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 skills/gaasher/agent-loop-skills/power-analysisnpx skills add gaasher/Agent-Loop-Skills --skill power-analysisgit clone --depth 1 https://github.com/gaasher/Agent-Loop-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/skills/gaasher/agent-loop-skills/power-analysis)<a href="https://agentmods.dev/skills/gaasher/agent-loop-skills/power-analysis"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/power-analysis.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 | $0.00138 | $0.02123 |
| Opus 5 | $0.00069 | $0.01061 |
| Sonnet 5 | $0.00028 | $0.00425 |
| Haiku 4.5 | $0.00014 | $0.00212 |
Grade A, and why
power-analysis 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Power Analysis Loop
A power-analysis-and-preregister loop for a two-arm comparison. The artifact is the study's statistical plan; the feedback signal is two parts — statistical power (estimated by Monte-Carlo simulation of the planned test) and a count of validity flaws. Each iteration simulates power, solves for the sample size that reaches the target, audits the design for flaws, and revises — until power clears the target and the flaw list is empty. The deliverable is a sample-size justification plus a preregistration that pins the hypothesis, primary outcome, analysis, sample size, and stopping rule before any data is seen.
Scope & limitations
This loop does exactly three things, in a loop: (1) computes power and required sample size for a
two-group comparison by simulation, (2) runs a fixed validity checklist over the design,
and (3) writes a preregistration. The vendored power model (tools/power_sim.py) covers
two-sample mean (continuous outcome) and two-proportion (binary outcome) tests only.
It is not a general experiment designer. It does not handle factorial, repeated-measures, clustered/multilevel, time-series, adaptive, or survival designs; it does not pick your outcome measure or manipulation from domain knowledge; and it does not analyze data you have already collected. For those, the power numbers here do not apply — use a design-appropriate power method. If the study is not a simple two-arm comparison, say so and stop rather than reporting a power that does not match the planned analysis.
When to use
Use this to size and preregister one two-arm comparison whose primary outcome is a continuous mean or a binary rate. Default to powering for the minimal effect of interest the user states; if they are unsure of that effect, help them set it from a baseline and a smallest-meaningful difference rather than an optimistic guess — a design "powered" for an effect bigger than reality is a fiction. If the study is not a two-arm comparison, stop and point to a design-appropriate method.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 134 lines · 138 tokens per session scan A a88e05b5d6fb
power-analysis is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (163 stars, last pushed 2mo ago), licensed MIT. It adds 138 tokens to every session and 2,123 once invoked, about $0.0007 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 skills, from other repositories
optimize
Drive structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current best/frontier, continue an evo search, or compare candidate changes in an evo workspace. The…
arbor-agent-orchestrator
Top-level controller for recreating the open-source AutoResearch workflow as a suite of skills. Use when the user asks to run, emulate, extract, validate, or refine Arbor/AutoResearch behavior, especially when a coordinator must load phase skills for setup, ideation, executors, merge evaluation, novelty search…
nomogram-construction
Use when constructing a prognosis nomogram from survival-related clinical predictors, exporting the nomogram bundle and C-index table, and optionally rendering the final nomogram PDF. NOT for: univariate/multivariable Cox feature screening, calibration curves, ROC analysis, decision-curve analysis, or non-survival…
discussion-composer
Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion.…
ssgsea-immune-infiltration-analysis
Use when estimating immune infiltration from bulk RNA-seq expression matrices with ssGSEA/GSVA, comparing case versus control groups, and generating downstream immune-score visualizations. NOT for single-cell RNA-seq, absolute cell proportion estimation, or clinical decision making.
figure-legend-writer
Writes complete, publication-grade figure legends that can stand on their own. Use when writing or revising figure legends for any scientific figure — bar charts, line graphs, scatter plots, box plots, heatmaps, survival curves, flow cytometry plots, western blots, microscopy images, or schematic diagrams. Also…