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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/anthropics/knowledge-work-pluginsnpx agentmods add skills/anthropics/knowledge-work-plugins/validate-dataWrote 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/validate-data)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/validate-data"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/validate-data/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/validate-data"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/validate-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00055 | $0.03250 |
| Opus 5.5 | $0.00022 | $0.01300 |
| Sonnet 5.5 | $0.00011 | $0.00650 |
| Haiku 4.5 | $0.00006 | $0.00325 |
Grade A, and why
validate-data 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
3 near-identical copies found in the catalogue:
- validate-data — 100% identical, 0 lines differ
- validate-data — 98% identical, 10 lines differ
- validate-data-th — 89% identical, 20 lines differ
How it starts
The opening of the file, as written. The whole thing — 384 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/validate-data - Validate Analysis Before Sharing
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Review an analysis for accuracy, methodology, and potential biases before sharing with stakeholders. Generates a confidence assessment and improvement suggestions.
Usage
/validate-data <analysis to review>
The analysis can be:
- A document or report in the conversation
- A file (markdown, notebook, spreadsheet)
- SQL queries and their results
- Charts and their underlying data
- A description of methodology and findings
Workflow
1. Review Methodology and Assumptions
Examine:
- Question framing: Is the analysis answering the right question? Could the question be interpreted differently?
- Data selection: Are the right tables/datasets being used? Is the time range appropriate?
- Population definition: Is the analysis population correctly defined? Are there unintended exclusions?
- Metric definitions: Are metrics defined clearly and consistently? Do they match how stakeholders understand them?
- Baseline and comparison: Is the comparison fair? Are time periods, cohort sizes, and contexts comparable?
2. Run the Pre-Delivery QA Checklist
Work through the checklist below — data quality, calculation, reasonableness, and presentation checks.
3. Check for Common Analytical Pitfalls
Systematically review against the detailed pitfall catalog below (join explosion, survivorship bias, incomplete period comparison, denominator shifting, average of averages, timezone mismatches, selection bias).
4. Verify Calculations and Aggregations
Where possible, spot-check:
- Recalculate a few key numbers independently
- Verify that subtotals sum to totals
- Check that percentages sum to 100% (or close to it) where expected
- Confirm that YoY/MoM comparisons use the correct base periods
- Validate that filters are applied consistently across all metrics
Apply the result sanity-checking techniques below (magnitude checks, cross-validation, red-flag detection).
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 · 384 lines · 55 tokens per session scan A 1e4248220434
validate-data is a skill published in the GitHub repository anthropics/knowledge-work-plugins (29,887 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 3,250 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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