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 --skill distribution-profilergit clone --depth 1 https://github.com/ai-analyst-lab/ai-analystWrote 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/distribution-profiler)<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/distribution-profiler"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/distribution-profiler/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ai-analyst-lab/ai-analyst/distribution-profiler"><img src="https://agentmods.dev/badge/skills/ai-analyst-lab/ai-analyst/distribution-profiler.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00087 | $0.02297 |
| Opus 5 | $0.00044 | $0.01149 |
| Sonnet 5 | $0.00017 | $0.00459 |
| Haiku 4.5 | $0.00009 | $0.00230 |
Grade A, and why
distribution-profiler 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.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Distribution Profiler
Purpose
Take any numeric data column and produce a complete analytical playbook: identify the distribution, compute the right summary statistics, recommend the correct statistical tests, flag common traps, and give specific A/B testing guidance.
This skill exists because the #1 mistake in product analytics is assuming data is normal when it's not — leading to wrong tests, false positives, and misleading dashboards. The profiler catches this automatically.
When to Use
- Before any analysis involving a numeric metric
- When the user asks "what distribution is this?" or "what test should I use?"
- When checking assumptions for an A/B test
- When a user says "profile" or "understand" a metric
- Proactively when you notice an analysis is about to use a t-test or OLS on data that hasn't been checked
Invocation
/distribution-profiler — profile a data column's distribution
Instructions
Step 0: Identify the Target
Figure out what column/metric the user wants profiled. This could be:
- A specific column name (e.g., "total_amount from orders")
- A derived metric (e.g., "revenue per user", "sessions per user per month")
- A SQL query result
If unclear, ask. If the user hasn't specified, look at what they're analyzing and suggest the most relevant metric to profile.
Step 1: Extract the Data
Write and execute a Python script to extract the target column from the active
dataset. Use helpers/data/data_helpers.py to resolve the data source:
from helpers.data.data_helpers import detect_active_source, check_connection
source = detect_active_source()
Query through ConnectionManager (helpers/data/connection_manager.py) whatever
the source; it resolves local vs. remote and auto-logs the query for provenance.
For per-user metrics (revenue per user, sessions per user), aggregate first — the unit of analysis matters. Profile the metric at the level it will be used in the analysis (per-user, per-session, per-day, etc.).
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.
- 2d ago First seen · 255 lines · 87 tokens per session scan A a0bc37ab347f
distribution-profiler is a skill published in the GitHub repository ai-analyst-lab/ai-analyst (298 stars, last pushed 3d ago), licensed MIT. It adds 87 tokens to every session and 2,297 once invoked, about $0.0004 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 2026-09-12.
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