techwavedev Avoid AI Writing — Audit & Rewrite
Detects and fixes AI writing patterns ("AI-isms") that make text sound machine-generated. Covers 21 pattern categories with a 43-entry word/phrase replacement table that maps each flagged term to a specific, plainer alternative.
When to Use This Skill
- When asked to "remove AI-isms," "clean up AI writing," or "make this sound less like AI"
- After drafting content with AI and before publishing
- When editing any text that sounds like it was generated rather than written
- When auditing documentation, blog posts, marketing copy, or internal communications for AI tells
What It Detects
21 pattern categories: formatting issues (em dashes, bold overuse, emoji headers, bullet-heavy sections), sentence structure problems (hedging, hollow intensifiers, rule of three), word/phrase replacements (43 entries like leverage→use, utilize→use, robust→reliable), template phrases, transition phrases, structural issues, significance inflation, copula avoidance, synonym cycling, vague attributions, filler phrases, generic conclusions, chatbot artifacts, notability name-dropping, superficial -ing analyses, promotional language, formulaic challenges, false ranges, inline-header lists, title case headings, and cutoff disclaimers.
Example
Prompt:
Audit this for AI writing patterns:
"In today's rapidly evolving AI landscape, developers are embarking on a pivotal journey to leverage cutting-edge tools that streamline their workflows. Moreover, these robust solutions serve as a testament to the industry's commitment to fostering seamless experiences."
Output: The skill returns four sections:
- Issues found — every AI-ism quoted (landscape, embarking, pivotal, leverage, cutting-edge, streamline, robust, serves as, testament to, fostering, seamless, Moreover, In today's rapidly evolving...)
- Rewritten version — "Developers are starting to use newer AI tools to simplify their work. These tools are reliable, and they're making development less painful."
- What changed — summary of edits
- Second-pass audit — re-reads the rewrite to catch any surviving tells
Limitations
- Does not detect AI-generated code, only prose
- Pattern matching is guideline-based, not absolute — some flagged words are fine in context
- The replacement table suggests alternatives but the best choice depends on context
- Cannot verify factual claims or find real citations to replace vague attributions
AGI Framework Integration
Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills
Memory-First Protocol
Cache compliance check results to avoid re-running expensive AWS API calls. Retrieve prior audit findings to track remediation progress across sessions.
# Check for prior security context before starting
python3 execution/memory_manager.py auto --query "prior security audit results for Avoid Ai Writing"
Storing Results
After completing work, store security decisions for future sessions:
python3 execution/memory_manager.py store \
--content "Audit findings: 3 critical IAM misconfigurations found and remediated" \
--type technical --project <project> \
--tags avoid-ai-writing security
Multi-Agent Collaboration
Share security findings with other agents so they avoid introducing vulnerabilities in their code changes.
python3 execution/cross_agent_context.py store \
--agent "<your-agent>" \
--action "Completed security audit — 3 critical findings fixed, compliance score 94%" \
--project <project>
Signed Audit Trail
All security findings are cryptographically signed with the agent's Ed25519 identity, providing tamper-proof audit logs for compliance reporting.
Semantic Cache for Compliance
Cache compliance check results (semantic_cache.py) to avoid redundant AWS API calls. Cache hit at similarity >0.92 returns prior results instantly.