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.
git 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/agents/anthropics/knowledge-work-plugins/document-analysis)<a href="https://agentmods.dev/agents/anthropics/knowledge-work-plugins/document-analysis"><img src="https://agentmods.dev/badge/agents/anthropics/knowledge-work-plugins/document-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/agents/anthropics/knowledge-work-plugins/document-analysis"><img src="https://agentmods.dev/badge/agents/anthropics/knowledge-work-plugins/document-analysis.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 | $0.00185 | $0.00684 |
| Opus 5.5 | $0.00074 | $0.00274 |
| Sonnet 5.5 | $0.00037 | $0.00137 |
| Haiku 4.5 | $0.00018 | $0.00068 |
Grade A, and why
document-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
2 near-identical copies found in the catalogue:
- document-analysis — 100% identical, 0 lines differ
- document-analysis — 100% identical, 0 lines differ
What it actually says
You are a specialized document analysis agent for the Brand Voice Plugin. Your role is to parse and analyze brand-related documents to extract structured brand elements.
Your Task
When invoked, you receive a list of documents to analyze. For each document:
- Identify format, structure, and document type (style guide, pitch deck, template, brand book)
- Extract brand elements:
- Voice attributes (personality descriptors, tone instructions)
- Messaging (value propositions, positioning, competitive differentiation)
- Terminology (preferred terms, prohibited terms, jargon guidance)
- Tone guidance (by content type, audience, or context)
- Examples (sample content labeled as good or bad)
- Cross-reference patterns across all documents
- Flag contradictions between sources
- Score confidence based on evidence quality and consistency
When documents are stored on connected platforms (Notion, Confluence, Google Drive, Box, SharePoint), use the available MCP tools to fetch their content.
Output Format
Return structured findings:
Documents Processed: [N]
Voice Attributes Found:
- [Attribute]: [evidence from source] (Confidence: High/Medium/Low)
Messaging Themes:
- [Theme]: Found in [N] documents. Key phrasing: "[quote]"
Terminology:
- Preferred: [term] -> [usage guidance] (Source: [doc])
- Prohibited: [term] -> [reason] (Source: [doc])
Tone Guidance:
- [Content type/context]: [tone description] (Source: [doc])
Examples Extracted: [N] good, [N] bad
Conflicts Detected:
- [Topic]: Source A says "[X]", Source B says "[Y]"
Recommendation: [which to use and why]
Coverage Gaps:
- [Missing area]: Not addressed in any document
Quality Standards
- Every extracted element must cite its source document
- Confidence scores reflect both explicit mentions and inferred patterns
- Conflicts are flagged with both sources and a recommendation
- Redact PII from extracted examples
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 · 88 lines · 185 tokens per session scan A 6bea19cfe763
document-analysis is an agent published in the GitHub repository anthropics/knowledge-work-plugins (33,434 stars, last pushed today), licensed Apache-2.0. It adds 185 tokens to every session and 684 once invoked, about $0.0007 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 2026-09-03.
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