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yxc023
Perfil de criador do GitHub

yxc023

Visão por repositório de 19 skills coletadas em 2 repositórios do GitHub.

skills coletadas
19
repositórios
2
atualizado
2026-05-22
explorador de repositórios

Repositórios e skills representativas

custom-nested-skill
Analistas de garantia de qualidade de software e testadores

A nested custom skill for testing multi-level directory support

2026-04-23
custom-skill
Analistas de garantia de qualidade de software e testadores

A custom skill for testing OPENCODE_CONFIG_DIR feature

2026-04-23
nested-skill
Analistas de garantia de qualidade de software e testadores

A nested skill for testing multi-level directory support

2026-04-23
test-skill
Analistas de garantia de qualidade de software e testadores

A test skill for regression testing

2026-04-23
ce-brainstorm
Especialistas em gestão de projetos

Explore requirements and approaches through collaborative dialogue before writing a right-sized requirements document and planning implementation. Use for feature ideas, problem framing, when the user says 'let's brainstorm', or when they want to think through options before deciding what to build. Also use when a user describes a vague or ambitious feature request, asks 'what should we build', 'help me think through X', presents a problem with multiple valid solutions, or seems unsure about scope or direction — even if they don't explicitly ask to brainstorm.

2026-04-20
ce-compound
Especialistas em gestão de projetos

Document a recently solved problem to compound your team's knowledge

2026-04-20
ce-ideate
Analistas de gestão

Generate and critically evaluate grounded ideas about a topic. Use when asking what to improve, requesting idea generation, exploring surprising directions, or wanting the AI to proactively suggest strong options before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on X', 'surprise me', 'what would you change', or any request for AI-generated suggestions rather than refining the user's own idea.

2026-04-20
ce-optimize
Cientistas de dados

Run metric-driven iterative optimization loops. Define a measurable goal, build measurement scaffolding, then run parallel experiments that try many approaches, measure each against hard gates and/or LLM-as-judge quality scores, keep improvements, and converge toward the best solution. Use when optimizing clustering quality, search relevance, build performance, prompt quality, or any measurable outcome that benefits from systematic experimentation. Inspired by Karpathy's autoresearch, generalized for multi-file code changes and non-ML domains.

2026-04-20
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