codealive-context-engine（Codealive 上下文引擎）

CodeAlive Context Engine

Semantic code intelligence across your entire code ecosystem — current project, organizational repos, dependencies, and any indexed codebase.

Authentication

All scripts require a CodeAlive API key. If any script fails with "API key not configured", help the user set it up:

Option 1 (recommended): Run the interactive setup and wait for the user to complete it:

python setup.py

Option 2 (not recommended — key visible in chat history): If the user pastes their API key directly in chat, save it via:

python setup.py --key THE_KEY

Do NOT retry the failed script until setup completes successfully.

Table of Contents

• Authentication
• Tools Overview
• When to Use
• Quick Start
• Tool Reference
• Data Sources
• Configuration

Tools Overview

Cost guidance: Search is lightweight and should be the default starting point. Chat with Codebase invokes an LLM on the server side, making it significantly more expensive per call — use it when you need a synthesized, ready-to-use answer rather than raw search results.

Three-step workflow:

1. Search — find relevant code locations with descriptions and identifiers
2. Review — examine the descriptions to understand what each result contains
3. Get content — use fetch.py for external repos or Read() for local files

When to Use

Use this skill for semantic understanding:

• "How is authentication implemented?"
• "Show me error handling patterns across services"
• "How does this library work internally?"
• "Find similar features to guide my implementation"

Use local file tools instead for:

• Finding specific files by name or pattern
• Exact keyword search in the current directory
• Reading known file paths
• Searching uncommitted changes

Quick Start

1. Discover what's indexed

python scripts/datasources.py

2. Search for code (fast, cheap)

python scripts/search.py "JWT token validation" my-backend
python scripts/search.py "error handling patterns" workspace:platform-team --mode deep
python scripts/search.py "authentication flow" my-repo --description-detail full

3. Fetch full content (for external repos)

python scripts/fetch.py "my-org/backend::src/auth.py::AuthService.login()"

4. Chat with codebase (slower, richer answers)

python scripts/chat.py "Explain the authentication flow" my-backend
python scripts/chat.py "What about security considerations?" --continue CONV_ID

5. Multi-step exploration

python scripts/explore.py "understand:user authentication" my-backend
python scripts/explore.py "debug:slow database queries" my-service

Tool Reference

datasources.py — List Data Sources

python scripts/datasources.py              # Ready-to-use sources
python scripts/datasources.py --all        # All (including processing)
python scripts/datasources.py --json       # JSON output

search.py — Semantic Code Search

Returns file paths, line numbers, descriptions, identifiers, and content sizes. Fast and cheap.

python scripts/search.py <query> <data_sources...> [options]

Getting content: Search returns descriptions and identifiers. For the current repo, use Read() on the file paths. For external repos, use fetch.py with the identifiers.

fetch.py — Fetch Artifact Content

Retrieves the full source code content for artifacts found via search. Use this for external repositories you cannot access locally.

python scripts/fetch.py <identifier1> [identifier2...]

chat.py — Chat with Codebase

Sends your question to an AI consultant that has full context of the indexed codebase. Returns synthesized, ready-to-use answers. Supports conversation continuity for follow-ups.

This is more expensive than search because it runs an LLM inference on the server side. Prefer search when you just need to locate code. Use chat when you need explanations, comparisons, or architectural analysis.

python scripts/chat.py <question> <data_sources...> [options]

Conversation continuity: Every response includes a conversation_id. Pass it with --continue for follow-up questions — this preserves context and is cheaper than starting fresh.

explore.py — Smart Exploration

Combines search and chat-with-codebase in multi-step workflows. Useful for complex investigations.

python scripts/explore.py <mode:query> <data_sources...>

Data Sources

Repository — single codebase, for targeted searches:

python scripts/search.py "query" my-backend-api

Workspace — multiple repos, for cross-project patterns:

python scripts/search.py "query" workspace:backend-team

Multiple repositories:

python scripts/search.py "query" repo-a repo-b repo-c

Configuration

Prerequisites

• Python 3.8+ (no third-party packages required — uses only stdlib)

API Key Setup

The skill needs a CodeAlive API key. Resolution order:

1. CODEALIVE_API_KEY environment variable
2. OS credential store (macOS Keychain / Linux secret-tool / Windows Credential Manager)

Environment variable (all platforms):

export CODEALIVE_API_KEY="your_key_here"

macOS Keychain:

security add-generic-password -a "$USER" -s "codealive-api-key" -w "YOUR_API_KEY"

Linux (freedesktop secret-tool):

secret-tool store --label="CodeAlive API Key" service codealive-api-key

Windows Credential Manager:

cmdkey /generic:codealive-api-key /user:codealive /pass:"YOUR_API_KEY"

Base URL (optional, defaults to https://app.codealive.ai):

export CODEALIVE_BASE_URL="https://your-instance.example.com"

Get API keys at: https://app.codealive.ai/settings/api-keys

Using with CodeAlive MCP Server

This skill works standalone, but delivers the best experience when combined with the CodeAlive MCP server. The MCP server provides direct tool access via the Model Context Protocol, while this skill provides the workflow knowledge and query patterns to use those tools effectively.

When both are installed, prefer the MCP server's tools for direct operations and this skill's scripts for guided multi-step workflows like explore.py.

Detailed Guides

For advanced usage, see reference files:

• Query Patterns — effective query writing, anti-patterns, language-specific examples
• Workflows — step-by-step workflows for onboarding, debugging, feature planning, and more