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Found 51 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.
Discovers how analytics tracking calls are actually written in this codebase — the concrete SDK calls, function signatures, and import patterns used to send events. Use this skill whenever you need to understand the existing analytics instrumentation patterns before adding new tracking, when someone asks "how do we track events here?", "show me the analytics setup", "what's the analytics pattern in this codebase?", or any time the instrument-events or discover-event-surfaces skills are about to run and you need to know the correct coding style to follow. Outputs a deduplicated list of patterns with generalized examples and the file paths where each pattern appears, plus the dominant event and property naming conventions inferred from those call sites. Always use this skill before writing any analytics instrumentation code.
Structured UX evaluation that produces quantitative assessments, identifies specific issues, and routes to the right Intent skill for resolution. Part of the Intent design strategy system. Runs heuristic evaluations, cognitive walkthroughs, anti-pattern detection, and task success analysis. Scores, categorizes, and prioritizes findings — then maps every issue to the skill that fixes it. Trigger on: UX review, design audit, heuristic evaluation, usability assessment, "review this design", "what's wrong with this", "evaluate the experience", "is this accessible", "check for dark patterns", "how good is this UX", "rate this design", "find the problems", or any request to systematically assess the quality of a user experience. This is the diagnostic entry point of the Intent system — the UX doctor that diagnoses issues and refers to specialists.
Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors
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.
Detects and enforces project-specific coding conventions by analyzing existing codebase patterns. Learns naming conventions, folder structure, test organization, and coding style from the existing code. Load when: "conventions", "coding standards", "project patterns", "enforce style", "detect patterns", "learn conventions", "code consistency".
Analyze and optimize slow SQL queries. Use when the user says a query is slow, asks to optimize or speed up SQL, wants to find anti-patterns, needs index recommendations, or asks for a query rewrite. Also use when EXPLAIN output shows full table scans or poor join strategies.
Perform systematic self-review of code changes before commits using structured checklist. Validates architecture boundaries, code quality, test coverage, documentation, and project-specific anti-patterns. Use before committing, creating PRs, or when user says "review my changes", "self-review", "check my code". Adapts to Python, JavaScript, TypeScript, Go, Rust projects.
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.
Grades a specified set of test methods individually and produces a concise table mapping each test (fully-qualified name) to a letter grade (A–F), a score band, and a one-line note — designed to be posted as a PR comment. Use when the caller wants per-test feedback on a curated list of methods (for example, the new or modified tests in a pull request), not a suite-wide audit. Polyglot: .NET, Python, TS/JS, Java, Go, Ruby, Rust, Swift, Kotlin, PowerShell, C++. Input is a list of test methods (or method bodies / file+line spans); output is a compact markdown table plus a short summary. DO NOT USE FOR: full suite audits (use test-quality-auditor agent or test-anti-patterns), writing new tests (use code-testing-generator agent or writing-mstest-tests), fixing failures, or measuring code coverage.