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 prospectgit 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/prospect)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/prospect"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/prospect/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/prospect"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/prospect.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 | $0.00033 | $0.00897 |
| Opus 5.5 | $0.00013 | $0.00359 |
| Sonnet 5.5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00090 |
Grade A, and why
prospect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prospect
Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$ARGUMENTS".
Examples
/apollo:prospect VP of Engineering at Series B+ SaaS companies in the US, 200-1000 employees/apollo:prospect heads of marketing at e-commerce companies in Europe/apollo:prospect CTOs at fintech startups, 50-500 employees, New York/apollo:prospect procurement managers at manufacturing companies with 1000+ employees/apollo:prospect SDR leaders at companies using Salesforce and Outreach
Step 1 — Parse the ICP
Extract structured filters from the natural language description in "$ARGUMENTS":
Company filters:
- Industry/vertical keywords →
q_organization_keyword_tags - Employee count ranges →
organization_num_employees_ranges - Company locations →
organization_locations - Specific domains →
q_organization_domains_list
Person filters:
- Job titles →
person_titles - Seniority levels →
person_seniorities - Person locations →
person_locations
If the ICP is vague, ask 1-2 clarifying questions before proceeding. At minimum, you need a title/role and an industry or company size.
Step 2 — Search for Companies
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_companies_search with the company filters:
q_organization_keyword_tagsfor industry/verticalorganization_num_employees_rangesfor sizeorganization_locationsfor geography- Set
per_pageto 25
Step 3 — Enrich Top Companies
Use mcp__claude_ai_Apollo_MCP__apollo_organizations_bulk_enrich with the domains from the top 10 results. This reveals revenue, funding, headcount, and firmographic data to help rank companies.
Step 4 — Find Decision Makers
Use mcp__claude_ai_Apollo_MCP__apollo_mixed_people_api_search with:
person_titlesandperson_senioritiesfrom the ICPq_organization_domains_listscoped to the enriched company domainsper_pageset to 25
Step 5 — Enrich Top Leads
Credit warning: Tell the user exactly how many credits will be consumed before proceeding.
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 · 91 lines · 33 tokens per session scan A 6ec853520619
prospect is a skill published in the GitHub repository anthropics/knowledge-work-plugins (33,434 stars, last pushed today), licensed Apache-2.0. It adds 33 tokens to every session and 897 once invoked, about $0.0001 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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