skills/43-wentorai-research-plugins/skills/literature/search/boolean-search-guide/SKILL.md
Master Boolean operators and advanced search syntax for academic databases
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research boolean-search-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
A skill for constructing precise, reproducible search queries using Boolean operators across major academic databases. Covers AND, OR, NOT logic, proximity operators, truncation, field codes, and strategies for building systematic search strings.
AND — Narrows results. Both terms must appear.
"machine learning" AND "drug discovery"
OR — Broadens results. Either term may appear.
"deep learning" OR "neural network"
NOT — Excludes results. Removes records containing the term.
cancer NOT "lung cancer"
() — Groups terms to control evaluation order.
(COVID-19 OR SARS-CoV-2) AND (vaccine OR vaccination)
Databases evaluate Boolean expressions in this order unless parentheses override:
Always use parentheses to make your intent explicit:
# Ambiguous (results depend on database precedence):
sleep disorders OR insomnia AND cognitive performance
# Clear (intended meaning explicit):
(sleep disorders OR insomnia) AND cognitive performance
* — Truncation (unlimited characters)
therap* matches therapy, therapies, therapeutic, therapeutics
? — Single-character wildcard
wom?n matches woman, women
$ — Optional character (some databases)
behavio$r matches behavior, behaviour
| Database | Operator | Example | Meaning | |----------|----------|---------|---------| | PubMed | Not supported | -- | Use phrase search instead | | Web of Science | NEAR/n | climate NEAR/3 adaptation | Within 3 words | | Scopus | W/n | gene W/5 therapy | Within 5 words, ordered | | Scopus | PRE/n | drug PRE/3 resistance | First term precedes second | | ProQuest | N/n, P/n | poverty N/5 education | Within 5 words | | EBSCO | Nn | mental N3 health | Within 3 words |
PubMed:
"machine learning"[Title]
"Smith J"[Author]
"Nature"[Journal]
"2020/01/01"[Date - Publication] : "2024/12/31"[Date - Publication]
Web of Science:
TI=("deep learning")
AU=(Smith, John)
SO=(Nature)
PY=(2020-2024)
Scopus:
TITLE("deep learning")
AUTH(Smith)
SRCTITLE(Nature)
PUBYEAR > 2019
def build_pico_search(population: str, intervention: str,
comparison: str, outcome: str) -> str:
"""
Construct a Boolean search string from PICO components.
Args:
population: Target population terms (OR-separated)
intervention: Intervention terms (OR-separated)
comparison: Comparator terms (OR-separated, may be empty)
outcome: Outcome terms (OR-separated)
Returns:
Complete Boolean search string
"""
blocks = []
blocks.append(f"({population})")
blocks.append(f"({intervention})")
if comparison:
blocks.append(f"({comparison})")
blocks.append(f"({outcome})")
return " AND ".join(blocks)
# Example: Effect of mindfulness on anxiety in college students
query = build_pico_search(
population='"college students" OR "university students" OR undergraduates',
intervention='mindfulness OR "mindfulness-based stress reduction" OR MBSR',
comparison='"wait list" OR "waitlist" OR "usual care" OR "control group"',
outcome='anxiety OR "generalized anxiety" OR GAD OR "anxiety symptoms"'
)
print(query)
1. Identify key concepts from your research question
2. List synonyms, related terms, and variant spellings for each concept
3. Combine synonyms within each concept using OR
4. Connect concept blocks using AND
5. Test the search in your target database
6. Review the first 50 results for relevance
7. If recall is too low: add more synonyms, use truncation
8. If precision is too low: add more AND blocks, use field limits
9. Document the final search string with date and result count
Language: English, Chinese, Spanish, etc.
Date range: Publication year or date added
Document type: Journal article, review, conference paper
Study design: RCT, cohort, case-control (PubMed clinical queries)
Species: Human, animal (PubMed)
Open access: Free full text available
PubMed provides validated search filters (hedges) for specific study types. Append these to your search:
# Therapy/Intervention (sensitive):
AND (randomized controlled trial[pt] OR controlled clinical trial[pt]
OR randomized[tiab] OR randomly[tiab] OR trial[tiab])
# Diagnosis (specific):
AND (sensitivity and specificity[MeSH] OR predictive value of tests[MeSH]
OR accuracy[tiab])
# Systematic Reviews:
AND (systematic review[pt] OR meta-analysis[pt] OR systematic[sb])
For systematic reviews and reproducible research, always record:
Store search strategies in a version-controlled file alongside your project so they can be independently verified and updated.
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.