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 skills add ai-analyst-lab/ai-analyst-plugin --skill srm-checkgit clone --depth 1 https://github.com/ai-analyst-lab/ai-analyst-pluginWrote 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/ai-analyst-lab/ai-analyst-plugin/srm-check)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst-plugin/srm-check"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst-plugin/srm-check.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 174 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00091 | $0.02163 |
| Opus 5 | $0.00046 | $0.01081 |
| Sonnet 5 | $0.00018 | $0.00433 |
| Haiku 4.5 | $0.00009 | $0.00216 |
Grade A, and why
srm-check 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 8d 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 — 178 lines — stays where its author put it; the contents beside it link to each section on GitHub.
If the skill install path cannot be resolved (some sandboxed environments): read the script file(s) from this skill, write a copy into a scripts/ folder inside the working folder, and run from there. The scripts are self-contained.
Skill: SRM Check (Sample Ratio Mismatch)
Purpose
Automatically detect Sample Ratio Mismatch in experiment data before any analysis proceeds. SRM is a randomization integrity check — if the treatment/control split deviates significantly from the expected ratio, the experiment is compromised and results cannot be trusted. This skill acts as a safety gate that blocks analysis when randomization is broken.
When to Use
Apply this skill when:
- Loading any experiment or A/B test dataset — auto-fire on detection of treatment/control columns (e.g.,
variant,group,treatment,arm,experiment_group) - Before any treatment effect calculation — SRM must pass before comparing outcomes
- When the Experiment Analyzer agent starts — first step of any experiment analysis workflow
This skill auto-fires on experiment data detection. Do NOT wait to be asked.
Instructions
What Is SRM?
Sample Ratio Mismatch (SRM) occurs when the observed ratio of users in treatment vs. control deviates significantly from the expected ratio. For a 50/50 experiment with 10,000 users, you expect ~5,000 in each group. If you see 5,500 vs. 4,500, something is wrong with randomization.
SRM CHECK
━━━━━━━━━━
Expected ratio: 50/50 (or whatever was designed)
Observed ratio: [actual counts]
Test: Chi-squared goodness-of-fit
Decision: PASS (proceed) or BLOCK (halt analysis)
Why SRM matters: If randomization is broken, treatment and control groups are NOT comparable. Any observed difference in outcomes could be caused by the broken randomization, not the treatment. SRM is the single most important validity check in experimentation.
Detection Logic
Step 1: Identify the experiment column
Scan the dataset for columns that indicate experiment assignment. Look for:
- Column names:
variant,group,treatment,control,arm,experiment_group,test_group,bucket,condition - Column values: binary (0/1, control/treatment, A/B), or small number of distinct values (< 10)
- User language: phrases like "A/B test", "experiment", "treatment vs control", "randomization"
- Metadata: check for experiment config files in
.knowledge/experiments/
What ships with it
1 file 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.
- 8d ago First seen · 178 lines · 91 tokens per session scan A 8bb1e73eec23
srm-check is a skill published in the GitHub repository ai-analyst-lab/ai-analyst-plugin (32 stars, last pushed 11d ago), licensed MIT. It adds 91 tokens to every session and 2,163 once invoked, about $0.0005 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…