Knowledge Work Plugins is an open-source collection of Claude extensions organized around roles such as productivity, sales, and customer support. Each plugin combines role-specific guidance, connectors, commands, and sub-agents so knowledge workers can use Claude with their team’s tools and processes. The catalogue entries are examples of, or workflows from, this plugin collection.
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 anthropics/knowledge-work-plugins --skill statistical-analysisgit clone --depth 1 https://github.com/anthropics/knowledge-work-pluginsWrote 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/anthropics/knowledge-work-plugins/statistical-analysis)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/statistical-analysis"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/statistical-analysis/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/anthropics/knowledge-work-plugins/statistical-analysis"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/statistical-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 137 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 | $0.00042 | $0.02448 |
| Opus 5.5 | $0.00017 | $0.00979 |
| Sonnet 5.5 | $0.00008 | $0.00490 |
| Haiku 4.5 | $0.00004 | $0.00245 |
Grade A, and why
statistical-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 1mo 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
7 near-identical copies found in the catalogue:
- statistical-analysis — 100% identical, 0 lines differ
- statistical-analysis — 100% identical, 0 lines differ
- statistical-analysis — 100% identical, 5 lines differ
- statistical-analysis — 97% identical, 1 lines differ
- statistical-analysis — 97% identical, 1 lines differ
- statistical-analysis — 91% identical, 4 lines differ
- statistical-analysis-th — 89% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Analysis Skill
Descriptive statistics, trend analysis, outlier detection, hypothesis testing, and guidance on when to be cautious about statistical claims.
Descriptive Statistics Methodology
Central Tendency
Choose the right measure of center based on the data:
| Situation | Use | Why |
|---|---|---|
| Symmetric distribution, no outliers | Mean | Most efficient estimator |
| Skewed distribution | Median | Robust to outliers |
| Categorical or ordinal data | Mode | Only option for non-numeric |
| Highly skewed with outliers (e.g., revenue per user) | Median + mean | Report both; the gap shows skew |
Always report mean and median together for business metrics. If they diverge significantly, the data is skewed and the mean alone is misleading.
Spread and Variability
- Standard deviation: How far values typically fall from the mean. Use with normally distributed data.
- Interquartile range (IQR): Distance from p25 to p75. Robust to outliers. Use with skewed data.
- Coefficient of variation (CV): StdDev / Mean. Use to compare variability across metrics with different scales.
- Range: Max minus min. Sensitive to outliers but gives a quick sense of data extent.
Percentiles for Business Context
Report key percentiles to tell a richer story than mean alone:
p1: Bottom 1% (floor / minimum typical value)
p5: Low end of normal range
p25: First quartile
p50: Median (typical user)
p75: Third quartile
p90: Top 10% / power users
p95: High end of normal range
p99: Top 1% / extreme users
Example narrative: "The median session duration is 4.2 minutes, but the top 10% of users spend over 22 minutes per session, pulling the mean up to 7.8 minutes."
Describing Distributions
Characterize every numeric distribution you analyze:
- Shape: Normal, right-skewed, left-skewed, bimodal, uniform, heavy-tailed
- Center: Mean and median (and the gap between them)
- Spread: Standard deviation or IQR
- Outliers: How many and how extreme
- Bounds: Is there a natural floor (zero) or ceiling (100%)?
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.
- 1mo ago First seen · 246 lines · 42 tokens per session scan A 91a15cfc144e
statistical-analysis is a skill published in the GitHub repository anthropics/knowledge-work-plugins (29,887 stars, last pushed yesterday), licensed Apache-2.0. It adds 42 tokens to every session and 2,448 once invoked, about $0.0002 per session on Opus 5.5. 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.
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