KS
Killer-Skills

ai-context-repository — Categories.community

v1.0.0
GitHub

About this Skill

Perfect for AI Agents needing comprehensive content analysis and visualization for Python projects, such as those utilizing vscode extensions for data flow diagrams. A vscode extension that provides data / logic flow diagrams similar to Unreal Engine's Blueprints for Python projects

BenWeatherall BenWeatherall
[0]
[0]
Updated: 3/4/2026

Quality Score

Top 5%
26
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add BenWeatherall/vscode-python-ast-extension/ai-context-repository

Agent Capability Analysis

The ai-context-repository MCP Server by BenWeatherall is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion.

Ideal Agent Persona

Perfect for AI Agents needing comprehensive content analysis and visualization for Python projects, such as those utilizing vscode extensions for data flow diagrams.

Core Value

Empowers agents to generate and maintain a single source of truth for overall architecture, directory and component layout, data flow between services, and high-level extension points using Markdown documents like `AI_CONTEXT_REPOSITORY.md` and linking to other AI_CONTEXT documents for structure.

Capabilities Granted for ai-context-repository MCP Server

Generating data flow diagrams for Python projects
Maintaining a single source of truth for AI context documentation
Creating directory and component layout visualizations

! Prerequisites & Limits

  • Requires vscode extension installation
  • Python project compatibility only
  • Maintenance of `AI_CONTEXT_REPOSITORY.md` document necessary
Project
SKILL.md
3.5 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

# Tags

[No tags]
SKILL.md
Readonly

AI Context Repository Skill

Purpose

Help the ai-context-writer subagent generate and maintain docs/AI_CONTEXT/AI_CONTEXT_REPOSITORY.md as the single source of truth for:

  • Overall architecture
  • Directory and component layout
  • Data flow between services
  • High-level extension points

Other AI_CONTEXT documents (patterns, component-specific docs) should link back here for structure.

Sources to Read

Before updating the repository document, read (as appropriate):

  • @README.md — project purpose and high-level goals
  • @python_service/ — parser, models, server entry point
  • @src/ — extension host, commands, integration logic
  • @webview-ui/ — React + Rete webview implementation
  • @tests/ — to see where tests live
  • @.cursor/rules/environment.mdc — structure and tooling expectations
  • Existing @docs/AI_CONTEXT/AI_CONTEXT_REPOSITORY.md (if present)

Required Sections in AI_CONTEXT_REPOSITORY.md

At minimum, ensure the file contains:

  1. Metadata

    • Version
    • Last Updated (ISO date)
    • Tags (include architecture, repository)
    • Cross-References to quick reference, patterns, and component-specific AI_CONTEXT docs
  2. High-Level Overview

    • Goal of the system in 1–2 paragraphs
    • Summary of the sidecar architecture (Python service, extension host, webview UI)
  3. Directory Structure

    • An annotated tree of the most important directories:
      • python_service/
      • src/
      • webview-ui/
      • tests/
      • .cursor/
      • _features/ (and others if relevant)
  4. Component Responsibilities

    • Subsections for each major component:
      • Python service
      • Extension host
      • Webview UI
    • For each: what it does, key modules, and main responsibilities.
  5. Data Flow

    • End-to-end flow from VS Code editor → extension → Python service → webview → back to editor.
    • Auto-refresh flow on save.
    • Error and retry flow.
    • At least one mermaid diagram illustrating the main happy path.
  6. Service Boundaries & Dependencies

    • Clarify the boundaries between:
      • Python process
      • Node/extension host
      • Webview/browser runtime
    • List key external libraries and frameworks per component.
  7. Entry Points & Extension Hooks

    • Python service entry (python_service.__main__, ASTParseServer).
    • Extension activation (extension.ts, command registration).
    • Webview bootstrap (webview-ui/src/index.tsx and App.tsx).
    • Guidance on where to plug in:
      • New AST node visitors
      • New webview messages
      • New VS Code commands

Style & Constraints

  • Keep the document architectural, not tutorial-style.
  • Use semantic headings and short paragraphs.
  • Prefer diagrams and structured lists over long prose.
  • Avoid management or roadmap content; focus purely on how the system is structured today.
  • Keep under the content length limit; if it grows too large:
    • Split into sub-documents (e.g. AI_CONTEXT_REPOSITORY/) per content_length rules.
    • Provide an index with links and one-line descriptions.

Update Strategy

When the project structure or flow changes:

  1. Update the directory tree to match the real repository.
  2. Adjust component responsibilities and data flow descriptions.
  3. Refresh diagrams to reflect new paths or services.
  4. Bump version/last-updated metadata.
  5. Ensure cross-references to component-specific docs remain valid.

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