plugins/writing/skills/ai-pattern-detection/SKILL.md
Detect AI-generated writing patterns and suggest authentic alternatives. Use when reviewing or editing content, or when the user mentions authenticity or natural voice.
npx skillsauth add jmagly/aiwg ai-pattern-detectionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
These immediately identify content as AI-generated:
Words acceptable at 1:1000 ratio but problematic at 1:100:
| 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" |
Strong authentic content includes:
When generating or reviewing content:
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.
The platform connects to existing tools through standard APIs. Initial tests show 40% faster task completion. Teams report fewer context switches between applications.
For automated scanning, use scripts/pattern_scanner.py which:
This skill works with:
/writing-validator command for explicit validationwriting-validator agent for deep analysisdata-ai
Report which research-corpus radar sidecars are overdue for refresh. Computes staleness (days since last refresh vs the cadence window) for every radar, sorted most-overdue-first. Runs via `aiwg corpus radar-status`.
data-ai
Aggregate research-corpus radar sidecars into a corpus or per-cluster freshness report — totals, overdue count, per-cluster / per-GRADE / per-trajectory breakdowns, an overdue table, and per-radar rationale snippets. Runs via `aiwg corpus radar-report`.
testing
Scaffold radar/freshness sidecars for research-corpus REFs. Pulls title/authors from the citation sidecar and GRADE from the analysis doc, defaults the refresh cadence from GRADE and the cluster from a corpus-local map, and stamps documentation/radar/REF-XXX-radar.md. Runs via `aiwg corpus radar-init`.
data-ai
Compute an entity's publication trajectory — per-year paper counts, topic drift, hot-streak detection (≥3 consecutive A-grade years), and career phase. Runs via `aiwg corpus profile-temporal`.