Agent skill

Qe Code Intelligence

by proffesor-for-testing in proffesor-for-testing/agentic-qe

Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.

MITAuto-check passedKnowledge Management

Install Qe Code Intelligence

skills CLI
$ npx skills add proffesor-for-testing/agentic-qe --skill qe-code-intelligence -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install proffesor-for-testing/agentic-qe qe-code-intelligence --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/proffesor-for-testing/agentic-qe.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.kiro/skills/qe-code-intelligence .claude/skills/qe-code-intelligence && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
qe-code-intelligence
GitHub stars
494
Used in
1 other repo
Token cost
~1.2k tokens
SKILL.md length
92 words
Files
1
Skills in repo
111
Repo updated
First seen
Licence
MIT

At a glance

Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.

  • Works in 3 steps: Codebase Indexing → Semantic Search → Dependency Analysis
  • Tasks that involve Knowledge graphs
  • SKILL.md covers Purpose, Activation, Quick Start and Agent Workflow, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Qe Code Intelligence is an agent skill from proffesor-for-testing/agentic-qe. Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Knowledge Management, covering Knowledge graphs. The repository describes itself as: Agentic QE Fleet is an open-source AI-powered QA/QE platform designed for use with Coding Agents (works best with Claude Code) featuring specialized agents and skills to support… The licence is MIT.

When your agent uses it

  • Tasks that involve Knowledge graphs

Example prompts

  • “/qe-code-intelligence”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Codebase Indexing
  2. Semantic Search
  3. Dependency Analysis

What it can do on your machine

Read from SKILL.md and the folder at commit 829d030. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Qe Code Intelligence loads about 1.2k tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 92 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~36
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from proffesor-for-testing/agentic-qe at commit 829d030, republished under its MIT licence (© proffesor-for-testing). 92 words, ~1,226 tokens.

Download SKILL.mdSave it as .claude/skills/qe-code-intelligence/SKILL.md (or your agent's skills folder).
name
qe-code-intelligence
description
Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.
inclusion
auto

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
});
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

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

© proffesor-for-testing, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .kiro/skills/qe-code-intelligence of proffesor-for-testing/agentic-qe.

Open the folder on GitHubat commit 829d030

Used in 1 other repository

We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in proffesor-for-testing/agentic-qe, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Qe Code Intelligence next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Qe Code Intelligence compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Qe Code Intelligence this skillproffesor-for-testing/agentic-qe4941 repos~1.2kAutomated safety check: PassMIT
LLM Wiki Knowledge GraphEgonex-AI/Understand-Anything85k1 repos~1.5kAutomated safety check: PassMIT
Obsidian Canvas BoardsAgriciDaniel/claude-obsidian15k—~1.4kAutomated safety check: PassMIT
Ontology1mancompany/OneManCompany4382 repos~1.5kAutomated safety check: PassApache-2.0
Graphagenticnotetaking/arscontexta3.5k1 repos~4.9kAutomated safety check: NotesMIT
Knowledge Graphgnomeria/usbtree688—~1.5kAutomated safety check: PassMIT

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  • LLM Wiki Knowledge Graph

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Questions about Qe Code Intelligence

What does Qe Code Intelligence do?

Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval. Qe Code Intelligence is an agent skill from proffesor-for-testing/agentic-qe. Knowledge graph-based code understanding with semantic search and 80% token reduction through intelligent context retrieval.

When should I use Qe Code Intelligence?

Qe Code Intelligence fits situations like: tasks that involve Knowledge graphs.

How do I install Qe Code Intelligence in Claude Code?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-code-intelligence -a claude-code`. Or copy the skill folder (.kiro/skills/qe-code-intelligence in proffesor-for-testing/agentic-qe) into .claude/skills/qe-code-intelligence in your project. Claude Code loads it when a task matches its description.

How do I install Qe Code Intelligence in Codex?

Run `npx skills add proffesor-for-testing/agentic-qe --skill qe-code-intelligence -a codex`. Or copy the skill folder (.kiro/skills/qe-code-intelligence in proffesor-for-testing/agentic-qe) into .agents/skills/qe-code-intelligence in your project. Codex loads it when a task matches its description.

Can I use Qe Code Intelligence in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add proffesor-for-testing/agentic-qe --skill qe-code-intelligence -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/qe-code-intelligence, .gemini/skills/qe-code-intelligence, .github/skills/qe-code-intelligence and .opencode/skills/qe-code-intelligence in your project.

What does Qe Code Intelligence need to run?

SKILL.md names no scripts, command-line tools or credentials: Qe Code Intelligence is instructions for the agent only.

Does Qe Code Intelligence access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Qe Code Intelligence safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Qe Code Intelligence use?

Qe Code Intelligence is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Qe Code Intelligence use?

About 1.2k tokens (SKILL.md is roughly 4.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Qe Code Intelligence?

Skills that share tags, products or a category with Qe Code Intelligence: LLM Wiki Knowledge Graph (Egonex-AI/Understand-Anything, 85k stars), Obsidian Canvas Boards (AgriciDaniel/claude-obsidian, 15k stars), Ontology (1mancompany/OneManCompany, 438 stars) and Graph (agenticnotetaking/arscontexta, 3.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Qe Code Intelligence?

proffesor-for-testing (a GitHub user) maintains it in proffesor-for-testing/agentic-qe, which has 494 GitHub stars. The repository holds 111 skills in this directory. The repository was last updated on October 4, 2026.

Source: proffesor-for-testing/agentic-qe on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.