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Found 207 Skills
Review Kafka producer and consumer performance configurations in both the live cluster (via Lenses MCP) and the codebase. Flags un-tuned defaults, anti-patterns and missing best practices. Use when user says "review Kafka performance", "check producer configs", "tune Kafka settings" or asks about throughput, batching or compression. Do NOT use for cluster sizing or capacity planning.
Analyzes the variety and depth of assertions across .NET test suites. Use when the user asks to evaluate assertion quality, find shallow testing, identify assertion-free tests (no assertions or only trivial ones like Assert.IsNotNull), flag self-referential or tautological assertions (output equals input on identity/round-trip operations), measure assertion coverage diversity, or audit whether tests verify different facets of correctness. Produces metrics and actionable recommendations. Works with MSTest, xUnit, NUnit, TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), other anti-patterns like flakiness or duplication (use test-anti-patterns), or fixing assertions.
Performs pseudo-mutation analysis on .NET production code to find gaps in existing test suites. Use when the user asks to find weak tests, discover untested edge cases, check if tests would catch a bug, or evaluate test effectiveness through mutation-style reasoning. Analyzes production code for mutation points (boundary conditions, boolean flips, null returns, exception removal, arithmetic changes) and checks whether existing tests would detect each mutation. Works with MSTest, xUnit, NUnit, and TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), detecting test anti-patterns (use test-anti-patterns), measuring assertion diversity (use assertion-quality), or running actual mutation testing tools.
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.
Code review and PR review skill for Python PySide6/Qt 6.8+ applications. Focuses on modern best practices, performance, thread safety, signal/slot patterns, Model/View architecture, QML integration, and async patterns. Use when reviewing Python Qt code, PySide6 PRs, GUI application code, or when asked to review code that uses QtWidgets, QtQuick, QtCore, QtGui, or any Qt module. Catches common anti-patterns, memory issues, thread violations, and suggests modern Qt 6.8+ idioms.
Analyzes Rails code quality, architecture, and patterns without modifying code. Use when the user wants a code review, quality analysis, architecture audit, or when user mentions review, audit, code quality, anti-patterns, or SOLID principles. WHEN NOT: Actually implementing fixes (use specialist agents), writing new tests (use rspec-agent), or generating new features.
Use when a Head of Ops, Knowledge Manager, or TPM-Internal needs to author, validate, or clean up company SOPs and internal runbooks (procurement intake, vendor offboarding, incident-comms cascade, employee onboarding, expense reimbursement, system-access provisioning, customer-escalation playbook) — including 5W2H completeness checks (Who-What-When-Where-Why-How-HowMuch), cross-link and orphan-page validation across a sprawling Notion/Confluence/Obsidian wiki, KB ingestion + hygiene reporting, ops onboarding doc generation, and runbook step verification (named owner, expected duration, observable success signal, rollback path, escalation contact). Pairs Kaoru Ishikawa's 5W2H method, Atul Gawande's *The Checklist Manifesto*, ISO 9001, ITIL v4 Service Operation, FDA 21 CFR Part 211, and Google SRE Workbook runbook discipline with deterministic stdlib-only Python tools that score completeness, detect anti-patterns, and emit prioritized cleanup lists. Distinct from `engineering/llm-wiki` (Karpathy-style personal PKM second brain), `engineering-team/runbook-generator` (system-ops production debugging runbook), `project-management/*` (Jira/Confluence delivery + ticket tracking), and sibling `business-operations/process-mapper` (BPMN process *design*, while knowledge-ops is process *documentation*).
Implement Spring Data JPA repositories, entities, and queries following modern best practices. Use for creating repositories (only for aggregate roots), writing queries (@Query, DTO projections), custom repositories (Criteria API, bulk ops), CQRS query services, entity relationships, and performance optimization. Covers patterns from simple repositories to advanced CQRS with detailed anti-patterns guidance.
Analyzes and optimizes SQL queries for performance. Use for index design, query rewriting, EXPLAIN/EXPLAIN ANALYZE interpretation, PostgreSQL tuning, N+1 prevention, CTE and window function optimization, join strategies, and common SQL anti-patterns.
Analyzes meeting transcripts and recordings to surface behavioral patterns, communication anti-patterns, and actionable coaching feedback. Use this skill whenever the user uploads or points to meeting transcripts (.txt, .md, .vtt, .srt, .docx), asks about their communication habits, wants feedback on how they run meetings, requests speaking ratio analysis, mentions filler words or conflict avoidance, or wants to compare their communication across time periods. Also trigger when users mention tools like Granola, Otter, Fireflies, or Zoom transcripts. Even if the user just says "look at my meetings" or "how do I come across in meetings" — use this skill.
Deep Agents framework — architectural decisions (when to use Deep Agents vs alternatives, backend strategies, subagent design, middleware approaches) AND code review (bugs, anti-patterns, improvements when reviewing Deep Agents code). Use when working with Deep Agents — designing a new system or reviewing existing code.
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.