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Braselog

Reusable agent skills published under this GitHub owner—sorted by stars with aggregate signals below.
15 skills30 GitHub stars0 weekly installsGitHub
Strongest repo by stars
braselog/researchassistant

15 skills · 30 stars total

Open repo

15 skills

next
Python

Assess current project state and suggest the most valuable next action. The primary entry point for users who aren't sure what to do. Analyzes context, checks for issues, and recommends specific skills or actions.

write-background
Python

Draft or update the background/introduction section based on literature in .research/literature/. Use when the user types /write_background, after completing literature review, or when background.md is empty but literature files exist.

summarize-meeting
Python

Extract action items, decisions, and key points from meeting transcripts. Automatically routes items to tasks.md or GitHub Issues based on complexity. Use when the user types /summarize_meeting or after running /transcribe.

hypothesis-generation
Python

Generate testable scientific hypotheses from observations, data, or research questions. Develop competing explanations, design experiments, and formulate predictions. Use during the PLANNING phase or when developing research aims.

review-script
Python

Review a script for documentation quality, code standards, and reproducibility. Helps ensure code is ready for the methods section. Use when the user types /review_script, after writing a new script, before writing methods, or when passive checks flag undocumented scripts.

deep-research
Python

Conduct a thorough literature search on a topic with verified citations. Use when the user types /deep_research, asks to "research a topic", "find papers on", or needs literature review. CRITICAL - Never fabricate citations. Every claim must have a verifiable source.

statistical-analysis
Python

Comprehensive statistical analysis toolkit for research. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, and APA reporting. Use when the user asks about statistics, needs help analyzing data, or when writing methods sections that include statistical approaches.

literature-review
Python

Conduct comprehensive, systematic literature reviews using multiple databases (PubMed, bioRxiv, Semantic Scholar, OpenAlex). Creates documented searches, synthesizes findings thematically, verifies citations, and generates professional markdown reports with multiple citation styles (APA, Nature, Vancouver). Use when the user needs thorough literature research or types /deep_research.

scientific-writing
Python

Write scientific manuscripts with proper structure (IMRAD), citations (APA/AMA/Vancouver), figures/tables, and reporting guidelines (CONSORT/STROBE/PRISMA). Use when drafting any manuscript section, improving writing clarity, or preparing for journal submission.

quarterly-review
Python

Conduct a quarterly review of your overall research mission and direction. This is a user-level review stored in ~/.researchAssistant/. Use when the user types /quarterly_review, every 3 months, after major project milestones, or when questioning research direction.

write-methods
Python

Generate or update the methods section based on actual scripts in the pipeline. Links code directly to reproducible documentation. Use when the user types /write_methods, after completing scripts, when methods.md is out of sync with code, or before writing results.

task
Python

Rapid task capture without breaking flow. Use when the user types /task or wants to quickly add a todo item to tasks.md. Supports priority flags (!high, !low). Use this instead of /note for action items.

transcribe
Python

Transcribe audio files from meetings into text documents using Whisper. Use when the user types /transcribe, has a new audio recording, or when RA detects new audio files in meetings/audio/. Supports speaker diarization with pyannote.

scientific-visualization
Python

Create publication-quality scientific figures with matplotlib, seaborn, and plotly. Includes multi-panel layouts, error bars, significance markers, colorblind-safe palettes, and journal-specific export (PDF/EPS/TIFF). Use when creating figures for manuscripts, presentations, or any research visualization.

exploratory-data-analysis
Python

Perform comprehensive exploratory data analysis on research data. Automatically analyze data structure, quality, distributions, and generate insights. Use when the user provides a dataset, asks to "explore data", "analyze this file", or needs to understand their data before formal analysis.

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