A graph-based code analysis system that allows developers to scan repositories once and query them in plain English for structural insights and debugging.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install meaningful-code-context-scanner
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install meaningful-code-context-scanner using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Meaningful Chunker?
Meaningful Chunker is a sophisticated code intelligence tool designed to move beyond simple keyword searches. By building a semantic graph of an entire repository, it understands the intricate relationships between components, functions, and files. This allows developers and AI agents to ask complex questions about system architecture, design philosophy, and bug origins without needing to manually traverse the codebase. This powerful analysis engine integrates seamlessly with Openclaw Skills to deliver high-context, actionable intelligence.
The system is language-agnostic and processes everything from Python and C++ to configuration files. Unlike traditional search tools that return raw file dumps, this skill provides structured, ranked results categorized into relevance tiers. It synthesizes findings into human-readable summaries, execution paths, and specific next-step recommendations, significantly reducing the cognitive load required to understand legacy systems or complex modern architectures.
Meaningful Chunker Use Cases
- Map out complex software architectures using natural language queries to understand module roles.
- Trace bugs to their source by analyzing execution chains and automated root cause hypotheses.
- Evaluate the blast radius and risk level associated with refactoring or changing specific code modules.
- Accelerate developer onboarding by querying the design principles and philosophies governing a project.
How Meaningful Chunker Works
- Initiate a repository scan by providing a GitHub or GitLab URL to the indexing engine.
- Monitor the status endpoint until the semantic graph construction is verified as ready.
- Submit a natural language query to one of the four intent-specific endpoints: architecture, debug, refactor, or philosophy.
- Receive a structured JSON response featuring synthesis fields like the answer summary and primary execution path.
- Review the ranked context tiers, starting with CENTER and EPIPHANY results for the most critical insights.
Meaningful Chunker Setup
To utilize this skill within the Openclaw Skills framework, you must configure your environment with the following credentials:
export CHUNKER_API_URL="https://meaningful-chunker-production.up.railway.app"
export CHUNKER_API_KEY="YOUR_API_KEY"
You can obtain a free API key by registering at the project homepage. Once configured, ensure your agent starts every new repository interaction with a POST /scan request to build the necessary code graph.
Meaningful Chunker Data Schema & Taxonomy
The skill returns a highly structured dataset designed for both human readability and agent processing. The output is organized as follows:
| Component | Description |
|---|---|
| answer_summary | A one-sentence synthesis of the key findings. |
| primary_path | The structural execution chain (e.g., A -> B -> C) showing component connections. |
| confidence | A trust signal (high, medium, or low) for the generated response. |
| query_profile | Indicates the detected intent, such as architecture or refactor_risk. |
| next_step | An actionable follow-up task pre-computed by the analysis engine. |
| context_tiers | Chunks ranked by relevance: CENTER (100), EPIPHANY (99), BREAKTHROUGH (82), and RELEVANT (70). |
| metadata | Includes file paths, component types, and specific reasons why each chunk matched the query. |
Meaningful Chunker Advanced Features
- Session continuity support using a session_id to maintain context across multiple related queries.
- Automated root cause analysis for debugging, providing ranked hypotheses and specific check instructions for suspects.
- Structural authority metrics that categorize code as untouchable, sensitive, or local to assess refactor safety.
- Execution timeline generation that identifies and brackets failing chunks within the call chain.
- Insight memory that automatically biases results toward components that are frequently queried within a session.
SKILL.md
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