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Found 42 Skills
Detect patterns, anomalies, and trends in code and data. Use when identifying code smells, finding security vulnerabilities, or discovering recurring patterns. Handles regex patterns, AST analysis, and statistical anomaly detection.
Early rug-risk triage for token launches and small DeFi deployments from public data—liquidity lock and pool events, dev and sniper wallet clustering, contract authority and transfer-risk checks, coordinated exits, and evidence-backed risk scores. Use when the user asks for rug pull detection, pump-and-dump signals, launch red flags, LP removal forensics, or cross-chain profit exit tracing—not for front-running trades, harassing teams, or certifying scams without on-chain proof.
This skill should be used when the user asks to "roast my code", "review code brutally", "find code sins", "what's wrong with my code", "shame my code", "critique this code", "find antipatterns", "code quality roast", or wants entertaining but actionable code criticism with severity-ranked fixes. Delivers brutally honest roasts with file:line citations and redemption paths.
Detect and remove AI-generated markers from Finnish text, making it sound like a native Finnish speaker wrote it. Use when asked to "humanize", "naturalize", or "remove AI feel" from Finnish text, or when editing .md/.txt files containing Finnish content. Identifies 26 patterns (12 Finnish-specific + 14 universal) and 4 style markers.
Reviews React Flow code for anti-patterns, performance issues, and best practices. Use when reviewing code that uses @xyflow/react, checking for common mistakes, or optimizing node-based UI implementations.
Run technical quality checks across accessibility, performance, theming, responsive design, and anti-patterns. Generates a scored report with P0-P3 severity ratings and actionable plan. Use when the user wants an accessibility check, performance audit, or technical quality review.
Skill for detecting institutional order flow patterns (absorption, exhaustion, imbalance, sweep) from L2 market depth and trade data.
Quick pragmatic review of .NET test code for anti-patterns that undermine reliability and diagnostic value. Use when asked to review tests, find test problems, check test quality, or audit tests for common mistakes. Catches assertion gaps, flakiness indicators, over-mocking, naming issues, and structural problems with actionable fixes. Use for periodic test code reviews and PR feedback. For a deep formal audit based on academic test smell taxonomy, use exp-test-smell-detection instead. Works with MSTest, xUnit, NUnit, and TUnit.
Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors
This skill should be used when the user asks to "rewrite my resume", "fix my resume", "humanize my resume", "make my resume sound human", "clean up my resume bullets", "reword my resume", or wants AI-sounding text in their resume rewritten with annotations explaining each change. Works for all resume and CV types: standard US, federal, academic, legal, medical, consulting, tech, executive, military transition, education, nonprofit, trades, creative, investment banking, and EU/Europass formats. Entry-level through executive.
Audit project directory structure for colocation, grouping, and anti-patterns. Use when creating files, organising components, or deciding where code should live.
Evaluates agent skills against Anthropic's best practices. Use when asked to review, evaluate, assess, or audit a skill for quality. Analyzes SKILL.md structure, naming conventions, description quality, content organization, and identifies anti-patterns. Produces actionable improvement recommendations.