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Qe Code Intelligence

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proffesor-for-testing
qe-code-intelligence

Builds semantic code indexes, maps dependency graphs, and performs intelligent code search across large codebases. Use when understanding unfamiliar code, tracing call chains, analyzing import dependencies, or reducing context window usage through targeted retrieval.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill nameqe-code-intelligence
Stars
480
Forks
92
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by proffesor-for-testing on GitHub. Read the source before you install it.

Installation

Install the Qe Code Intelligence AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git /tmp/agentic-qe
mkdir -p .claude/skills
cp -r /tmp/agentic-qe/assets/skills/qe-code-intelligence .claude/skills/qe-code-intelligence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qe Code Intelligence in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Qe Code Intelligence on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Qe Code Intelligence is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

QE Code Intelligence

Purpose

Guide the use of v3's code intelligence capabilities including knowledge graph construction, semantic code search, dependency mapping, and context-aware code understanding with significant token reduction.

Activation

  • When understanding unfamiliar code
  • When searching for code semantically
  • When analyzing dependencies
  • When building code knowledge graphs
  • When reducing context for AI operations

Quick Start

bash
# Index codebase into knowledge graph
aqe code index src/ --incremental

# Semantic code search
aqe code search "authentication middleware"

# Analyze change impact
aqe code impact src/services/UserService.ts --depth 3

# Map dependencies
aqe code deps src/

# Analyze complexity and find hotspots
aqe code complexity src/

# Generate C4 architecture diagrams (Mermaid) with a confidence score
aqe code c4 .

Agent Workflow

typescript
// Build knowledge graph
Task("Index codebase", `
  Build knowledge graph for the project:
  - Parse all TypeScript files in src/
  - Extract entities (classes, functions, types)
  - Map relationships (imports, calls, inheritance)
  - Generate embeddings for semantic search
  Store in AgentDB vector database.
`, "qe-kg-builder")

// Semantic search
Task("Find relevant code", `
  Search for code related to "user authentication flow":
  - Use semantic similarity (not just keyword)
  - Include related functions and types
  - Rank by relevance score
  - Return with minimal context (80% token reduction)
`, "qe-code-intelligence")

Knowledge Graph Operations

1. Codebase Indexing

typescript
await knowledgeGraph.index({
  source: 'src/**/*.ts',
  extraction: {
    entities: ['class', 'function', 'interface', 'type', 'variable'],
    relationships: ['imports', 'calls', 'extends', 'implements', 'uses'],
    metadata: ['jsdoc', 'complexity', 'lines']
  },
  embeddings: {
    model: 'code-embedding',
    dimensions: 384,
    normalize: true
  },
  incremental: true  // Only index changed files
});

2. Semantic Search

typescript
await semanticSearcher.search({
  query: 'payment processing with stripe',
  options: {
    similarity: 'cosine',
    threshold: 0.7,
    limit: 20,
    includeContext: true
  },
  filters: {
    fileTypes: ['.ts', '.tsx'],
    excludePaths: ['node_modules', 'dist']
  }
});

3. Dependency Analysis

typescript
await dependencyMapper.analyze({
  entry: 'src/services/OrderService.ts',
  depth: 3,
  direction: 'both',  // imports and importedBy
  output: {
    graph: true,
    metrics: {
      afferentCoupling: true,
      efferentCoupling: true,
      instability: true
    }
  }
});

Token Reduction Strategy

typescript
// Get context with 80% token reduction
const context = await codeIntelligence.getOptimizedContext({
  query: 'implement user registration',
  budget: 4000,  // max tokens
  strategy: {
    relevanceRanking: true,
    summarization: true,
    codeCompression: true,
    deduplication: true
  },
  include: {
    signatures: true,
    implementations: 'relevant-only',
    comments: 'essential',
    examples: 'top-3'
  }
});

Knowledge Graph Schema

typescript
interface KnowledgeGraph {
  entities: {
    id: string;
    type: 'class' | 'function' | 'interface' | 'type' | 'file';
    name: string;
    file: string;
    line: number;
    embedding: number[];
    metadata: Record<string, any>;
  }[];
  relationships: {
    source: string;
    target: string;
    type: 'imports' | 'calls' | 'extends' | 'implements' | 'uses';
    weight: number;
  }[];
  indexes: {
    byName: Map<string, string[]>;
    byFile: Map<string, string[]>;
    byType: Map<string, string[]>;
  };
}

Search Results

typescript
interface SearchResult {
  entity: {
    name: string;
    type: string;
    file: string;
    line: number;
  };
  relevance: number;
  snippet: string;
  context: {
    before: string[];
    after: string[];
    related: string[];
  };
  explanation: string;
}

CLI Examples

bash
# Full reindex
aqe code index src/

# Incremental index (changed files only)
aqe code index src/ --incremental

# Index only files changed since a git ref
aqe code index . --git-since HEAD~5

# Semantic code search
aqe code search "database connection"

# Change impact analysis
aqe code impact src/services/UserService.ts

# Dependency mapping
aqe code deps src/ --depth 5

# Complexity metrics and hotspots
aqe code complexity src/ --format json

Gotchas

  • WARNING: code-intelligence domain has 18% success rate — prefer direct grep/glob over agent-based code search for simple queries
  • Knowledge graph construction fails on repos >50K LOC — scope to specific modules
  • Semantic search returns irrelevant results without domain-specific embeddings — always verify search results manually
  • Agent claims "80% token reduction" but may skip critical context — verify key files are included in results
  • Fleet must be initialized before using: run aqe health to diagnose, or aqe init to re-initialize if you get initialization errors

Coordination

Primary Agents: qe-kg-builder, qe-dependency-mapper, qe-impact-analyzer, qe-code-complexity Coordinator: qe-code-intelligence Related Skills: qe-test-generation, qe-defect-intelligence

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Qe Code Intelligence AI skill do?

Builds semantic code indexes, maps dependency graphs, and performs intelligent code search across large codebases. Use when understanding unfamiliar code, tracing call chains, analyzing import dependencies, or reducing context window usage through targeted retrieval.

Why use Qe Code Intelligence on TypingMind?

Because you install it once and use it with any model. Qe Code Intelligence is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Qe Code Intelligence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/proffesor-for-testing/agentic-qe/tree/main/assets/skills/qe-code-intelligence. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Qe Code Intelligence?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Qe Code Intelligence?

As many as you like. As long as a model supports skills, you can use Qe Code Intelligence with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Qe Code Intelligence AI skill free?

Yes. It is published on GitHub by proffesor-for-testing under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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