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 matteotitta/genesys-skills --skill apollo-findgit clone --depth 1 https://github.com/matteotitta/genesys-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/matteotitta/genesys-skills/apollo-find)<a href="https://agentmods.dev/skills/matteotitta/genesys-skills/apollo-find"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/apollo-find/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/matteotitta/genesys-skills/apollo-find"><img src="https://agentmods.dev/badge/skills/matteotitta/genesys-skills/apollo-find.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 | $0.00031 | $0.01831 |
| Opus 5 | $0.00015 | $0.00915 |
| Sonnet 5 | $0.00006 | $0.00366 |
| Haiku 4.5 | $0.00003 | $0.00183 |
Grade A, and why
apollo-find-companies 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 9d 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/apollo-find-companies -- Target account discovery via Apollo
Search 70M+ companies by industry, size, funding, tech stack, revenue, and hiring activity. Always free — no Apollo credits consumed.
Imported via: /steal analysis of workflows.io Apollo x Claude Playbook (2026-04-08)
When to use
- Building a target account list for a new campaign
- Finding companies in a specific industry or vertical
- Identifying companies using a competitor's tech stack (displacement plays)
- Finding recently funded companies (buying signal)
- Researching companies actively hiring for specific roles (intent signal)
- Expanding into a new market or vertical
- Building TAM lists from ICP criteria
When NOT to use
- Researching a specific known company ->
/company-context - Finding people at companies ->
/clay-search(with Apollo fallback) - Enriching a company for full profile ->
/deepline-enrichor Apollo MCP directly - Building full prospect lists with people ->
/build-tam
Credit usage
FREE. Company search does not consume Apollo credits. Search freely.
Company enrichment is separate and costs credits. Use /deepline-enrich for that.
Framework
Step 1: Gather search criteria
Ask the user for their search parameters. At minimum, get one of:
| Parameter | Maps to | Example |
|---|---|---|
| Company name | q_organization_name |
Apollo |
| Domain(s) | q_organization_domains_list |
['apollo.io', 'notion.so'] |
| Industry keywords | q_organization_keyword_tags |
['SaaS', 'fintech', 'AI'] |
| HQ location | organization_locations |
['San Francisco, CA', 'United States'] |
| Exclude locations | organization_not_locations |
['China', 'Russia'] |
| Employee count | organization_num_employees_ranges |
['50,200', '201,500'] |
| Revenue range | revenue_range |
{ min: 1000000, max: 50000000 } |
| Tech stack | currently_using_any_of_technology_uids |
['salesforce', 'hubspot'] |
| Total funding | total_funding_range |
{ min: 5000000, max: 50000000 } |
| Latest funding amount | latest_funding_amount_range |
{ min: 1000000, max: 10000000 } |
| Latest funding date | latest_funding_date_range |
{ min: '2025-01-01', max: '2026-04-08' } |
| Hiring for roles | q_organization_job_titles |
['SDR', 'Account Executive'] |
| Hiring in locations | organization_job_locations |
['London', 'remote'] |
| Active job postings | organization_num_jobs_range |
{ min: 5, max: 100 } |
| Number of results | per_page |
25 (default) |
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.
- 9d ago First seen · 195 lines · 131 tokens per session scan A 1f53613ef346
apollo-find-companies is a skill published in the GitHub repository matteotitta/genesys-skills (36 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 1,831 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.
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