domains / families / research / auto-research

auto-research

combined

Run fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings. Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis

When to use

• User asks a question where multiple valid approaches exist
• Claude is uncertain about algorithm details or API usage
• Design/architecture choices need comparison
• The user explicitly asks to search the web or consult ChatGPT and approves the proposed query

When not to use

• Requires a user-configured, pinned browser automation connector if browser consultation is used
• ChatGPT consultation is optional; use ordinary web search when it meets the need
• GPT response time varies (10-30s typically)
• Web search quality depends on available sources
• Does not replace expert domain knowledge — always let user make the final call

Skill page

Conflict

The three copies use the same name but describe three different procedures. dzhng/skills defines a local optimization loop that maps tunable parameters against a benchmark. huytieu/COG-second-brain is a thin pointer that delegates to a different playbook and subagents. sickn33/agentic-awesome-skills defines an external research workflow that requires explicit user consent before web search or ChatGPT consultation. None of the copies is a reproduction of the others, so crowning one would hide real alternatives.