companion-inc/feynman - 2페이지

ml-training-recipe

Find implementable ML training recipes from papers, datasets, docs, and code. Use when the user wants to fine-tune, train, reproduce, or choose a practical ML method, dataset, hyperparameter setup, or benchmark recipe.

원문 언어: 영어

contributing

Contribute changes to the Feynman repository itself. Use when the task is to add features, fix bugs, update prompts or skills, change install or release behavior, improve docs, or prepare a focused PR against this repo.

원문 언어: 영어

deep-research

Run a thorough, source-heavy investigation on any topic. Use when the user asks for deep research, a comprehensive analysis, an in-depth report, or a multi-source investigation. Produces a cited research brief with provenance tracking.

원문 언어: 영어

literature-review

Run a literature review using paper search and primary-source synthesis. Use when the user asks for a lit review, paper survey, state of the art, or academic landscape summary on a research topic.

원문 언어: 영어

paper-code-audit

Compare a paper's claims against its public codebase. Use when the user asks to audit a paper, check code-claim consistency, verify reproducibility of a specific paper, or find mismatches between a paper and its implementation.

원문 언어: 영어

paper-writing

Turn research findings into a polished paper-style draft with sections, equations, and citations. Use when the user asks to write a paper, draft a report, write up findings, or produce a technical document from collected research.

원문 언어: 영어

session-log

Write a durable session log capturing completed work, findings, open questions, and next steps. Use when the user asks to log progress, save session notes, write up what was done, or create a research diary entry.

원문 언어: 영어

source-comparison

Compare multiple sources on a topic and produce a grounded comparison matrix. Use when the user asks to compare papers, tools, approaches, frameworks, or claims across multiple sources.

원문 언어: 영어