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Get Started Free →Master Boolean operators and advanced search syntax for academic databases
.claude/skills/brycewang-stanford-boolean-search-guide/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 195% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 178% | 0% |
| case-13 | ✓→✗ | ▼ Worse | 40% | 0% |
| case-21 | ✓→✓ | = Same ✓ | 54% | 0% |
| case-01 | ✓→✓ | = Same ✓ | 99% | 0% |
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 > 2019pythondef 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 countLanguage: 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 availablePubMed 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.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-21 | pass→pass | 13,174 | 15,748 | +20% | 1 | 1 | 0% | 2,497 | 3,846 | +54% | 0 | 0 | — |
case-01 | pass→pass | 14,984 | 27,063 | +81% | 1 | 1 | 0% | 2,706 | 5,395 | +99% | 0 | 0 | — |
case-02 | pass→pass | 7,793 | 3,784 | -51% | 1 | 1 | 0% | 1,135 | 1,893 | +67% | 0 | 0 | — |
case-03 | pass→pass | 5,884 | 3,663 | -38% | 1 | 1 | 0% | 956 | 1,853 | +94% | 0 | 0 | — |
case-04 | pass→pass | 4,340 | 3,681 | -15% | 1 | 1 | 0% | 705 | 1,985 | +182% | 0 | 0 | — |
case-05 | pass→pass | 4,368 | 3,562 | -18% | 1 | 1 | 0% | 732 | 1,957 | +167% | 0 | 0 | — |
case-10 | pass→pass | 8,611 | 5,502 | -36% | 1 | 1 | 0% | 1,189 | 2,279 | +92% | 0 | 0 | — |
case-06 | pass→pass | 5,292 | 3,597 | -32% | 1 | 1 | 0% | 700 | 1,955 | +179% | 0 | 0 | — |
case-07 | fail→pass | 4,973 | 5,812 | +17% | 1 | 1 | 0% | 744 | 2,196 | +195% | 0 | 0 | — |
case-08 | pass→pass | 6,055 | 3,928 | -35% | 1 | 1 | 0% | 1,085 | 2,008 | +85% | 0 | 0 | — |
case-09 | pass→pass | 5,526 | 5,746 | +4% | 1 | 1 | 0% | 1,067 | 2,303 | +116% | 0 | 0 | — |
case-11 | pass→pass | 13,242 | 7,829 | -41% | 1 | 1 | 0% | 2,006 | 2,448 | +22% | 0 | 0 | — |
case-12 | fail→pass | 3,724 | 2,839 | -24% | 1 | 1 | 0% | 638 | 1,776 | +178% | 0 | 0 | — |
case-13 | pass→fail | 15,178 | 17,208 | +13% | 1 | 1 | 0% | 2,973 | 4,157 | +40% | 0 | 0 | — |
case-14 | pass→pass | 13,784 | 10,307 | -25% | 1 | 1 | 0% | 2,138 | 2,926 | +37% | 0 | 0 | — |
case-20 | pass→pass | 12,559 | 14,454 | +15% | 1 | 1 | 0% | 2,058 | 3,568 | +73% | 0 | 0 | — |
case-15 | pass→pass | 11,496 | 5,175 | -55% | 1 | 1 | 0% | 2,279 | 2,293 | +1% | 0 | 0 | — |
case-16 | fail→fail | 10,577 | 11,719 | +11% | 1 | 1 | 0% | 1,809 | 3,118 | +72% | 0 | 0 | — |
case-17 | pass→pass | 15,395 | 11,692 | -24% | 1 | 1 | 0% | 2,125 | 3,314 | +56% | 0 | 0 | — |
case-18 | pass→pass | 17,388 | 16,773 | -4% | 1 | 1 | 0% | 2,488 | 3,748 | +51% | 0 | 0 | — |
case-19 | pass→pass | 16,195 | 12,925 | -20% | 1 | 1 | 0% | 2,605 | 3,358 | +29% | 0 | 0 | — |
case-22 | pass→pass | 7,955 | 13,702 | +72% | 1 | 1 | 0% | 1,616 | 3,368 | +108% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.