codealive-context-engine

name

codealive-context-engine

description

Semantic code search and AI-powered codebase Q&A across indexed repositories. Use when understanding code beyond local files, exploring dependencies, discovering cross-project patterns, planning features, debugging, or onboarding. Queries like "How does X work?", "Show me Y patterns", "How is library Z used?". Provides search (fast, returns file locations) and chat-with-codebase (slower, costs more, but returns synthesized answers).

CodeAlive Context Engine

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

Table of Contents

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

Tools Overview

Tool	Script	Speed	Cost	Best For
List Data Sources	datasources.py	Instant	Free	Discovering indexed repos and workspaces
Search	search.py	Fast	Low	Finding code locations, file paths, snippets
Chat with Codebase	chat.py	Slow	High	Synthesized answers, architectural explanations
Explore	explore.py	Slow	High	Multi-step discovery workflows

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.

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

3. 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

4. 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, and code snippets. Fast and cheap.

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

Option	Description
--mode auto	Default. Intelligent semantic search — use 80% of the time
--mode fast	Quick lexical search for known terms
--mode deep	Exhaustive search for complex cross-cutting queries. Resource-intensive
--include-content	Include full file content (use for external repos you can't Read locally)

Content inclusion rule: Use --include-content only for repositories outside your working directory. For the current repo, get paths from search and then read files directly for latest content.

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]

Option	Description
--continue <id>	Continue a previous conversation (saves context and cost)

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...>

Mode	Purpose
understand:<topic>	Search + explanation
dependency:<library>	Library usage and internals
pattern:<pattern>	Cross-project pattern discovery
implement:<feature>	Find similar features for guidance
debug:<issue>	Trace symptom to root cause

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

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