mcpField
R
research-taste

research-taste

github.com/leo1oelProductivity042 installsUpdated 14w ago
Repository

About research-taste

research-taste is a research-guidance skill for choosing directions, evaluating ideas, and planning studies. It emphasizes mechanism, evidence, rigor, and quickly falsifiable plans over trend-following or polished narratives.

Key features

  1. 01Guidance for research direction
  2. 02Criteria for evaluating ideas
  3. 03Advice on rigorous methods
  4. 04Focused taste modules for task types

Use cases

  1. 01Selecting a research problem
  2. 02Critiquing a proposed idea
  3. 03Planning experiments and evaluation

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/leo1oel/leo-agent-skills --skill research-taste

After installing, type/research-tastein Claude Code to confirm it loaded.

Bundled filesreferences/tastes/method-and-rigor.mdreferences/tastes/problem-selection.md

Details

From the project README

Research taste

Apply the user's research taste when it would improve the answer: choosing directions, judging ideas, designing studies, interpreting results, or deciding what is worth doing. Treat everything here and in the modules as guidance, not ground truth. Distinguish preferences from evidence, and push back when the evidence disagrees.

Default stance

Prefer research that clarifies mechanisms, exposes bottlenecks, creates useful abstractions, or rules out plausible wrong directions. Be especially careful about chasing trends without understanding why they matter, confusing clean narratives with true mechanisms, overvaluing novelty and undervaluing correctness, scaling before debugging, ignoring failed experiments, reading summaries instead of papers, producing confident prose that hides uncertainty, and making research plans that are too expensive to falsify quickly.

Pick the module that fits the task

The detail lives in focused modules under references/tastes/. Read the one that fits; for open-ended research-direction work, read both.

  • Choosing what to work on — idea generation, problem selection, predictions as taste, critiquing ideas, depth-vs-breadth strategy. Read references/tastes/problem-selection.md.
  • Doing the work rigorously — experiment design, evaluation, engineering, and the artifacts to leave behind. Read references/tastes/method-and-rigor.md.

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