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/build-dashboardWrote 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/build-dashboard)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/build-dashboard"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/build-dashboard/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/build-dashboard"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/build-dashboard.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 | $0.00056 | $0.06097 |
| Opus 5.5 | $0.00022 | $0.02439 |
| Sonnet 5.5 | $0.00011 | $0.01219 |
| Haiku 4.5 | $0.00006 | $0.00610 |
Grade A, and why
build-dashboard 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:
- build-dashboard — 100% identical, 0 lines differ
- build-dashboard — 95% identical, 7 lines differ
- build-dashboard — 95% identical, 10 lines differ
- build-dashboard-th — 81% identical, 78 lines differ
How it starts
The opening of the file, as written. The whole thing — 925 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/build-dashboard - Build Interactive Dashboards
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Build a self-contained interactive HTML dashboard with charts, filters, tables, and professional styling. Opens directly in a browser -- no server or dependencies required.
Usage
/build-dashboard <description of dashboard> [data source]
Workflow
1. Understand the Dashboard Requirements
Determine:
- Purpose: Executive overview, operational monitoring, deep-dive analysis, team reporting
- Audience: Who will use this dashboard?
- Key metrics: What numbers matter most?
- Dimensions: What should users be able to filter or slice by?
- Data source: Live query, pasted data, CSV file, or sample data
2. Gather the Data
If data warehouse is connected:
- Query the necessary data
- Embed the results as JSON within the HTML file
If data is pasted or uploaded:
- Parse and clean the data
- Embed as JSON in the dashboard
If working from a description without data:
- Create a realistic sample dataset matching the described schema
- Note in the dashboard that it uses sample data
- Provide instructions for swapping in real data
3. Design the Dashboard Layout
Follow a standard dashboard layout pattern:
┌──────────────────────────────────────────────────┐
│ Dashboard Title [Filters ▼] │
├────────────┬────────────┬────────────┬───────────┤
│ KPI Card │ KPI Card │ KPI Card │ KPI Card │
├────────────┴────────────┼────────────┴───────────┤
│ │ │
│ Primary Chart │ Secondary Chart │
│ (largest area) │ │
│ │ │
├─────────────────────────┴────────────────────────┤
│ │
│ Detail Table (sortable, scrollable) │
│ │
└──────────────────────────────────────────────────┘
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 · 925 lines · 56 tokens per session scan A 6f23faa0b266
build-dashboard is a skill published in the GitHub repository anthropics/knowledge-work-plugins (29,798 stars, last pushed today), licensed Apache-2.0. It adds 56 tokens to every session and 6,097 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.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…