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yxc023
GitHub creator profile

yxc023

Repository-level view of 19 collected skills across 2 GitHub repositories.

skills collected
19
repositories
2
updated
2026-05-22
repository explorer

Repositories and representative skills

custom-nested-skill
software-quality-assurance-analysts-and-testers

A nested custom skill for testing multi-level directory support

2026-04-23
custom-skill
software-quality-assurance-analysts-and-testers

A custom skill for testing OPENCODE_CONFIG_DIR feature

2026-04-23
nested-skill
software-quality-assurance-analysts-and-testers

A nested skill for testing multi-level directory support

2026-04-23
test-skill
software-quality-assurance-analysts-and-testers

A test skill for regression testing

2026-04-23
ce-brainstorm
project-management-specialists

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 โ€” even if they don't explicitly ask to brainstorm.

2026-04-20
ce-compound
project-management-specialists

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

2026-04-20
ce-ideate
management-analysts-131111

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
data-scientists-152051

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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