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/analyzeWrote 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/analyze)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/analyze"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/analyze/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/analyze"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/analyze.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 | $0.00045 | $0.00955 |
| Opus 5.5 | $0.00018 | $0.00382 |
| Sonnet 5.5 | $0.00009 | $0.00191 |
| Haiku 4.5 | $0.00005 | $0.00096 |
Grade A, and why
analyze 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
4 near-identical copies found in the catalogue:
- analyze — 100% identical, 0 lines differ
- analyze — 98% identical, 9 lines differ
- analyze — 98% identical, 7 lines differ
- analyze-th — 94% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/analyze - Answer Data Questions
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Answer a data question, from a quick lookup to a full analysis to a formal report.
Usage
/analyze <natural language question>
Workflow
1. Understand the Question
Parse the user's question and determine:
- Complexity level:
- Quick answer: Single metric, simple filter, factual lookup (e.g., "How many users signed up last week?")
- Full analysis: Multi-dimensional exploration, trend analysis, comparison (e.g., "What's driving the drop in conversion rate?")
- Formal report: Comprehensive investigation with methodology, caveats, and recommendations (e.g., "Prepare a quarterly business review of our subscription metrics")
- Data requirements: Which tables, metrics, dimensions, and time ranges are needed
- Output format: Number, table, chart, narrative, or combination
2. Gather Data
If a data warehouse MCP server is connected:
- Explore the schema to find relevant tables and columns
- Write SQL query(ies) to extract the needed data
- Execute the query and retrieve results
- If the query fails, debug and retry (check column names, table references, syntax for the specific dialect)
- If results look unexpected, run sanity checks before proceeding
If no data warehouse is connected:
- Ask the user to provide data in one of these ways:
- Paste query results directly
- Upload a CSV or Excel file
- Describe the schema so you can write queries for them to run
- If writing queries for manual execution, use the
sql-queriesskill for dialect-specific best practices - Once data is provided, proceed with analysis
3. Analyze
- Calculate relevant metrics, aggregations, and comparisons
- Identify patterns, trends, outliers, and anomalies
- Compare across dimensions (time periods, segments, categories)
- For complex analyses, break the problem into sub-questions and address each
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 · 120 lines · 45 tokens per session scan A 4ee816363d94
analyze is a skill published in the GitHub repository anthropics/knowledge-work-plugins (29,887 stars, last pushed yesterday), licensed Apache-2.0. It adds 45 tokens to every session and 955 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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