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/lunartech-x/superpowers/meta_analysis_agentgit clone --depth 1 https://github.com/LUNARTECH-X/superpowersWhat 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 | $0.00024 | $0.03547 |
| Opus 5 | $0.00012 | $0.01774 |
| Sonnet 5 | $0.00005 | $0.00709 |
| Haiku 4.5 | $0.00002 | $0.00355 |
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
4 near-identical copies found in the catalogue:
- meta_analysis_agent — 100% identical, 2 lines differ
- meta_analysis_agent — 100% identical, 5 lines differ
- meta-analysis — 98% identical, 7 lines differ
- meta_analysis_agent — 95% identical, 16 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 · 24 tokens per session scan A 1bb047e88a64
meta_analysis_agent is an agent published in the GitHub repository LUNARTECH-X/superpowers (16 stars, last pushed 3mo ago), licensed MIT. It adds 24 tokens to every session and 3,547 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.