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 skills add anthropics/knowledge-work-plugins --skill call-prepgit 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/call-prep)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/call-prep"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/call-prep/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/call-prep"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/call-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00055 | $0.01270 |
| Opus 5.5 | $0.00022 | $0.00508 |
| Sonnet 5.5 | $0.00011 | $0.00254 |
| Haiku 4.5 | $0.00006 | $0.00127 |
Grade A, and why
call-prep 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Call Prep
Produce a complete, scannable call prep brief by combining account research, contact research, and signal synthesis from Common Room.
Prep Process
Step 1: Identify the Account and Attendees
Parse what the user has provided:
- Company name — required; look up the account in Common Room
- Attendee names — optional; if provided, research each one
Calendar lookup: If a ~~calendar connector is available, search for upcoming meetings with the named company to automatically surface attendee names, meeting time, and any meeting notes or agenda. Use this to fill gaps the user didn't provide.
If neither attendees nor a calendar match can be found, ask: "Who will be on the call from [Company]? I can research each attendee to make your prep more useful."
Step 2: Run Account Research
Use the account-research skill process to build a full account snapshot. For call prep, prioritize:
- Recent product signals (what are they doing in the product right now?)
- Open opportunities or renewal timeline
- Any risk signals (declining usage, support tickets, churned seats)
- Key recent events (funding, executive change, new hire)
When reviewing activity history, prioritize Gong and call recording activities — these provide direct context about previous conversations. Do not filter out call recordings by activity origin.
Step 3: Run Contact Research for Each Attendee
For each external attendee, use the contact-research skill process. For call prep, focus on:
- Role and influence in the buying process
- Their personal activity and engagement history
- Any recent signals that suggest their current mood/priorities
- Spark persona classification if available
Step 4: Synthesize Talking Points and Objectives
Based on the combined account and contact research:
- Identify the call objective (e.g., discovery, demo, expansion conversation, renewal, QBR)
- Generate 3–5 tailored talking points grounded in specific signal data
- Anticipate 2–3 likely objections or topics the customer may raise
- Suggest a recommended outcome for the call
What ships with it
1 file 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.
- 1mo ago First seen · 138 lines · 55 tokens per session scan A 652c42776860
call-prep is a skill published in the GitHub repository anthropics/knowledge-work-plugins (33,434 stars, last pushed today), licensed Apache-2.0. It adds 55 tokens to every session and 1,270 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 2026-09-03.
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