Co-Pilot
Updated 24 days ago

omni-dev-fusion

Ttao3k
0.0k
tao3k/omni-dev-fusion
82
Agent Score

πŸ’‘ Summary

Omni-Dev-Fusion is an Agentic OS kernel that automates the software development lifecycle through skill-based commands.

🎯 Target Audience

Software developers looking for automation toolsDevOps engineers managing CI/CD pipelinesData scientists needing dynamic skill acquisitionTechnical project managers overseeing development workflowsAI researchers exploring agent-based systems

πŸ€– AI Roast: β€œPowerful, but the setup might scare off the impatient.”

Security AnalysisMedium Risk

Risk: Medium. Review: shell/CLI command execution; outbound network access (SSRF, data egress); API keys/tokens handling and storage; filesystem read/write scope and path traversal. Run with least privilege and audit before enabling in production.

Omni-Dev-Fusion Fusion

From Copilot to Autopilot: Building the Agentic OS for the post-IDE era.

Omni-Dev-Fusion Fusion is an Agentic OS kernel that bridges the gap between human intent and machine execution. By integrating the innovative Tri-MCP Tri-Core Architecture, Fusion strictly separates cognitive planning (Brain/Orchestrator), atomic execution (Hands/Executor), and precision coding (Pen/Coder) at the physical layer.

With Nix for absolute environment reproducibility and a rigorous "Legislation-Execution" policy engine, Fusion empowers AI to autonomously handle the complete SDLCβ€”from architectural design to AST-level refactoring.

One Tool Architecture

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

@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 |

Skill Network (Git Installer)

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

# 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 <url> | Install skill from Git URL | | omni skill install <url> --name <name> | Install with custom name | | omni skill install <url> --version <ref> | Install specific branch/tag/commit | | omni skill update <skill> | Update skill to latest | | omni skill update <skill> --strategy stash\|abort\|overwrite | Update with conflict handling | | omni skill list | List all installed skills | | omni skill info <skill> | 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)

Cascading Templates & Skill Structure Validation

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

# 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/
β”œβ”€β”€ commit_message.j2     # Custom template (highest priority)

# Priority 2: Skill Defaults (assets/skills/{skill}/templates/)
assets/skills/git/templates/
β”œβ”€β”€ commit_message.j2     # Skill default (fallback)
β”œβ”€β”€ workflow_result.j2
└── error_message.j2

Skill Structure Validation

πŸ” git

βœ… Valid: True
πŸ“Š Score: 100.0%
πŸ“ Location: assets/skills/git

## πŸ“ Structure
β”œβ”€β”€ SKILL.md              # Required
β”œβ”€β”€ scripts/              # Required (commands.py with @skill_command)
β”œβ”€β”€ templates/            # Optional (cascading)
└── tests/                # Optional (zero-config)

JIT Skill Acquisition

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

# 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"})
     β†’ Found: network-analysis (keywords: pcap, network, wireshark)

     @omni("skill.jit_install", {"skill_id": "network-analysis"})
     β†’ βœ… 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

Vector-Enhanced Discovery (Virtual Loading)

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

# Reindex all skills into vector store omni skill reindex omni skill reindex --clear # Full rebuild # Show index statistics omni skill index-stats # Search local skills only @omni("skill.discover", {"query": "docker containers", "local_only": true}) # Get task-based suggestions @omni("skill.suggest", {"task": "analyze nginx logs"})

Routing Flow

User Request
    ↓
1. Semantic Cortex (fuzzy cache)
2. Exact Match Cache
3. LLM Routing (Hot Path)
4. Vector Fallback (Cold Path) ← NEW
    - Only searches LOCAL skills (installed_only=True)
    - Returns suggested_skills in RoutingResult

How It Works

  • Index Source: SKILL.md files are parsed and embedded
  • Storage: LanceDB table skills (via omni-vector)
  • Query: Semantic similarity search with metadata filtering
  • Fallback: Triggered when LLM confidence < 0.5 or uses generic skills

Vector Fallback Example

# User: "write documentation about this project" # LLM returns: confidence=0.7, selects generic "writer" skill # Vector Fallback triggers # Searches for "write documentation" in local skills # Finds: documentation (score=0.92) # Result: # { # "selected_skills": ["writer"], # "suggested_skills": ["documentation"], # NEW! # "confidence": 0.85, # Boosted # "reasoning": "... [Vector Fallback] Found local skill: documentation" # }

Available Commands

| Command | Description | | ------------------------ | -------------------------- | | omni skill reindex | Rebuild vector index | | omni skill index-stats | Show index statistics | | skill.discover | Semantic skill search | | skill.suggest | Task-based recommendations | | skill.reindex | Rebuild index (tool) |

See Developer Guide for detailed architecture documentation.

Hot Reload & Index Sync

Omni now supports zero-downtime skill reloading wi

5-Dim Analysis
Clarity8/10
Novelty9/10
Utility9/10
Completeness8/10
Maintainability7/10
Pros & Cons

Pros

  • Highly flexible skill management
  • Supports dynamic skill discovery
  • Integrates with Git for version control
  • Facilitates reproducible environments

Cons

  • Complexity may overwhelm new users
  • Dependency on external Git repositories
  • Potential for version conflicts during updates
  • Requires understanding of skill architecture

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Disclaimer: This content is sourced from GitHub open source projects for display and rating purposes only.

Copyright belongs to the original author tao3k.