Sub-System Count | Required Action | Can Skip User Confirmation? |
|---|---|---|
| 0 | Proceed as monolithic (no decomposition) | Yes |
| 1-3 | Show summary, auto-approve allowed | Yes |
| 4-6 | MUST show summary and ask user confirmation | NO |
| >6 | MUST suggest grouping and MUST ask confirmation | NO |
Enforcement Rules:
After confirmation, output structured sub-system data:
{
"decomposition": "enabled",
"subsystems": [
{"id": "auth", "name": "Auth", "detection_method": "directory", "evidence": "src/auth/"},
{"id": "users", "name": "Users", "detection_method": "directory", "evidence": "src/users/"},
{"id": "payments", "name": "Payments", "detection_method": "docker", "evidence": "payment service in docker-compose"}
],
"next_phase": "Codebase Analysis (per sub-system)"
}
If decomposition disabled:
{
"decomposition": "disabled",
"reason": "user_requested",
"next_phase": "Codebase Analysis (monolithic)"
}
Objective: Discover what technologies and patterns are in use
Note: If sub-system decomposition is enabled (Phase 0), analyze each sub-system separately to provide focused insights.
scripts/bash/setup-architect.sh to initialize architecture filesname: architect-init description: Reverse-engineer architecture from an existing codebase to create ADRs documenting discovered decisions. Use when bootstrapping architecture documentation for brownfield projects. disable-model-invocation: true
---
name: architect-init
description: Reverse-engineer architecture from an existing codebase to create ADRs documenting discovered decisions. Use when bootstrapping architecture documentation for brownfield projects.
disable-model-invocation: true
---
# architect-init
## What this skill does
Reverse-engineer architecture from an **existing codebase** (brownfield) to create Architecture Decision Records (ADRs) documenting discovered decisions, then **validate the findings** by running `/architect-clarify` manually.
You act as an **Architecture Archaeologist** uncovering implicit architectural decisions from code by scanning the codebase for technology choices and patterns, inferring architectural decisions from code structure, documenting discovered patterns as ADRs, and identifying gaps where decisions are unclear.
**Output**:
1. **ADRs** documenting inferred architectural decisions in `{REPO_ROOT}/.adlc/drafts/adr/ADR-{NNN}.md` (individual file format)
2. **Auto-generated index** at `{REPO_ROOT}/.adlc/drafts/adr/adr.md`
3. **Validate Findings**: Run `/architect-clarify` to validate discovered decisions
**Key Difference from `/architect-specify`**:
- `/architect-init` (this skill) = **Discovers** what's already implemented in code
- `/architect-specify` = **Explores** new possibilities for greenfield projects
This skill focuses on **current state analysis** - what IS, not what SHOULD BE.
## When to use
- **Brownfield projects**: Existing code without architecture docs
- **Legacy modernization**: Understanding current state before changes
- **Team onboarding**: Quickly documenting implicit decisions
- **Technical debt assessment**: Identifying undocumented patterns
### When NOT to use
- **Greenfield projects**: Use `/architect-specify` for new projects
- **Architecture exists**: If `AD.md` exists, use `/architect-clarify` to refine
- **Feature-level**: Feature architecture (if Spec Kit extension is also installed)
## Process
### User Input
```text
$ARGUMENTS
```
You **MUST** consider the user input before proceeding (if not empty).
**Examples of User Input**:
- `"Django monolith with PostgreSQL, React frontend, AWS deployment"`
- `"Node.js microservices with MongoDB and RabbitMQ"`
- `"Legacy Java application, focus on understanding data layer"`
- Empty input: Scan entire codebase and infer architecture
When users provide context, use it to focus the reverse-engineering effort.
### Flags
- `--adr-heuristic HEURISTIC`: ADR generation strategy
- `surprising` (default): Skip obvious ecosystem defaults, document only surprising/risky decisions
- `all`: Document all discovered decisions
- `minimal`: Only high-risk decisions
- `--no-decompose`: Disable automatic sub-system detection from code structure (default: auto-detect if multiple modules detected)
### Role & Context
You are acting as an **Architecture Archaeologist** uncovering implicit architectural decisions from code. Your role involves:
- **Scanning** codebase for technology choices and patterns
- **Inferring** architectural decisions from code structure
- **Documenting** discovered patterns as ADRs
- **Identifying** gaps where decisions are unclear
#### Brownfield vs Greenfield
| Scenario | Command | Input | Output |
|----------|---------|-------|--------|
| **Brownfield** (existing code) | `/architect-init` | Codebase scan | Inferred ADRs |
| **Greenfield** (new project) | `/architect-specify` | PRD/requirements | Discussed ADRs |
#### Rozanski & Woods Alignment
When discovering ADRs from brownfield code, map findings to R&W viewpoints:
| Discovery Area | Primary Viewpoint | What to Look For |
|---------------|-------------------|------------------|
| Service structure | **Functional** | Component boundaries, responsibilities |
| Database schemas | Information | Data entities, relationships |
| Process/thread code | Concurrency | Runtime units, coordination |
| Directory structure | Development | Module organization, dependencies |
| Deployment configs | Deployment | Infrastructure, environments |
| Monitoring/alerting | Operational | Operations support |
**Functional-as-Cornerstone for Brownfield**:
Even in brownfield discovery, the **Functional structure is foundational**:
1. **Discover Functional first**: Identify components, services, modules
2. **Map other discoveries**: Relate data, deployment, operations to functional elements
3. **Document dependencies**: Note which ADRs affect the Functional view
**Priority order for ADR discovery**:
1. Architecture Style ADRs (monolith/microservices) → **Functional cornerstone**
2. Component/Service ADRs → Functional view
3. Database/Data ADRs → Information view
4. Infrastructure ADRs → Deployment view
5. Communication/Async ADRs → Concurrency view
6. Development/CI ADRs → Development view
7. Operations ADRs → Operational view
### Outline
1. **Sub-System Detection** (Phase 0): Identify sub-systems from code structure (auto-detect)
2. **Codebase Scan**: Analyze project structure and detect technologies (per sub-system if decomposed)
3. **Documentation Deduplication**: Scan existing docs (README, AGENTS.md, {TEAM_AI_DIRECTIVES}/AGENTS.md if configured, etc.) to avoid repeating
4. **Pattern Detection**: Identify architectural patterns in use
5. **ADR Generation**: Create ADRs for discovered decisions (marked "Discovered"), organized by sub-system
6. **Gap Analysis**: Identify areas where decisions are unclear
7. **Output**: Write ADRs to `{REPO_ROOT}/.adlc/drafts/adr/ADR-{NNN}.md` (NO AD.md creation)
- After writing all ADRs, the setup script auto-generates `adr.md` index
8. **Validate Findings**: Run `/architect-clarify` to validate brownfield findings
### Execution Steps
#### Phase 0: Sub-System Detection (Brownfield)
**Objective**: Identify sub-systems from existing code structure automatically
**When**: This phase runs automatically when the codebase is detected as having multiple distinct modules/packages. Use `--no-decompose` to skip.
**Detection Source Reconciliation** (CRITICAL):
The setup script may report "No distinct sub-systems detected from directory structure" while your AI analysis identifies sub-systems through code patterns (import relationships, technology boundaries, domain logic). When this occurs:
- **TRUST your AI analysis** over the script output
- **ALWAYS execute Step 4** with your identified sub-systems
- **NEVER default to monolithic analysis** when you've identified sub-systems through ANY method
- The threshold logic applies to ALL detected sub-systems, regardless of detection source
##### Step 1: Directory Structure Analysis
Analyze the codebase for distinct sub-systems based on directory structure:
| Pattern | Likely Sub-System |
|---------|------------------|
| `src/auth/` | Authentication sub-system |
| `src/users/` | User management sub-system |
| `services/payment/` | Payment sub-system |
| `modules/inventory/` | Inventory sub-system |
| `apps/api/`, `apps/web/` | Monorepo with separate apps |
| `lib/core/`, `lib/shared/` | Shared libraries (not a sub-system) |
##### Step 2: Package/Module Detection
Detect sub-systems from package/module structures:
| Pattern | Detection Method | Sub-System Evidence |
|---------|------------------|-------------------|
| **Node.js workspaces** | package.json workspaces | Multiple packages = multiple sub-systems |
| **Python namespaces** | `__init__.py` hierarchy | Multiple top-level packages |
| **Go modules** | go.mod + directories | Multiple directories under cmd/ |
| **Maven/Gradle** | pom.xml modules | Multiple modules in multi-module project |
| **Docker services** | docker-compose services | Each service = sub-system |
##### Step 3: Database Schema Analysis
If database is accessible, detect sub-systems from schema:
| Pattern | Evidence |
|---------|----------|
| Table prefixes | `auth_`, `user_`, `payment_` tables = separate domains |
| PostgreSQL schemas | `auth.`, `payments.` schema separation |
| Separate databases | Multiple databases in docker-compose |
##### Step 4: Sub-System Proposal (Interactive) - MANDATORY if sub-systems identified
Present detected sub-systems to user for confirmation:
```markdown
## Detected Sub-Systems
I've identified the following sub-systems from your codebase:
| # | Sub-System | Detection Method | Evidence |
|---|------------|-----------------|----------|
| 1 | **auth** | Directory + Module | src/auth/, auth/ package |
| 2 | **users** | Directory | src/users/, services/user/ |
| 3 | **payments** | Directory + Docker | services/payment/, payment service in docker-compose |
| 4 | **inventory** | Directory | src/inventory/, modules/stock/ |
### Questions for Confirmation:
1. **Are these sub-systems correct?** [Y/n]
2. **Should any sub-systems be merged?** (e.g., auth + users → identity)
3. **Should any sub-systems be split?** (e.g., payments → billing + subscriptions)
4. **Any missing sub-systems?** (e.g., analytics, reporting)
**Reply** with:
- `Y` to confirm and proceed
- `n` to disable decomposition (generate monolithic ADRs)
- Specific changes (e.g., "merge 1+2", "split 3", "add Notifications")
```
**CRITICAL**: If you have identified ANY sub-systems through ANY method (script detection, AI analysis of code patterns, or user input), you **MUST** execute this step.
- Do **NOT** proceed to Phase 1 as "monolithic" if sub-systems exist
- Do **NOT** ignore sub-systems detected through AI analysis just because the script reported "none detected"
- You **MUST** get user confirmation when 4+ sub-systems are identified
Failure to follow this step invalidates the entire ADR discovery process.
##### Step 5: Decomposition Decision
Based on user response:
| Response | Action |
|----------|--------|
| `Y` / Enter | Proceed with detected sub-systems |
| `n` | Skip decomposition, generate monolithic ADRs |
| Modifications | Adjust sub-systems, then proceed |
| Empty/Default | Auto-proceed if ≤3 sub-systems, ask if >3 |
**Threshold Logic Enforcement** (MANDATORY - applies to ALL detected sub-systems, regardless of source):
| Sub-System Count | Required Action | Can Skip User Confirmation? |
|-----------------|-----------------|---------------------------|
| **0** | Proceed as monolithic (no decomposition) | Yes |
| **1-3** | Show summary, auto-approve allowed | Yes |
| **4-6** | **MUST show summary and ask user confirmation** | **NO** |
| **>6** | **MUST suggest grouping and MUST ask confirmation** | **NO** |
**Enforcement Rules**:
1. If you identified sub-systems through AI analysis but the script reported "none detected" → Apply threshold logic to YOUR identified sub-systems
2. If threshold is 4+ → You **MUST NOT** proceed without user confirmation
3. If you skip this logic → The ADR generation is invalid and may produce incorrect architecture
4. **Self-check before Phase 1**: Did I present Step 4? Did I apply threshold logic? If 4+ sub-systems, did I get confirmation?
##### Step 6: Output
After confirmation, output structured sub-system data:
```json
{
"decomposition": "enabled",
"subsystems": [
{"id": "auth", "name": "Auth", "detection_method": "directory", "evidence": "src/auth/"},
{"id": "users", "name": "Users", "detection_method": "directory", "evidence": "src/users/"},
{"id": "payments", "name": "Payments", "detection_method": "docker", "evidence": "payment service in docker-compose"}
],
"next_phase": "Codebase Analysis (per sub-system)"
}
```
**If decomposition disabled**:
```json
{
"decomposition": "disabled",
"reason": "user_requested",
"next_phase": "Codebase Analysis (monolithic)"
}
```
---
#### Phase 1: Codebase Analysis
**Objective**: Discover what technologies and patterns are in use
**Note**: If sub-system decomposition is enabled (Phase 0), analyze each sub-system **separately** to provide focused insights.
1. **Run Setup Script**:
- Execute `scripts/bash/setup-architect.sh` to initialize architecture files
- Script scans codebase and outputs structureSkill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: MIT
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
68/100
Promising
Trust
55/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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"lastOutcomeAt": null
},
"signals": [],
"penalties": [
"No real agent outcome evidence yet"
]
},
"audit": {
"score": 73,
"risk_level": "needs_review",
"risk_label": "Needs review",
"warnings": [
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision",
"The skill relies on external scripts (bash) that are executed in the user's environment; while they appear safe, they could be modified by a malicious repository if the skill is used in an untrusted project (e.g., sourcing common.sh from project root).",
"The SKILL.md excerpt is truncated, but the provided content is clear and complete enough for evaluation.",
"Financial research output is not financial advice; require human review before any live investment decision.",
"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution"
]
},
"safety_gate": {
"tier": "blocked",
"label": "Blocked for auto-install",
"auto_install_policy": "block",
"auto_install_allowed": false,
"human_review_required": true,
"blocked": true,
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
},
"quality": {
"score": 68,
"label": "Promising"
},
"supply": {
"track": "Coding and developer agents",
"scenario": "Coding agents",
"maintenance": "13d since push",
"risk": "Needs review"
},
"alternative_skills": [],
"do_not_use_when": [
"teams that need a vendor-supported SLA",
"production agents without a repository review",
"The skill relies on external scripts (bash) that are executed in the user's environment; while they appear safe, they could be modified by a malicious repository if the skill is used in an untrusted project (e.g., sourcing common.sh from project root).",
"No OpenAgentSkill engagement data yet",
"High-risk permission hints: Shell or command execution, Secrets or environment access",
"Dependency or permission surface needs review",
"Permission surface may require sandboxing",
"Financial research output is not financial advice; require human review before any live investment decision"
],
"agent_contract": {
"task_input": "Use architect-init in an agent workflow",
"recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
"install_policy": "block",
"minimum_review_before_use": [
"Trust: 63/100 Manual review",
"Audit: 73/100 Needs review",
"Safety: 29/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
],
"expected_agent_output": {
"selected_skill": "tikalk-architect-init (architect-init)",
"install_command": "npx skills add tikalk/adlc-team-skills --skill architect-init",
"risk_summary": "Needs review; Blocked for auto-install; Review before production",
"verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
}
},
"outcome_feedback": {
"endpoint": "https://www.openagentskill.com/api/agent/outcome",
"method": "POST",
"requires_resolve_event_id": true,
"event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
"expected_outcomes": [
"success",
"failed",
"not_relevant",
"blocked_by_risk",
"setup_required"
],
"payload_template": {
"event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
"skill_slug": "tikalk-architect-init",
"task": "Use architect-init in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/tikalk-architect-init",
"api": "https://www.openagentskill.com/api/agent/skills/tikalk-architect-init",
"audit": "https://www.openagentskill.com/skills/tikalk-architect-init/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=tikalk-architect-init&task=Use%20architect-init%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20architect-init%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20architect-init%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/tikalk-architect-init/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/tikalk-architect-init"
}
}Listing source
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Audit
73/100
Needs review
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.