From review-of-educational-research-skills
Executes systematic literature search, PRISMA screening, and data extraction for educational research reviews. Use when you need a reproducible search trail and structured evidence dataset.
How this skill is triggered — by the user, by Claude, or both
Slash command
/review-of-educational-research-skills:revedres-literature-synthesisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- The protocol is fixed and it is time to search the literature exhaustively
An RER systematic review's credibility rests on a reader's belief that you found everything that meets your criteria — and can prove it. Execute the protocol, logging every number for the PRISMA flow diagram (identification → screening → eligibility → included).
Summarizing is restating each study; synthesizing is making the studies answer your question together. Maintain a coding dataset as you extract:
| Column | What to capture |
|---|---|
| Study | author–year; the included report (watch for multiple reports of one sample) |
| Sample/context | learners, setting, grade/level, country — for moderator analysis and scope claims |
| Design | RCT / quasi-experiment / correlational / qualitative — for risk-of-bias and weighting |
| Construct/measure | exactly what was measured (so non-commensurable outcomes are not pooled) |
| Effect / finding | effect size + variance (meta-analysis) or coded finding (narrative synthesis) |
| Risk of bias | your appraisal on the a-priori tool (you do not re-run the study; you judge it) |
| Dependencies | shared samples / multiple effects per study (drives the variance model) |
This dataset feeds the organizing framework, the forest plot and coding tables, and the even-handed treatment of conflicting evidence. You appraise the primary studies (you are the field's reviewer-of-record); you do not re-collect their data.
The education literature is scattered across disciplines and document types, which creates predictable holes:
Document how you handled each, so a reviewer sees the gaps were anticipated, not missed.
【Databases + date】<sources searched, search date>
【PRISMA counts】identified / dedup / screened / full-text / excluded-w-reasons / included
【Screening reliability】κ or % agreement; conflicts resolved by <method>
【Snowballing】backward + forward to saturation? Y/N
【Grey literature】included? Y/N — publication-bias implication noted? Y/N
【Coding dataset】codebook applied; dependent effects + shared samples flagged? Y/N
【Coverage risks】<any eligible study/database a reviewer could name as missing>
【Next step】→ revedres-organizing-framework (impose the conceptual spine on the corpus)
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin review-of-educational-research-skillsDesigns and documents systematic literature searches for Psychological Bulletin reviews, covering databases, search strings, grey literature, deduplication, and PRISMA-compliant records flow.
Guides systematic, scoping, and narrative literature reviews using PRISMA/PRISMA-ScR protocols, Boolean/MeSH search strategies, databases (PubMed, Scopus, Web of Science, Embase), screening, extraction, synthesis, and reporting.
Plans and writes systematic reviews of scientific literature following PRISMA guidelines — from protocol registration through search, screening, risk-of-bias assessment, and evidence synthesis.