antislope-ai 0.1.0

Local AI coding supervision layer — watches your code, runs on-device review, surfaces findings via MCP

antislope-ai

Stop your vibe-coded project from turning into a mess.

You're using Cursor, Copilot, or Claude Code to write code fast — but AI generates a lot of code quickly, and without a feedback loop, issues pile up silently until the codebase becomes too tangled to fix.

antislope-ai is a local background layer that:

• Watches every save → runs an on-device AI review automatically (no API cost, no code leaves your machine)
• Catches issues early → flags naming problems, missing docs, risky boundaries before they stack up into debt
• Feeds your AI tool live context via MCP → Cursor / Copilot / Claude Code automatically know your active rules, project structure, and recent findings without you pasting anything manually
• Saves tokens → instead of re-explaining your project in every conversation (2,000–20,000 tokens), MCP injects a compact, always-fresh summary (~1,000 tokens total)

Designed for vibe coders and beginners: runs silently in the background, no manual code review needed, works with whatever AI coding tool you already use.

How it works

  Your editor  ──saves──▶  watcher  ──triggers──▶  local model (Ollama, free)
                                                           │
                                                    review results
                                                           │
  AI coding tool  ◀──MCP tools──  dashboard (http://127.0.0.1:8771)
  (Copilot / Cursor / Claude Code)

• Local model (qwen2.5-coder:7b or any Ollama model) reviews every save against your project rules — runs on your machine, zero API cost
• Dashboard shows current issues, risk chains, and handling status
• MCP endpoint lets Cursor, VS Code Copilot, Claude Code, and others automatically read your active rules and recent findings — no manual copy-paste, fewer tokens per session

Requirements

Item	Version
Python	≥ 3.11
Ollama	latest
macOS / Linux	—

Windows is not tested. Ollama runs on Windows but the shell commands differ.

Quick start

# 1. Install Ollama and pull a model (one-time)
brew install ollama          # macOS
ollama pull qwen2.5-coder:7b

# 2. Clone and set up
git clone https://github.com/zcj220/antislope-ai.git
cd antislope-ai
python3 -m venv venv
source venv/bin/activate
pip install -e .

# 3. Initialize project
antislope init

# 4. Start the dashboard (also starts the MCP server)
antislope dashboard
# Open http://127.0.0.1:8771 in your browser

Watch a file for live review

# In a second terminal
antislope watch --file path/to/your/file.py

MCP integration (AI coding tools read your rules automatically)

The dashboard exposes an MCP endpoint at http://127.0.0.1:8771/mcp with four tools:

Tool	Returns
get_active_rules	Current active review rules
get_project_structure	Entry points and core file roles
get_recent_issues	Latest detected issues and risk level
get_project_context	Project goal, direction, and high-risk areas

Cursor

Config already included at .cursor/mcp.json. Restart Cursor — tools appear automatically.

VS Code Copilot (Agent mode, v1.99+)

Config already included at .vscode/mcp.json. Restart VS Code → open Copilot Chat → switch to Agent mode → enable antislope tools.

Claude Code

claude mcp add antislope http://127.0.0.1:8771/mcp

Windsurf

Edit ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "antislope": { "serverUrl": "http://127.0.0.1:8771/mcp" }
  }
}

Other commands

antislope review-real --file path/to/file.py   # One-shot manual review
antislope stats                                 # Review event statistics
antislope validate-rules                        # Test rules against sample files
antislope clean-review-data                     # Normalize historical review data
antislope index-structure                       # Rebuild structure index

Default model

The default model is qwen2.5-coder:7b. To change it, edit data/model-settings.json:

{ "model_name": "deepseek-coder-v2:16b", "base_url": "http://localhost:11434" }

Any model available in your local Ollama installation can be used.

Rules

Rules live in rules/ (YAML) and data/custom-rules.json. The system ships with a set of default rules. You can add project-specific rules via the dashboard → Rules panel.

License

MIT