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Get Started Free →Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
.claude/skills/affaan-m-literature-review/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flashlowest | 99% | 439 |
| gemini-3.1-pro-preview | 100% | 9 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 10% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 52% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 94% | 0% |
Use this skill when the task is to find, screen, synthesize, and cite a body of academic or technical literature.
technical reports.
screening and exclusion.
Ask the user which level of rigor is needed. If unspecified, default to a scoping review for exploratory work and a systematic review for publication or clinical claims.
Convert the prompt into a searchable research question.
For clinical or biomedical work, use PICO:
For technical work, use:
Create a search protocol before collecting sources:
Minimum useful database set:
patent databases, standards bodies, or official technical docs.
Keep a search log that makes the review reproducible:
markdown| Database | Date searched | Query | Filters | Results | Export | | --- | --- | --- | --- | ---: | --- | | PubMed | 2026-05-11 | `("CRISPR"[tiab] OR "Cas9"[tiab]) AND "sickle cell"[tiab]` | 2020:2026, English | 86 | PMID list | | arXiv | 2026-05-11 | `CRISPR sickle cell gene editing` | q-bio, 2020:2026 | 9 | BibTeX |
Save raw IDs, URLs, DOIs, abstracts, and notes separately from the final prose.
Deduplicate in this order:
Record how many duplicates were removed.
Screen in stages:
For systematic work, record exclusion reasons:
Use a structured extraction table:
markdown| Study | Design | Population/Data | Method | Comparator | Outcome | Key finding | Limitations | | --- | --- | --- | --- | --- | --- | --- | --- | | Author Year | RCT/cohort/review/etc. | sample or corpus | method | baseline | measured outcome | result | caveat |
For technical papers, include dataset, benchmark, metric, baseline, and reproducibility notes.
Group evidence by theme rather than summarizing papers one by one.
Useful synthesis lenses:
Separate claims by confidence:
Before finalizing:
markdown# Literature Review: <Topic> Generated: <date> Review type: <narrative | scoping | systematic | meta-analysis> Search window: <dates> Databases: <list> ## Research Question ## Search Strategy ## Inclusion and Exclusion Criteria ## Evidence Summary ## Thematic Synthesis ## Gaps and Limitations ## References ## Search Log
limited to that database.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | fail→pass | 18,632 | 12,500 | -33% | 1 | 1 | 0% | 2,856 | 3,139 | +10% | 0 | 0 | — |
case-01 | fail→pass | 38,314 | 38,512 | +1% | 1 | 1 | 0% | 6,208 | 7,364 | +19% | 0 | 0 | — |
case-05 | pass→pass | 9,731 | 11,558 | +19% | 1 | 1 | 0% | 1,702 | 3,294 | +94% | 0 | 0 | — |
case-02 | fail→fail | 38,789 | 29,268 | -25% | 1 | 1 | 0% | 6,208 | 6,034 | -3% | 0 | 0 | — |
case-03 | fail→pass | 38,706 | 36,858 | -5% | 1 | 1 | 0% | 6,207 | 7,363 | +19% | 0 | 0 | — |
case-06 | pass→pass | 16,055 | 12,916 | -20% | 1 | 1 | 0% | 2,726 | 3,264 | +20% | 0 | 0 | — |
case-07 | fail→pass | 15,728 | 15,024 | -4% | 1 | 1 | 0% | 2,319 | 3,525 | +52% | 0 | 0 | — |
case-08 | fail→pass | 13,218 | 16,041 | +21% | 1 | 1 | 0% | 1,922 | 3,732 | +94% | 0 | 0 | — |
case-09 | pass→pass | 14,798 | 12,701 | -14% | 1 | 1 | 0% | 2,149 | 3,151 | +47% | 0 | 0 | — |
case-10 | pass→pass | 16,634 | 16,219 | -2% | 1 | 1 | 0% | 2,410 | 3,383 | +40% | 0 | 0 | — |
case-11 | fail→pass | 11,801 | 12,166 | +3% | 1 | 1 | 0% | 2,023 | 3,274 | +62% | 0 | 0 | — |
case-12 | fail→pass | 22,155 | 26,200 | +18% | 1 | 1 | 0% | 3,833 | 5,534 | +44% | 0 | 0 | — |
case-13 | pass→pass | 15,361 | 11,425 | -26% | 1 | 1 | 0% | 2,318 | 2,906 | +25% | 0 | 0 | — |
case-14 | pass→pass | 8,438 | 8,699 | +3% | 1 | 1 | 0% | 1,392 | 2,455 | +76% | 0 | 0 | — |
case-15 | pass→pass | 12,378 | 9,844 | -20% | 1 | 1 | 0% | 1,742 | 2,665 | +53% | 0 | 0 | — |
case-16 | pass→pass | 9,144 | 7,113 | -22% | 1 | 1 | 0% | 1,273 | 2,211 | +74% | 0 | 0 | — |
case-17 | pass→pass | 9,996 | 5,423 | -46% | 1 | 1 | 0% | 1,447 | 1,955 | +35% | 0 | 0 | — |
case-18 | pass→pass | 9,857 | 6,302 | -36% | 1 | 1 | 0% | 1,484 | 1,979 | +33% | 0 | 0 | — |
case-19 | pass→pass | 14,490 | 11,406 | -21% | 1 | 1 | 0% | 2,167 | 2,836 | +31% | 0 | 0 | — |
case-20 | fail→fail | 18,509 | 16,946 | -8% | 1 | 1 | 0% | 2,638 | 3,656 | +39% | 0 | 0 | — |
case-21 | pass→pass | 26,495 | 25,471 | -4% | 1 | 1 | 0% | 3,651 | 4,748 | +30% | 0 | 0 | — |
case-22 | pass→pass | 9,491 | 9,053 | -5% | 1 | 1 | 0% | 1,599 | 2,702 | +69% | 0 | 0 | — |
case-23 | pass→pass | 16,363 | 18,814 | +15% | 1 | 1 | 0% | 2,677 | 4,242 | +58% | 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. 23 cases were attempted. The headline lift of +30 percentage points is the difference between those two pass rates over the 23 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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/3/2026 | +18% |
Other measured skills in the registry, with their headline benchmark lift.