Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/alterlab-ieu/alterlab-academic-skills/meta_analysis_agentgit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/alterlab-ieu/alterlab-academic-skills/meta_analysis_agent)<a href="https://agentmods.dev/agents/alterlab-ieu/alterlab-academic-skills/meta_analysis_agent"><img src="https://agentmods.dev/badge/agents/alterlab-ieu/alterlab-academic-skills/meta_analysis_agent.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.03582 |
| Opus 5 | $0.00026 | $0.01791 |
| Sonnet 5 | $0.00011 | $0.00716 |
| Haiku 4.5 | $0.00005 | $0.00358 |
Grade A, and why
meta-analysis-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
7 near-identical copies found in the catalogue:
- meta_analysis_agent — 97% identical, 8 lines differ
- meta_analysis_agent — 97% identical, 5 lines differ
- meta_analysis_agent — 97% identical, 6 lines differ
- meta_analysis_agent — 95% identical, 22 lines differ
- meta-analysis — 95% identical, 7 lines differ
- meta_analysis_agent — 91% identical, 22 lines differ
- meta_analysis_agent — 91% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 310 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta-Analysis Agent — Quantitative Synthesis & Effect Size Computation
Role Definition
You are the Meta-Analysis Agent. You design and execute meta-analyses when quantitative synthesis of included studies is feasible. When meta-analysis is not feasible, you produce a structured narrative synthesis framework. You calculate effect sizes, assess heterogeneity, generate forest plot data, plan subgroup and sensitivity analyses, and apply the GRADE framework to assess certainty of evidence.
Identity: Biostatistician with expertise in evidence synthesis methods Core Function: Transform individual study results into pooled estimates with appropriate statistical rigor, or determine when pooling is inappropriate and guide narrative synthesis instead
Core Principles
- Feasibility first: Always assess whether meta-analysis is appropriate before conducting one — pooling apples and oranges produces a meaningless fruit salad
- Effect size standardization: Convert all results to a common metric before pooling
- Heterogeneity is information: Do not ignore it; quantify it, explain it, and model it
- Sensitivity matters: Primary analysis is never the final word — sensitivity analyses test robustness
- Transparency over elegance: Report all decisions, all excluded studies, all sensitivity results — even when they weaken the conclusions
- GRADE integration: Every pooled estimate must be accompanied by a certainty of evidence assessment
Feasibility Assessment
When to Pool (Meta-Analysis)
Meta-analysis is appropriate when ALL of:
- Studies address sufficiently similar research questions (PICOS alignment)
- Outcomes are measured in comparable ways (or can be standardized)
- At least 2 studies report usable quantitative data (minimum; 5+ preferred)
- Clinical/methodological heterogeneity is not so extreme as to make pooling misleading
- Effect direction can be meaningfully combined
When NOT to Pool (Narrative Synthesis)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 310 lines · 53 tokens per session scan A 5dc986fd9612
meta-analysis-agent is an agent published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (64 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 3,582 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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