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 descriptions) and chat-with-codebase (slower, costs more, but returns synthesized answers). Use when this capability is needed.
metadata	{"author":"codealive-ai"}

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

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, descriptions, identifiers
Fetch Artifacts	fetch.py	Fast	Low	Retrieving full content for search results
Artifact Relationships	relationships.py	Fast	Low	Drilling into call graph, inheritance, references for one artifact
Chat with Codebase	chat.py	Slow	High	Synthesized answers, architectural explanations

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 (search → triage → load real content):

1. Search — find relevant code locations with descriptions and identifiers
2. Triage — use description ONLY to decide which results are worth a closer look. It is a pointer, NOT the source of truth. Do not draw conclusions from it.
3. Get real content — for every artifact you decide is relevant:
   • External repos (no local access): python fetch.py <identifier>
   • Current working repo: read the file at the shown path with your editor's file-read tool Treat only that real content as ground truth.

Optional drill-down: once you know an artifact matters, run python relationships.py <identifier> to expand its call graph, inheritance, or references.

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. Drill into an artifact's relationships (optional)

# Full call graph (default)
python scripts/relationships.py "my-org/backend::src/auth.py::AuthService.login()"

# Inheritance hierarchy for a class
python scripts/relationships.py "my-org/backend::src/models.py::User" --profile inheritanceOnly

# Calls + inheritance, raise the per-type cap
python scripts/relationships.py "my-org/backend::src/svc.py::Service" --profile allRelevant --max-count 200

5. 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

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]

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
--description-detail short	Default. Brief description of each result
--description-detail full	More detailed description of each result

description is a triage pointer ONLY — it tells you which artifacts are worth a closer look. It is NOT the source of truth and you must NOT draw conclusions from it. For every result you consider relevant, load the real source: use fetch.py <identifier> for external repos, or your editor's file-read tool on the path for repos in the current working directory. Treat only that real content as ground truth.

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...]

Constraint	Value
Max identifiers per request	20
Identifiers source	identifier field from search results
Identifier format	{owner/repo}::{path}::{symbol} (symbols), {owner/repo}::{path} (files)

For function-like artifacts the response includes a small relationships preview (up to 3 outgoing/incoming calls per direction). To see the full call graph, inheritance, or references, run relationships.py with the artifact's identifier.

relationships.py — Drill into an Artifact's Relationship Graph

Returns the full call graph (incoming/outgoing calls), inheritance hierarchy (ancestors/descendants), or symbol references for a single artifact. This is the drill-down tool — use it AFTER search.py or fetch.py once you have an identifier and want to understand how the artifact relates to the rest of the codebase.

python scripts/relationships.py <identifier> [--profile PROFILE] [--max-count N]

Option	Description
--profile callsOnly	Default. Outgoing + incoming calls
--profile inheritanceOnly	Ancestors + descendants
--profile allRelevant	Calls + inheritance (4 groups)
--profile referencesOnly	Symbol references
--max-count N	Max related artifacts per relationship type (1–1000, default 50)
--json	Emit the raw JSON response instead of the formatted view

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.

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"

For self-hosted CodeAlive, use your deployment origin. https://your-instance.example.com is preferred, but https://your-instance.example.com/api is also accepted and normalized automatically.

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.

Component	What it provides
This skill	Query patterns, workflow guidance, cost-aware tool selection
MCP server	Direct codebase_search, fetch_artifacts, get_artifact_relationships, codebase_consultant, get_data_sources tools

When both are installed, prefer the MCP server's tools for direct operations and this skill's scripts for guided workflows.

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

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