AI Software Factory

Autonomous Engineering Operating System for multi-repository software delivery.

Transform Jira issues into tested, reviewed PRs across multiple repositories with one command.

Installation

Option 1: Private Marketplace (Recommended)

# In Claude Code
/plugin install em-software-factory@em-plugins

Skills are available immediately across all projects.

Option 2: Relative Path

# If the factory is in your workspace
claude --plugin-dir ../EM-AISoftwareFactory

# Or with full path
claude --plugin-dir /path/to/EM-AISoftwareFactory

Quick Start

Single Repository

# 1. Install the plugin (see Installation above)

# 2. Navigate to your repository
cd /path/to/your/repo

# 3. Start Claude Code
claude

# 4. Implement a Jira issue
/autonomous-implement SEMI-1413

# That's it! Creates plan → implements → tests → PR → updates Jira

Multi-Repository with Orchestrator

# 1. Install the plugin (see Installation above)

# 2. From workspace root
cd /path/to/em-aisoftwarefactory

# 3. Auto-routes to correct repository and injects knowledge
python3 -m orchestrator implement SEMI-1413

# 4. Follow the printed instructions in Claude Code

What Is This?

An Engineering OS that provides:

Single-Command SDLC - From Jira → tested PR
Multi-Repo Orchestration - Auto-routes issues to 5 repositories
Knowledge-Driven - Applies repo-specific patterns automatically
Quality Enforced - 80% coverage, air-gapped, foundations standards
80% Autonomous - Strategic checkpoints only (plan, PR)

Documentation

Core Guides

Guide	Description
Quickstart	Get started
Orchestrator Usage	Single & multi-repo
Skills Reference	All available skills
Knowledge System	Architecture, ADRs, patterns

Documentation

Doc	Purpose
Foundations Standards	Air-gapped, DoD, engineering principles
Orchestrator Guide	Complete orchestrator usage
Complete Docs	Full documentation index

Orchestrator Usage

The orchestrator provides workspace-level automation with repository routing and knowledge injection.

Single Repository

Direct skill invocation (no orchestrator):

# 1. Navigate to repository
cd /path/to/your/repo

# 2. Start Claude Code (plugin already installed)
claude

# 3. Run autonomous-implement
/autonomous-implement SEMI-1413

What happens:

Fetches SEMI-1413 from Jira
Creates branch from main
Researches em-semi codebase
Creates implementation plan
Generates tests from acceptance criteria
Implements solution
Validates with tests
Creates PR
Updates Jira

Limitations:

No repository-specific knowledge injection
No Foundations standards enforcement
Manual repository selection

Multi-Repository with Orchestrator

Orchestrator-based (recommended for production):

# Step 1: Test routing
python3 -m orchestrator test SEMI-1413

# Output:
#  Routed SEMI-1413 → semi
#  Loaded knowledge: architecture, patterns, conventions

# Step 2: Generate implementation instructions
python3 -m orchestrator implement SEMI-1413

# Output:
#  Knowledge context prepared: /tmp/knowledge_context_xyz.md
#  Repository: /path/to/your/repo
# 
# To execute:
#   cd /path/to/your/repo
#   claude
#   /autonomous-implement SEMI-1413 --context-file /tmp/knowledge_context_xyz.md

# Step 3: Follow the instructions
# (Opens Claude Code and runs the skill with knowledge context)

What the orchestrator adds:

Auto-routing: SEMI-1413 → em-semi (via Jira component)
Knowledge injection: em-semi architecture/patterns
Foundations enforcement: Air-gapped, 80% coverage, DoD
Standards compliance: Automatic validation

Routing logic:

# workspace.yaml
jira:
  component_mapping:
    Semi: semi                 # SEMI-* issues → em-semi
    Runtime: runtime           # RT-* issues → em-runtime
    UI: runtime-ui             # UI-* issues → em-runtime-ui
    Talk2Data: talk2data       # T2D-* issues → em-talk2data
    "Data Readiness": data-readiness

Batch Multi-Repository

# Implement multiple issues across repositories
python3 -m orchestrator multi-repo SEMI-1413 T2D-890 RT-567

# Output:
#  SEMI-1413 → em-semi
#  T2D-890 → em-talk2data
#  RT-567 → em-runtime
# 
# Generated 3 instruction sets (see /tmp/orchestrator_instructions_*.sh)

Skills Reference

Autonomous Skills (End-to-End)

|-------|-------------|----------|------| | /autonomous-implement | Full SDLC: Jira → PR | 80% (2 checkpoints) | 10 min | | /autonomous-sprint | Full sprint automation | 80% (3 checkpoints) | 25 min | | /batch-implement | Parallel batch processing | 80% (2 checkpoints) | 15 min |

Core Development Skills

Skill	Description	Autonomy
`/create-plan`	Generate implementation plan from Jira	100%
`/implement-plan`	Execute phased implementation	95% (1 checkpoint)
`/eval-generator`	Generate tests from acceptance criteria	100%
`/create-pr`	Create pull request	95% (1 checkpoint)
`/code-review`	Automated code review	95% (1 checkpoint)
`/commit`	Smart commit organization	100%

Research & Planning

Skill	Description
`/research-codebase`	Semantic code search
`/jira-to-branches`	Batch branch creation from JQL
`/jira-update`	Update Jira issue status

See docs/guides/QUICKSTART.md for detailed skill usage.

Knowledge System

The knowledge system extracts and applies repository-specific context automatically.

What Gets Extracted

knowledge/
├── repositories/
│   ├── semi/
│   │   ├── architecture.md      ← em-semi architecture
│   │   ├── patterns.md          ← Coding patterns (context managers, etc.)
│   │   ├── conventions.md       ← Style guide (imports, type hints)
│   │   └── dependencies.md      ← Package management approach
│   ├── talk2data/
│   │   └── ...                  ← talk2data knowledge
│   └── runtime/
│       └── ...                  ← Runtime knowledge
└── foundations/
    ├── standards.md             ← Air-gapped requirements, DoD
    └── overview.md              ← Engineering principles

Automatic Sync

# Runs automatically before orchestrator
./sync_knowledge.sh

# Or manually
./sync_knowledge.sh

# Only re-extracts if repository changed (git diff check)

Pointing to Specific Knowledge

In Code

Knowledge is automatically injected via --context-file:

/autonomous-implement SEMI-1413 --context-file /tmp/knowledge_context.md

The context file contains:

# Repository Knowledge Context

## Architecture
[Complete em-semi architecture patterns]

## Coding Patterns
[Context managers, async/await, type hints]

## Conventions
[Import style, naming, docstrings]

## Foundations Standards
### Air-Gapped Requirements (CRITICAL)
- NO cloud APIs (AWS, GCP, Azure)
...

Referencing ADRs

Option 1: Add to knowledge extraction

# Edit knowledge/repositories/semi/architecture.md
# Add section:

## Architecture Decision Records

### ADR-001: Event Sourcing for Workflow State
- Decision: Use event sourcing for all workflow state changes
- Rationale: Enables time-travel debugging and audit trails
- File: docs/adr/001-event-sourcing.md

### ADR-002: DuckDB for Analytics
- Decision: Embed DuckDB for local analytics queries
- Rationale: Air-gapped requirement, zero-dependency
- File: docs/adr/002-duckdb.md

Option 2: Link in workspace.yaml

# workspace.yaml
repositories:
  - name: semi
    path: em-semi
    knowledge:
      adrs:
        - path: docs/adr/001-event-sourcing.md
          title: Event Sourcing for Workflow State
        - path: docs/adr/002-duckdb.md
          title: DuckDB for Analytics

Option 3: Direct reference in prompts

/autonomous-implement SEMI-1413

# Then mention in plan approval:
"Ensure this follows ADR-002 (DuckDB for analytics)"

Workspace Configuration

Repository Mapping

# workspace.yaml
workspace:
  root: /path/to/your/workspace

repositories:
  - name: semi
    path: em-semi
    jira_component: Semi
    github: EmergenceAI/em-semi
    
  - name: talk2data
    path: em-talk2data
    jira_component: Talk2Data
    github: EmergenceAI/em-talk2data

jira:
  component_mapping:
    Semi: semi                    # Routes SEMI-* → em-semi
    Talk2Data: talk2data          # Routes T2D-* → em-talk2data
    Runtime: runtime
    UI: runtime-ui
    "Data Readiness": data-readiness

Adding a New Repository

# 1. Add to workspace.yaml
# 2. Extract knowledge
./sync_knowledge.sh

# 3. Test routing
python3 -m orchestrator test YOUR-ISSUE-123

# 4. Verify knowledge loaded
# Should show: "Loaded knowledge for your-repo"

Requirements

Claude Code v2.1.81+ (for skills)
Python 3.8+ (for orchestrator)
Git (for repositories)
Jira MCP (optional, for real Jira data)

Jira MCP Setup (Optional)

# Set environment variables
export JIRA_URL=https://your-company.atlassian.net
export JIRA_EMAIL=your-email@company.com
export JIRA_API_TOKEN=your_api_token

# Test connection
/mcp

Without Jira MCP: Uses mock data (still works for testing)

Common Workflows

Implement Single Issue

# Quick (no orchestrator)
cd /path/to/your/repo && /autonomous-implement SEMI-1413

# Full (with knowledge)
python3 -m orchestrator implement SEMI-1413
# Follow instructions

Implement Sprint

# All issues in sprint
/autonomous-sprint --jql "sprint in openSprints()"

# Specific filter
/autonomous-sprint --jql "filter = 17150"

Research First

# Understand codebase before implementing
/research-codebase "How does wafer processing work?"

# Then implement
/autonomous-implement SEMI-1413

Troubleshooting

"Can't find repository"

# Check workspace.yaml
cat workspace.yaml | grep -A 5 "repositories:"

# Verify path exists
ls -la /path/to/your/repo

"No knowledge found"

# Sync knowledge
./sync_knowledge.sh

# Check extracted
ls -la knowledge/repositories/semi/

"Routing to wrong repository"

# Test routing
python3 -m orchestrator test SEMI-1413

# Check Jira component mapping
cat workspace.yaml | grep -A 10 "component_mapping:"

"Branch created from wrong base"

Fixed in latest version! All branches now created from main automatically.

See CRITICAL_FIX_BRANCHING.md for details.

Next Steps

[5 min] Read Quickstart Guide
[10 min] Try /autonomous-implement on a real issue
[15 min] Set up orchestrator for your workspace
[Optional] Configure Jira MCP for real data

Support

Documentation: docs/README.md
Issues: GitHub Issues
Internal: #ai-software-factory Slack

Name		Name	Last commit message	Last commit date
Latest commit History 31 Commits
.claude-plugin		.claude-plugin
.claude		.claude
adapters		adapters
docs		docs
hooks		hooks
knowledge		knowledge
orchestrator		orchestrator
prompts		prompts
scripts		scripts
skills		skills
templates		templates
workflows		workflows
.DS_Store		.DS_Store
.gitignore		.gitignore
.mcp.json		.mcp.json
.sync_state.json		.sync_state.json
EM-AISoftwareFactory.code-workspace		EM-AISoftwareFactory.code-workspace
README.md		README.md
craft-mvp-epics-sample.csv		craft-mvp-epics-sample.csv
extract_knowledge.sh		extract_knowledge.sh
sync_knowledge.sh		sync_knowledge.sh
test-epic-small.csv		test-epic-small.csv
test_adapter.py		test_adapter.py
test_orchestrator.py		test_orchestrator.py
test_orchestrator_with_sync.py		test_orchestrator_with_sync.py
test_subprocess_claude.py		test_subprocess_claude.py
workspace.yaml		workspace.yaml

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AI Software Factory

Installation

Option 1: Private Marketplace (Recommended)

Option 2: Relative Path

Quick Start

Single Repository

Multi-Repository with Orchestrator

What Is This?

Documentation

Core Guides

Documentation

Orchestrator Usage

Single Repository

Multi-Repository with Orchestrator

Batch Multi-Repository

Skills Reference

Autonomous Skills (End-to-End)

Core Development Skills

Research & Planning

Knowledge System

What Gets Extracted

Automatic Sync

Pointing to Specific Knowledge

In Code

Referencing ADRs

Workspace Configuration

Repository Mapping

Adding a New Repository

Requirements

Jira MCP Setup (Optional)

Common Workflows

Implement Single Issue

Implement Sprint

Research First

Troubleshooting

"Can't find repository"

"No knowledge found"

"Routing to wrong repository"

"Branch created from wrong base"

Next Steps

Support

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