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Qe Coverage Analysis

Community
proffesor-for-testing
qe-coverage-analysis

Analyzes test coverage data (Istanbul, c8, lcov) to identify uncovered lines, branches, and functions with risk-weighted gap detection. Use when analyzing coverage reports, identifying coverage gaps, comparing coverage between branches, or prioritizing which untested code to cover first.

Overview

Publisherproffesor-for-testing
Repositoryagentic-qe
Skill nameqe-coverage-analysis
Stars
480
Forks
92
Bundled files
4
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.

  • 4 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 Coverage Analysis 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-coverage-analysis .claude/skills/qe-coverage-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Qe Coverage Analysis 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 Coverage Analysis 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 Coverage Analysis 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 Coverage Analysis

Purpose

Guide the use of v3's advanced coverage analysis capabilities including sublinear gap detection algorithms, risk-weighted coverage scoring, and intelligent test prioritization based on code criticality.

Activation

  • When analyzing test coverage
  • When identifying coverage gaps
  • When prioritizing testing effort
  • When setting coverage targets
  • When assessing code risk

Quick Start

bash
# Analyze coverage with gap detection
aqe coverage analyze --source src/ --tests tests/

# Find high-risk uncovered code
aqe coverage gaps --risk-weighted --threshold 80

# Generate coverage report
aqe coverage report --format html --output coverage-report/

# Compare coverage between branches
aqe coverage diff --base main --head feature-branch

Agent Workflow

typescript
// Comprehensive coverage analysis
Task("Analyze coverage gaps", `
  Perform O(log n) coverage analysis on src/:
  - Calculate statement, branch, function coverage
  - Identify uncovered critical paths
  - Risk-weight gaps by code complexity and change frequency
  - Recommend tests to write for maximum coverage impact
`, "qe-coverage-specialist")

// Risk-based prioritization
Task("Prioritize coverage effort", `
  Analyze coverage gaps and prioritize by:
  - Business criticality (payment, auth, data)
  - Code complexity (cyclomatic > 10)
  - Recent bug history
  - Change frequency
  Output prioritized list of files needing tests.
`, "qe-coverage-analyzer")

Analysis Strategies

1. Sublinear Gap Detection

typescript
await coverageAnalyzer.detectGaps({
  algorithm: 'sublinear',  // O(log n) complexity
  source: 'src/**/*.ts',
  metrics: ['statement', 'branch', 'function'],
  sampling: {
    enabled: true,
    confidence: 0.95,
    maxSamples: 1000
  }
});

2. Risk-Weighted Coverage

typescript
await coverageAnalyzer.riskWeightedAnalysis({
  coverage: coverageReport,
  riskFactors: {
    complexity: { weight: 0.3, threshold: 10 },
    changeFrequency: { weight: 0.25, window: '90d' },
    bugHistory: { weight: 0.25, window: '180d' },
    criticality: { weight: 0.2, tags: ['payment', 'auth'] }
  },
  output: {
    riskScore: true,
    prioritizedGaps: true
  }
});

3. Differential Coverage

typescript
await coverageAnalyzer.diffCoverage({
  base: 'main',
  head: 'feature-branch',
  requirements: {
    newCode: 80,           // New code must have 80% coverage
    modifiedCode: 'maintain',  // Don't decrease existing
    deletedCode: 'ignore'
  }
});

Coverage Thresholds

yaml
thresholds:
  global:
    statements: 80
    branches: 75
    functions: 85
    lines: 80

  per_file:
    min_statements: 70
    critical_paths: 90

  new_code:
    statements: 85
    branches: 80

  exceptions:
    - path: "src/migrations/**"
      reason: "Database migrations"
    - path: "src/generated/**"
      reason: "Auto-generated code"

Coverage Report

typescript
interface CoverageAnalysis {
  summary: {
    statements: { covered: number; total: number; percentage: number };
    branches: { covered: number; total: number; percentage: number };
    functions: { covered: number; total: number; percentage: number };
  };
  gaps: {
    file: string;
    uncoveredLines: number[];
    uncoveredBranches: BranchInfo[];
    riskScore: number;
    suggestedTests: string[];
  }[];
  trends: {
    period: string;
    coverageChange: number;
    newGaps: number;
    closedGaps: number;
  };
  recommendations: {
    priority: 'critical' | 'high' | 'medium' | 'low';
    file: string;
    action: string;
    expectedImpact: number;
  }[];
}

Quality Gates

yaml
quality_gates:
  coverage:
    block_merge:
      - new_code_coverage < 80
      - coverage_regression > 5
      - critical_path_uncovered

    warn:
      - overall_coverage < 75
      - branch_coverage < 70

    metrics:
      - track_trends: true
      - alert_on_decline: 3  # consecutive PRs

Run History

After each coverage analysis, append results to run-history.json in this skill directory:

bash
# Read current history, append new entry, write back
node -e "
const fs = require('fs');
const h = JSON.parse(fs.readFileSync('.claude/skills/qe-coverage-analysis/run-history.json'));
h.runs.push({date: new Date().toISOString().split('T')[0], statements_pct: STATEMENTS, branches_pct: BRANCHES, gaps_found: GAPS});
fs.writeFileSync('.claude/skills/qe-coverage-analysis/run-history.json', JSON.stringify(h, null, 2));
"

Read run-history.json before each run to detect trends (e.g., "coverage dropped 3 consecutive times").

Skill Composition

  • Coverage dropped? → Use /coverage-drop-investigator to trace the cause
  • Need more tests → Use /qe-test-generation to fill gaps
  • Validate quality → Use /mutation-testing to ensure coverage means quality
  • Ship decision → Feed into /qe-quality-assessment for deployment readiness

Gotchas

  • High line coverage does NOT mean good tests — 100% coverage with 0% assertions is common agent output. Use mutation testing to verify
  • coverage-analysis domain has 86% success rate — 14% of runs fail on initialization. Always verify results and have fallback plan (e.g. manual coverage tools)
  • Self-learning pipeline may silently stop learning (statusline frozen for days) — only human inspection catches this

Coordination

Primary Agents: qe-coverage-specialist, qe-coverage-analyzer, qe-gap-detector Coordinator: qe-coverage-coordinator Related Skills: qe-test-generation, qe-quality-assessment

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 Coverage Analysis AI skill do?

Analyzes test coverage data (Istanbul, c8, lcov) to identify uncovered lines, branches, and functions with risk-weighted gap detection. Use when analyzing coverage reports, identifying coverage gaps, comparing coverage between branches, or prioritizing which untested code to cover first.

Why use Qe Coverage Analysis on TypingMind?

Because you install it once and use it with any model. Qe Coverage Analysis 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 Coverage Analysis 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-coverage-analysis. 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 Coverage Analysis?

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 Coverage Analysis?

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

Is the Qe Coverage Analysis 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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