Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →brycewang-stanford/slr-automation-guide
Tools and pipelines for automating systematic literature reviews
Passed 19 of 22 test cases with the skill, and 9 of 22 without it.
and 11 more in the full report
10 fixed9 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Which tool parameter should be passed to slr_tools RiskOfBias when evaluating non-randomized observational studies instead of RCTs?
| Checked | Without skill | With skill |
|---|---|---|
| Sets tool='robins_i' in RiskOfBias constructor | Missed | Passed |
The instructions your model reads when the skill loads.
Systematic Literature Reviews (SLRs) are rigorous, reproducible surveys of research evidence following protocols like PRISMA and Cochrane. This guide covers tools that automate the most time-consuming steps — deduplication, title/abstract screening, full-text assessment, and data extraction — using active learning, NLP, and AI agents. Key tools include ASReview, Rayyan, and custom pipelines.
Protocol Definition (PICO, inclusion/exclusion criteria)
↓
Database Search (PubMed, Scopus, Web of Science)
↓
Deduplication (ASReview, Rayyan, or custom)
↓
Title/Abstract Screening (AI-assisted prioritization)
↓
Full-text Assessment (relevance + quality)
↓
Data Extraction (structured tables)
↓
Quality Assessment (risk of bias)
↓
Synthesis + PRISMA Report
# Install ASReview
pip install asreview
# Launch web interface
asreview lab
# CLI screening
asreview simulate benchmark:van_de_Schoot_2017 \
-m nb -e tfidf \
--n_prior_included 5 --n_prior_excluded 5 \
-o results/simulation.asreview
test cases passed, vs 9 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.