meta-analysis
Skill1 starsChanged 4 months agoMITView on GitHub
Pool effect sizes across studies only when the studies are genuinely comparable; otherwise plot the individual estimates and state why they cannot be combined. Use whenever an agent wants to produce a single summary number from multiple studies.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- zamushwani/biomedical-ai-skillstheir own version, first seen Aug 26, 2026
- DanaResearchGroup/agent-skillstheir own version, first seen Aug 26, 2026
- xjtulyc/awesome-rosetta-skillstheir own version, first seen Mar 17, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- xjtulyc/awesome-rosetta-skills★ 34same name
- DanaResearchGroup/agent-skills★ 1same name
- zamushwani/biomedical-ai-skills★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository8 other files it gives its agents.
- citation-faithfulnessSkill
- claim-extractionSkill
- conflict-detectionSkill
- evidence-coverageSkill
- prisma-searchSkill
- query-planningSkill
- rag-injection-defenseSkill
- source-reliabilitySkill
The file
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
Before combining, every study in the pool must share:
- Distinctive: line 28
If any of these differ materially, do not pool.
- Distinctive: line 35
Prefer primary studies: Do not pool a review's summary figure together with the primary trials it already summarizes — that double-counts.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Meta-Analysis
A pooled number is powerful and dangerous. Done on comparable studies it sharpens the truth; done on studies that measured different things it manufactures a precise-looking lie. The judgment this skill enforces is not "how do I average these" — it is "should these be averaged at all?"
When to use
Whenever the agent is tempted to report one effect size (a risk ratio, an odds ratio, a mean difference, an HR) drawn from more than one study.
The rule
Only pool comparable primary studies. When they are not comparable, show them individually and say why.
Before combining, every study in the pool must share:
- Population — same clinical group, not "adults" vs "elderly with comorbidity" flattened together.
- Intervention / exposure — same drug, comparable dose and route; same exposure definition.
- Comparator — placebo vs active control are different questions; do not mix them into one pool.
- Outcome and its measure — the same endpoint, defined and measured the same way, on the same scale.
- Design tier — do not pool RCTs with observational studies into a single estimate. Report them in separate strata.
If any of these differ materially, do not pool. Plot each study's own estimate with its confidence interval (a forest-style layout) and let the reader see the spread.
Doing the pool responsibly
- Weight by precision, not by vote. Larger, tighter studies carry more weight; do not average raw numbers as if each study counted once.
- Report heterogeneity. State how much the studies disagree (e.g. an I² and the between-study variance). High heterogeneity is a reason to stop pooling, not a footnote.
- Keep confidence intervals bound to their estimate. A CI that has drifted from the number it belongs to is a parsing bug that becomes a clinical error. Verify each estimate carries its own interval.
- Prefer primary studies. Do not pool a review's summary figure together with the primary trials it already summarizes — that double-counts.
When you cannot pool
Say it plainly and show the evidence anyway:
These 6 studies cannot be pooled: 4 measured 28-day mortality, 2 measured
90-day; comparators differ (placebo vs standard care). Individual estimates:
Study A (RCT, placebo, 28d) RR 0.81 [0.70–0.94]
Study B (RCT, placebo, 28d) RR 0.88 [0.79–0.98]
Study C (obs, standard, 90d) RR 0.72 [0.55–0.95]
...A row of honest individual estimates is more useful than one dishonest summary. The skill is knowing the difference.
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Your trail
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