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Get Started Free →Conduct a systematic literature review following the PRISMA framework with explicit search strategy, inclusion and exclusion criteria, quality assessment, and transparent synthesis. Use this skill when the user needs to design a reproducible literature search, apply PRISMA flow documentation, develop inclusion and exclusion criteria, assess study quality, or when they ask 'how do I do a systematic review', 'what is PRISMA', or 'how do I make my literature review reproducible'.
.claude/skills/asgard-ai-platform-grad-systematic-review/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -38% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 404% | 0% |
| case-22 | ✗→✓ | ▲ Improved | 58% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 45% | 0% |
A systematic review uses explicit, pre-defined methods to identify, select, appraise, and synthesize all relevant research on a specific question. Unlike narrative reviews, systematic reviews follow a reproducible protocol that minimizes bias in study selection and interpretation. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework provides the standard reporting structure, including the iconic flow diagram tracking records through identification, screening, eligibility, and inclusion.
IRON LAW: A systematic review must be REPRODUCIBLE — every search
decision, inclusion criterion, and quality assessment must be documented
so another researcher can replicate the process. If your review cannot
be replicated, it is a narrative review, NOT a systematic review.Key assumptions:
Formulate a focused question using a framework (PICO for interventions, PEO for qualitative, SPIDER for mixed methods). Register the protocol (e.g., PROSPERO). Define databases, search terms, date ranges, and language restrictions.
| Framework | Components | |-----------|-----------| | PICO | Population, Intervention, Comparison, Outcome | | PEO | Population, Exposure, Outcome | | SPIDER | Sample, Phenomenon of Interest, Design, Evaluation, Research type |
Search at least 3 databases (e.g., Scopus, Web of Science, PubMed). Use Boolean operators (AND, OR, NOT) with controlled vocabulary and free-text terms. Document every search string and date. Supplement with citation chaining (forward and backward), grey literature, and hand-searching key journals.
Apply inclusion/exclusion criteria in two phases:
Document the process in a PRISMA flow diagram:
Extract data into a standardized form. Assess quality using appropriate tools (e.g., Cochrane RoB for RCTs, CASP for qualitative, JBI checklists). Synthesize via meta-analysis (quantitative), thematic synthesis (qualitative), or narrative synthesis. Report per PRISMA 2020 checklist.
markdown## Systematic Review: [Research Question] ### Protocol - Question framework: [PICO/PEO/SPIDER] - Registration: [PROSPERO ID or equivalent] - Databases searched: [list] - Date range: [start-end] ### Search Strategy | Database | Search String | Records Found | |----------|--------------|---------------| | [name] | [Boolean query] | [N] | ### PRISMA Flow - Identified: [N] records - Duplicates removed: [N] - Screened (title/abstract): [N] - Excluded at screening: [N] - Full-text assessed: [N] - Excluded at full-text: [N] (reasons: ...) - Included in synthesis: [N] ### Inclusion/Exclusion Criteria | Criterion | Include | Exclude | |-----------|---------|---------| | Population | [specification] | [specification] | | Study type | [specification] | [specification] | | Language | [specification] | [specification] | | Date | [specification] | [specification] | ### Quality Assessment Summary | Study | Tool Used | Overall Rating | Key Concerns | |-------|-----------|---------------|--------------| | [author, year] | [RoB/CASP/JBI] | [high/moderate/low] | [specific issues] | ### Synthesis - [Key finding 1 with evidence strength] - [Key finding 2 with evidence strength] - Gaps identified: [what remains unknown] ### Limitations - [Search limitations] - [Assessment limitations]
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 46,675 | 25,343 | -46% | 1 | 1 | 0% | 8,280 | 6,028 | -27% | 0 | 0 | — |
case-02 | fail→pass | 45,104 | 25,793 | -43% | 1 | 1 | 0% | 8,281 | 6,223 | -25% | 0 | 0 | — |
case-03 | fail→pass | 46,377 | 23,291 | -50% | 1 | 1 | 0% | 8,276 | 5,113 | -38% | 0 | 0 | — |
case-04 | pass→pass | 21,468 | 17,838 | -17% | 1 | 1 | 0% | 3,120 | 4,516 | +45% | 0 | 0 | — |
case-05 | fail→pass | 10,552 | 47,386 | +349% | 1 | 1 | 0% | 1,906 | 9,602 | +404% | 0 | 0 | — |
case-06 | pass→pass | 29,222 | 36,522 | +25% | 1 | 1 | 0% | 4,828 | 6,610 | +37% | 0 | 0 | — |
case-07 | pass→pass | 10,274 | 8,705 | -15% | 1 | 1 | 0% | 1,932 | 2,988 | +55% | 0 | 0 | — |
case-08 | fail→fail | 7,639 | 8,032 | +5% | 1 | 1 | 0% | 1,286 | 2,662 | +107% | 0 | 0 | — |
case-09 | pass→pass | 10,056 | 7,102 | -29% | 1 | 1 | 0% | 1,593 | 2,598 | +63% | 0 | 0 | — |
case-10 | pass→pass | 10,234 | 5,368 | -48% | 1 | 1 | 0% | 1,663 | 2,273 | +37% | 0 | 0 | — |
case-11 | pass→pass | 7,427 | 4,313 | -42% | 1 | 1 | 0% | 1,116 | 2,072 | +86% | 0 | 0 | — |
case-12 | pass→pass | 9,342 | 6,262 | -33% | 1 | 1 | 0% | 1,451 | 2,417 | +67% | 0 | 0 | — |
case-13 | pass→pass | 9,896 | 6,717 | -32% | 1 | 1 | 0% | 1,635 | 2,538 | +55% | 0 | 0 | — |
case-14 | pass→pass | 9,923 | 6,565 | -34% | 1 | 1 | 0% | 1,690 | 2,492 | +47% | 0 | 0 | — |
case-15 | pass→pass | 12,047 | 13,021 | +8% | 1 | 1 | 0% | 1,985 | 3,468 | +75% | 0 | 0 | — |
case-16 | pass→pass | 13,600 | 14,562 | +7% | 1 | 1 | 0% | 2,337 | 3,738 | +60% | 0 | 0 | — |
case-17 | pass→pass | 12,207 | 14,261 | +17% | 1 | 1 | 0% | 2,106 | 3,843 | +82% | 0 | 0 | — |
case-18 | pass→pass | 14,169 | 10,107 | -29% | 1 | 1 | 0% | 2,427 | 3,154 | +30% | 0 | 0 | — |
case-19 | pass→pass | 12,319 | 13,292 | +8% | 1 | 1 | 0% | 2,155 | 3,725 | +73% | 0 | 0 | — |
case-20 | pass→pass | 9,959 | 11,847 | +19% | 1 | 1 | 0% | 1,478 | 3,027 | +105% | 0 | 0 | — |
case-21 | pass→pass | 10,509 | 6,239 | -41% | 1 | 1 | 0% | 1,463 | 2,293 | +57% | 0 | 0 | — |
case-22 | fail→pass | 16,815 | 18,858 | +12% | 1 | 1 | 0% | 2,830 | 4,459 | +58% | 0 | 0 | — |
case-23 | pass→pass | 8,805 | 9,159 | +4% | 1 | 1 | 0% | 1,526 | 3,045 | +100% | 0 | 0 | — |
case-24 | pass→pass | 17,394 | 13,832 | -20% | 1 | 1 | 0% | 3,037 | 3,809 | +25% | 0 | 0 | — |
case-25 | pass→pass | 4,249 | 4,453 | +5% | 1 | 1 | 0% | 617 | 1,984 | +222% | 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. 25 cases were attempted. The headline lift of +16 percentage points is the difference between those two pass rates over the 25 comparable cases.
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.