git

Fusion uses a single MCP tool entry point with infinite skill commands:

```python

@omni("git.status") # Get git status

@omni("git.commit", {...}) # Commit with cog validation

@omni("file.read", {...}) # Read file

@omni("help") # Show all skills

```

| Pattern | Example | Dispatches To |

| --------------- | ------------ | ------------------ |

| skill.command | git.status | git.git_status() |

| skill | git | Shows skill help |

Omni can now download and install skills from Git repositories at runtime, enabling true capability expansion:

```bash

# Install a skill from GitHub

omni skill install https://github.com/omni-dev/skill-pandas

# Install specific version

omni skill install https://github.com/omni-dev/skill-docker --version v2.1.0

# Update an installed skill

omni skill update pandas-expert

# List all installed skills

omni skill list

# Show skill details

omni skill info pandas-expert

```

Skill Management Commands

| Command | Description |

| -------------------------------------------------------------- | ---------------------------------- |

| omni skill install | Install skill from Git URL |

| omni skill install --name | Install with custom name |

| omni skill install --version | Install specific branch/tag/commit |

| omni skill update | Update skill to latest |

| omni skill update --strategy stash\|abort\|overwrite | Update with conflict handling |

| omni skill list | List all installed skills |

| omni skill info | Show skill manifest and lockfile |

How It Works

1. Clone/Update: Uses GitPython + subprocess for reliable Git operations
2. Sparse Checkout: Supports monorepo subdirectory installation
3. Dependency Detection: Prevents circular dependencies during install
4. Lockfile: Generates .omni-lock.json for reproducible installs
5. Dirty Handling: Stashes local changes, pulls, then pops (stash strategy)

Omni supports cascading templates with "User Overrides > Skill Defaults" pattern:

```bash

# Check skill structure

omni skill check # Check all skills

omni skill check git # Check specific skill

omni skill check git --examples # With structure examples

# Manage templates (cascading pattern)

omni skill templates list git # List templates with source

omni skill templates eject git commit_message.j2 # Copy default to user dir

omni skill templates info git commit_message.j2 # Show template content

# Create new skill from template

omni skill create my-skill --description "My new skill"

```

Cascading Template Pattern

```

# Priority 1: User Overrides (assets/templates/{skill}/)

assets/templates/git/

├── commit_message.j2 # Custom template (highest priority)

# Priority 2: Skill Defaults (assets/skills/{skill}/templates/)

assets/skills/git/templates/

├── commit_message.j2 # Skill default (fallback)

├── workflow_result.j2

└── error_message.j2

```

Skill Structure Validation

```

🔍 git

✅ Valid: True

📊 Score: 100.0%

📁 Location: assets/skills/git

├── SKILL.md # Required

├── scripts/ # Required (commands.py with @skill_command)

├── templates/ # Optional (cascading)

└── tests/ # Optional (zero-config)

```

Omni can dynamically discover and install skills when you need capabilities not currently loaded:

```python

# Discover skills matching a query

@omni("skill.discover", {"query": "data analysis", "limit": 5})

# Get task-based suggestions

@omni("skill.suggest", {"task": "analyze pcap file"})

# Install and load a skill

@omni("skill.jit_install", {"skill_id": "network-analysis"})

# List all known skills

@omni("skill.list_index")

```

Workflow: Acquiring a New Capability

```

User: "Analyze this pcap file"

You: @omni("skill.suggest", {"task": "analyze pcap file"})

→ Found: network-analysis (keywords: pcap, network, wireshark)

@omni("skill.jit_install", {"skill_id": "network-analysis"})

→ ✅ Installed and loaded!

Ready to analyze your pcap file.

```

Available Skills in Index (20 skills)

| Skill ID | Description | Keywords |

| ------------------ | ----------------------------------------- | ---------------------------------- |

| pandas-expert | Advanced pandas data manipulation | pandas, dataframe, data-analysis |

| docker-ops | Docker container management | docker, containers, kubernetes |

| network-analysis | PCAP analysis and network troubleshooting | pcap, network, wireshark |

| ml-pytorch | Machine learning with PyTorch | pytorch, ml, deep-learning |

| aws-cloud | AWS cloud services management | aws, ec2, s3, lambda |

| database-sql | SQL database operations | sql, postgres, mysql, query |

| video-processing | FFmpeg video transcoding | video, ffmpeg, transcoding |

| rust-systems | Rust systems programming | rust, wasm, systems |

| graphql-api | GraphQL schema design | graphql, api, schema |

| terraform-infra | Infrastructure as Code | terraform, infrastructure, iac |

| security-audit | Security vulnerability scanning | security, vulnerability, audit |

| regex-master | Regular expression parsing | regex, pattern, text |

| http-client | HTTP requests and API testing | http, api, rest, curl |

| git-advanced | Advanced git operations | git, rebase, bisect, workflow |

| shell-scripting | Bash/shell scripting | bash, shell, scripting, automation |

| cryptography | Encryption and cryptographic ops | crypto, encryption, hash |

| testing-pytest | Python testing with pytest | pytest, testing, coverage |

| async-python | Python async/await programming | async, asyncio, concurrency |

| fastapi-web | FastAPI web framework | fastapi, web, api, openapi |

| image-processing | PIL/Pillow image manipulation | image, pillow, png, jpeg |

Key Features

1. Weighted Scoring: Keywords (+10), ID (+8), Name (+5), Description (+2)
2. Auto-Load: Skills are automatically loaded after installation
3. Index-Based: Uses known_skills.json for reliable discovery

Omni now uses LanceDB-based vector search for intelligent skill discovery with semantic match