What is sampling-estimation?

A data-analysis guide for estimating values about a larger group from a smaller sample. It covers uncertainty ranges, including confidence intervals, which show how precise an estimate is.

Use it to plan samples, calculate means or proportions, examine data quality, assess representativeness, and produce estimates using bootstrap, Bayesian, or standard statistical methods.

It helps reveal whether a sample represents the target group and reduces the risk of presenting a misleading estimate without accounting for missing data or sampling bias.

sampling-estimation — Skill for Claude Code by ChrisGVE

Install

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.

Any agent

npx skills add ChrisGVE/localdata-mcp --skill sampling-estimation

Clone the repo

git clone --depth 1 https://github.com/ChrisGVE/localdata-mcp

Made for: Claude Code.

Or install localdata-mcp, the plugin that ships this one along with the rest of its 18 skills, 11 agents, 1 MCP server.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/sampling-estimation.svg)](https://agentmods.dev/skills/chrisgve/localdata-mcp/sampling-estimation)

Your own site

<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/sampling-estimation"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/sampling-estimation.svg" alt="Measured on agentmods" height="20"></a>

Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 517 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00031	$0.00517
Opus 5	$0.00015	$0.00259
Sonnet 5	$0.00006	$0.00103
Haiku 4.5	$0.00003	$0.00052

Measured 8d ago against content hash d24c87667e7a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

sampling-estimation 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.

skills/statistical/sampling-estimation/SKILL.md · 37 lines

How it starts

The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sampling and Estimation

Design a sampling strategy and compute estimates with proper uncertainty quantification.

Steps

Understand the estimation goal. Identify what parameter needs to be estimated (mean, proportion, difference, ratio) and the target population from the user's question.
Profile the data. Call describe_database and get_data_quality_report with the database name from $ARGUMENTS. Assess available sample size, data completeness, and any stratification variables present in the data.
Assess sample representativeness. Call execute_query to examine the distribution of key demographic or stratification variables. Determine whether the sample is a plausible representation of the target population. Flag potential selection biases.
Compute point estimates. Call execute_query to calculate the sample statistic of interest (mean, proportion, median, etc.) along with summary statistics (n, SD, IQR) needed for confidence interval construction.
Construct confidence intervals. Based on the data characteristics:
- Large sample, normal: use classical parametric intervals
- Small sample or skewed: describe bootstrap approach (resample with replacement, compute statistic on each resample, use percentile method for CI)
- Proportion near 0 or 1: use Wilson or Clopper-Pearson interval rather than Wald
Compute required sample size. If the user needs to plan future data collection, calculate the sample size needed for a target margin of error. Report assumptions about expected variability and confidence level.
Report estimates. Present:
- Point estimate with units
- Confidence interval (95% default, note level)
- Margin of error
- Sample size and effective sample size (accounting for missing data)
- Method used for interval construction
Discuss limitations. Address sampling bias, non-response, measurement error, and any extrapolation concerns. Distinguish between the precision of the estimate (narrow CI) and its accuracy (freedom from bias).

Read the full file on GitHub · 37 lines

Changes

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 · 37 lines · 31 tokens per session scan A d24c87667e7a

Subscribe to this mod's changes

sampling-estimation is a skill published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 31 tokens to every session and 517 once invoked, about $0.0002 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 2026-08-31.

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