ai-pattern-detection
Detects AI-generated writing patterns and suggests authentic alternatives. Auto-applies when reviewing content, editing documents, generating text, or when user mentions writing quality, AI detection, authenticity, or natural voice.
Best use case
ai-pattern-detection is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
It is a strong fit for teams already working in Codex.
Detects AI-generated writing patterns and suggests authentic alternatives. Auto-applies when reviewing content, editing documents, generating text, or when user mentions writing quality, AI detection, authenticity, or natural voice.
Teams using ai-pattern-detection should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ai-pattern-detection/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ai-pattern-detection Compares
| Feature / Agent | ai-pattern-detection | Standard Approach |
|---|---|---|
| Platform Support | Codex | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Detects AI-generated writing patterns and suggests authentic alternatives. Auto-applies when reviewing content, editing documents, generating text, or when user mentions writing quality, AI detection, authenticity, or natural voice.
Which AI agents support this skill?
This skill is designed for Codex.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
SKILL.md Source
# AI Pattern Detection Skill ## Purpose Automatically scan content for AI-generated writing patterns and provide authentic alternatives. This skill activates when Claude generates or reviews text content, ensuring outputs maintain human-like authenticity. ## When This Skill Applies - Generating any prose, documentation, or written content - Reviewing or editing existing documents - User mentions "AI detection", "writing quality", "authentic voice" - User asks to "make it sound more natural" or "less robotic" - Creating marketing copy, documentation, or communications ## Detection Categories ### Critical Patterns (Always Flag) These immediately identify content as AI-generated: 1. **Corporate Buzzwords**: "seamlessly integrates", "cutting-edge", "revolutionary", "next-generation", "comprehensive solution" 2. **Vague Intensifiers**: "dramatically improves", "significantly enhances", "vastly superior" 3. **Formulaic Transitions**: "Moreover,", "Furthermore,", "Additionally,", "In conclusion," 4. **Performative Language**: "aims to provide", "strives to achieve", "designed to enhance" 5. **Academic Passive**: "It has been observed that...", "It can be argued that..." ### Structural Patterns (Flag When Overused) 1. **Three-item lists**: "reliable, scalable, and secure" 2. **Em-dash overuse**: Multiple em-dashes in a paragraph 3. **Identical paragraph structure**: Topic → 3 points → conclusion repeated 4. **Balanced hedging**: "While X has challenges, it also offers opportunities" ### Contextual Patterns (Check Frequency) Words acceptable at 1:1000 ratio but problematic at 1:100: - manifest, revolutionary, next-generation - robust, scalable, comprehensive - synergy, leverage, utilize ## Replacement Guidelines | Instead of | Use | |-----------|-----| | "plays a crucial role" | "handles" / "manages" / "does" | | "seamlessly integrates" | "works with" / "connects to" | | "cutting-edge" | "new" / "recent" / specific tech name | | "Moreover," | [just start the next sentence] | | "comprehensive solution" | [specific description of what it does] | | "dramatically improves" | [specific metric: "reduces latency by 40%"] | | "robust" | "handles X requests/second" / "99.9% uptime" | ## Authenticity Markers to Include Strong authentic content includes: 1. **Specific opinions**: "I prefer X because..." not "X is preferred" 2. **Acknowledged trade-offs**: "This approach sacrifices Y for Z" 3. **Real-world constraints**: "Budget limited us to..." 4. **Uncertainty where appropriate**: "We're not sure yet whether..." 5. **Varied sentence structure**: Mix short and long, different openings 6. **Domain-specific vocabulary**: Use actual technical terms, not generic descriptions ## Application Process When generating or reviewing content: 1. **Scan** for critical banned patterns 2. **Count** contextual pattern frequency 3. **Check** structural variety 4. **Suggest** specific replacements 5. **Verify** authenticity markers present ## Examples ### Before (AI-Detected) > The platform seamlessly integrates cutting-edge technology to dramatically improve workflow efficiency. Moreover, it plays a crucial role in enabling next-generation solutions. In conclusion, this comprehensive approach transforms how teams collaborate. ### After (Authentic) > The platform connects to existing tools through standard APIs. Initial tests show 40% faster task completion. Teams report fewer context switches between applications. ## Script Reference For automated scanning, use `scripts/pattern_scanner.py` which: - Counts pattern frequencies - Flags critical violations - Generates replacement suggestions - Produces authenticity score (0-100) ## Integration This skill works with: - `/writing-validator` command for explicit validation - `writing-validator` agent for deep analysis - Any content generation task automatically ## References - @$AIWG_ROOT/agentic/code/addons/voice-framework/README.md — Voice framework for target style profiles - @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/instruction-comprehension.md — Parsing content requirements accurately - @$AIWG_ROOT/agentic/code/frameworks/sdlc-complete/README.md — SDLC framework context for documentation quality - @$AIWG_ROOT/docs/cli-reference.md — CLI reference for writing-related commands - @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/research-before-decision.md — Research patterns before making writing recommendations
Related Skills
pattern-selector
Recommends the right LLM pipeline pattern for a use case — simple chain, embedded agent, state machine, RAG, eval loop, or dynamic prompt
aiwg-orchestrate
Route structured artifact work to AIWG workflows via MCP with zero parent context cost
venv-manager
Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.
pytest-runner
Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.
vitest-runner
Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.
eslint-checker
Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.
repo-analyzer
Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.
pr-reviewer
Review GitHub pull requests for code quality, security, and best practices. Use for automated PR feedback and approval workflows.
YouTube Acquisition
yt-dlp patterns for acquiring content from YouTube and video platforms
Quality Filtering
Accept/reject logic and quality scoring heuristics for media content
Provenance Tracking
W3C PROV-O patterns for tracking media derivation chains and production history
Metadata Tagging
opustags and ffmpeg patterns for applying metadata to audio and video files