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.
npx agentmods add skills/anthropics/knowledge-work-plugins/linuxnpx skills add anthropics/knowledge-work-plugins --skill linuxgit 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/skills/anthropics/knowledge-work-plugins/linux)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/linux"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/linux.svg" alt="Measured on agentmods" 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 | $0.00033 | $0.03783 |
| Opus 5 | $0.00016 | $0.01892 |
| Sonnet 5 | $0.00007 | $0.00757 |
| Haiku 4.5 | $0.00003 | $0.00378 |
Grade A, and why
meeting-sdk/linux 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 yesterday.
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:
- meeting-sdk/linux — 100% identical, 0 lines differ
- meeting-sdk/linux — 91% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 430 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zoom Meeting SDK - Linux Development
Expert guidance for building headless meeting bots with the Zoom Meeting SDK on Linux. This SDK enables server-side meeting participation, raw media capture, transcription, and AI-powered meeting automation.
How to Build a Meeting Bot That Automatically Joins and Records
Use this skill when the requirement is:
- visible bot joins a real Zoom meeting
- the bot records raw media itself
- or the bot triggers a Zoom-managed cloud-recording workflow after join
Skill chain:
- primary:
meeting-sdk/linux - add
zoom-rest-apifor OBF/ZAK lookup, scheduling, or cloud-recording settings - add
zoom-webhookswhen post-meeting cloud recording retrieval is required
Minimal raw-recording flow:
JoinParam join_param;
join_param.userType = SDK_UT_WITHOUT_LOGIN;
auto& params = join_param.param.withoutloginuserJoin;
params.meetingNumber = meeting_number;
params.userName = "Recording Bot";
params.psw = meeting_password.c_str();
params.app_privilege_token = obf_token.c_str();
SDKError join_err = meeting_service->Join(join_param);
if (join_err != SDKERR_SUCCESS) {
throw std::runtime_error("join_failed");
}
// In MEETING_STATUS_INMEETING callback:
auto* record_ctrl = meeting_service->GetMeetingRecordingController();
if (!record_ctrl) {
throw std::runtime_error("recording_controller_unavailable");
}
if (record_ctrl->CanStartRawRecording() != SDKERR_SUCCESS) {
throw std::runtime_error("raw_recording_not_permitted");
}
SDKError record_err = record_ctrl->StartRawRecording();
if (record_err != SDKERR_SUCCESS) {
throw std::runtime_error("start_raw_recording_failed");
}
GetAudioRawdataHelper()->subscribe(new MyAudioDelegate());
Use raw recording when the bot must own PCM/YUV media or feed an AI pipeline directly.
Use cloud recording + webhooks when the requirement is Zoom-managed MP4/M4A/transcript assets after the meeting.
Official Documentation: https://developers.zoom.us/docs/meeting-sdk/linux/
API Reference: https://marketplacefront.zoom.us/sdk/meeting/linux/
Sample Repository (Raw Recording): https://github.com/zoom/meetingsdk-linux-raw-recording-sample
Sample Repository (Headless): https://github.com/zoom/meetingsdk-headless-linux-sample
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 430 lines · 33 tokens per session scan A 5a1f6b87e040
meeting-sdk/linux is a skill published in the GitHub repository anthropics/knowledge-work-plugins (23,849 stars, last pushed yesterday), licensed Apache-2.0. It adds 33 tokens to every session and 3,783 once invoked, about $0.0002 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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…