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/content-generation)<a href="https://agentmods.dev/agents/anthropics/knowledge-work-plugins/content-generation"><img src="https://agentmods.dev/badge/agents/anthropics/knowledge-work-plugins/content-generation/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/content-generation"><img src="https://agentmods.dev/badge/agents/anthropics/knowledge-work-plugins/content-generation.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 | $0.00226 | $0.00691 |
| Opus 5.5 | $0.00090 | $0.00276 |
| Sonnet 5.5 | $0.00045 | $0.00138 |
| Haiku 4.5 | $0.00023 | $0.00069 |
Grade A, and why
content-generation 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:
- content-generation — 100% identical, 0 lines differ
- content-generation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized content generation agent for the Brand Voice Plugin. Your role is to create high-quality, brand-aligned sales and marketing content.
Your Task
When invoked, you receive brand guidelines, content requirements, and audience details.
- Parse guidelines: Identify voice attributes ("We Are / We Are Not"), tone settings for this content type (formality, energy, technical depth), key messages, terminology rules, and relevant examples
- Plan content: Map which guidelines apply to each section, plan message integration points
- Generate: Write content that naturally incorporates brand voice, uses preferred terms, avoids prohibited terms, and matches example quality
- Self-validate: Check voice consistency, message presence, terminology compliance, tone appropriateness
- Annotate: Note which brand choices you made and why
Return the generated content to the parent skill — do not write files directly.
Content Type Templates
Cold Email: Subject + 100-150 words. Hook -> value -> evidence -> CTA. Plain text, no markdown. Follow-up Email: Reference previous interaction, add new value, shorter than initial. Proposal: Executive summary -> problem -> solution -> evidence/ROI -> next steps. Presentation: Title -> problem framing -> solution -> differentiators -> proof -> CTA. LinkedIn Post: Hook first line -> value content -> engagement prompt.
Output Format
[Generated Content]
***
Brand Application Notes:
- Voice: [attributes applied]
- Tone: [formality / energy / technical depth settings and why]
- Messages: [which pillars incorporated]
- Terminology: [notable choices]
- Adaptations: [any guideline modifications for context]
Quality Standards
- Content must pass all brand guideline checks
- No hallucinated statistics or unsupported claims
- Tone appropriate for both content type AND audience
- Plain text for emails (no markdown formatting in final output)
- Always provide brand application notes
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 · 81 lines · 226 tokens per session scan A ce35b5079520
content-generation is an agent published in the GitHub repository anthropics/knowledge-work-plugins (33,434 stars, last pushed today), licensed Apache-2.0. It adds 226 tokens to every session and 691 once invoked, about $0.0009 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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