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 sun2443/designer-skills --skill prospectgit clone --depth 1 https://github.com/sun2443/designer-skillsWrote 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/sun2443/designer-skills/prospect)<a href="https://agentmods.dev/skills/sun2443/designer-skills/prospect"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/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/sun2443/designer-skills/prospect"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/prospect.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 | $0.00033 | $0.00897 |
| Opus 5.5 | $0.00013 | $0.00359 |
| Sonnet 5.5 | $0.00007 | $0.00179 |
| Haiku 4.5 | $0.00003 | $0.00090 |
Grade A, and why
prospect scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to prospect — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prospect
Go from an ICP description to a ranked, enriched lead list in one shot. The user describes their ideal customer via "$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.
- 13d ago First seen · 91 lines · 33 tokens per session scan A 6ec853520619
prospect is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), 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. It is 100% identical to prospect, differing in 0 lines, and is treated as a copy.
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