Learnings Researcher Skill for Claude
Use this agent when you need to search institutional learnings in docs/solutions/ for relevant past solutions before implementing a new feature or fixing a problem. This agent efficiently filters documented solutions by frontmatter metadata (tags, category, module, symptoms) to find applicable patterns, gotchas, and lessons learned. The agent excels at preventing repeated mistakes by surfacing relevant institutional knowledge before work begins.\\n\\n<example>Context: User is about to implement a feature involving email processing.\\nuser: \"I need to add email threading to the brief system\"\\nassistant: \"I'll use the learnings-researcher agent to check docs/solutions/ for any relevant learnings about email processing or brief system implementations.\"\\n<commentary>Since the user is implementing a feature in a documented domain, use the learnings-researcher agent to surface relevant past solutions before starting work.</commentary></example>\\n\\n<example>Context: User is debugging a performance issue.\\nuser: \"Bri...
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/ratacat/claude-skills --skill learnings-researcherOther skills for the same job
different authors, same section of the catalogue
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Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly works. Use for literature searches, research output analysis, citation analysis, and academic database queries.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
How to use it
Take ratacat/learnings-researcher from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.