Desenvolvimento

Instrument web/web-app code with structured debug logging via a global variable (window.__debug_logs). Produces a clean JSON timeline for reproducing and diagnosing bugs. Use when user wants to debug a feature or track down a bug.

Capture current work context for handoff to another agent/developer. Gathers git state, todos, and modified files into a structured handoff document saved to the related spec folder.

Perform thorough code reviews focusing on unused code, duplications, coding patterns, bugs, and optimizations. Use when user wants code reviewed or audited. Read-only - outputs findings without making changes.

Este pacote de habilidades gerencia a produção de vídeos promocionais de 60-90 segundos a partir de descrições de produtos, sites, capturas de tela de aplicativos ou repositórios GitHub. Ele abrange brief, storyboard, materiais, edição HyperFrames, design de BGM e entrega, sendo ideal para lançamentos de produtos, introduções de projetos ou promoções de código aberto.

Before committing to an implementation plan, run this skill to stress-test the proposed architecture. Catches over-engineering, circular dependencies, missing failure modes, security gaps, and scalability cliffs — before any code is written. Acts as a "second eye" on the plan.

Strip AI-slop patterns from reasoning traces (chain-of-thought, extended thinking, agent decomposition) — not final prose. Reasoning text has its own slop catalog that regular unslop doesn't target: over-explaining the question, over-hedging, over-decomposing trivial problems into 6-bullet substeps, infinite-loop rationalization. Trigger: /unslop-reasoning, "clean up my reasoning", "fix this chain

Systematic code review skill covering both requesting a review (pre-commit checklist) and receiving and responding to review feedback. Checks code quality, security, test coverage, architectural alignment, and documentation before any code is committed.

Humanize LLM output so it reads like a careful human wrote it. Subtracts AI-isms (sycophancy, tricolons, em-dash overuse, "delve"/"tapestry"/"testament", hedging stacks, tidy five-paragraph shapes), engineers burstiness and calibrated uncertainty, and preserves technical accuracy. Supports intensity levels: subtle, balanced (default), full, voice-match, anti-detector. Use when user says "humanize

Automatiza o fluxo de trabalho da Wiki LLM de Karpathy, convertendo URLs da web, GitHub e YouTube em páginas wikilinkadas, bem estruturadas e citáveis, com linting e referências automáticas. Invoque com /pin-llm-wiki.

Humanize LLM output so it reads like a careful human wrote it. Subtracts AI-isms (sycophancy, tricolons, em-dash overuse, "delve"/"tapestry"/"testament", hedging stacks, tidy five-paragraph shapes), engineers burstiness and calibrated uncertainty, and preserves technical accuracy. Supports intensity levels: subtle, balanced (default), full, voice-match, anti-detector. Use when user says "humanize

Before writing any implementation code, define the acceptance criteria and test cases that the code must satisfy. Agents then write code to pass these tests — not to match a vague description. Eliminates "it works on my machine" and "I think this is what you wanted" outcomes.

<! TÜRKÇE AÇIKLAMA ─────────────── Bu skill, onaylanmış bir scope veya fikirden somut, uygulanabilir bir implementasyon planı üretir. Hangi dosya değişecek, hangi sırayla, kim yapacak, ne kadar sürecek, hangi riskler var — hepsini netleştirir. Planın çıktısı doğrudan executing…