Explorar skills

Full pull request lifecycle — create branches, commit changes, open PRs, monitor CI status, auto-fix failures, and merge. Works with gh CLI or falls back to git + GitHub REST API via curl.

Use when code needs restructuring without changing observable behavior.

Guidance for building and executing B2B partner marketing programs, including co-marketing, influencer partnerships, channel programs, and nearbound strategy. Trigger when a user asks about partner marketing, co-marketing, channel programs, agency partnerships, influencer strategy, or partnership structure.

Side-by-side comparison of your current design against Stitch variations. Cherry-pick improvements at section-level and token-level, then delegates code changes to existing design skills. Use when: 'stitch compare', 'compare stitch designs', 'pick stitch design', 'adopt stitch changes', 'stitch diff', 'review stitch variations'.

Usar ao projetar ou usar busca de código estruturada com suporte MCP, incluindo fluxos de trabalho de busca, consulta AST, inventário de símbolos e extração limitada.

Use quando o usuário precisar de fluxos de briefing baseados em tópicos, configuração durável de tópicos, envio recorrente de notícias ou análise de eventos de acompanhamento dentro do espaço de trabalho Skrya. Acione para solicitações em chinês como "每天帮我关注", "每天早上8点推送", "定期简报", "官媒信息和新闻", ou "今天有什么重要".

Compare Claude Code output with full config vs minimal config using standardized tasks per stack.

Inspect and test web UIs using screenshots, browser automation, accessibility snapshots, and console/network signals with structured visual evidence collection.

Generate a 2026-grade content brief for a target query — combines classic SERP analysis with AI Overview / Perplexity / ChatGPT citation analysis, entity recommendations (target ≥15 recognized entities), topical authority gap map, intent-mapped heading structure, internal linking targets, FAQ from PAA + Reddit + AI prompt mining, and explicit E-E-A-T requirements (author entity profile, first-part

Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.

Audit codebases for leaked secrets and hardcoded credentials, generate .env templates, configure secrets management with AWS Secrets Manager, Vault, Doppler, or GCP Secret Manager, set up credential rotation, and integrate secrets into CI/CD pipelines via OIDC federation

Complete technical program management intelligence — cross-functional execution, milestone tracking, dependency management, technical risk, stakeholder coordination, and shipping complex programs on time at scale