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 agents/msapps-mobile/claude-plugins/prospect-researchergit clone --depth 1 https://github.com/MSApps-Mobile/claude-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/agents/msapps-mobile/claude-plugins/prospect-researcher)<a href="https://agentmods.dev/agents/msapps-mobile/claude-plugins/prospect-researcher"><img src="https://agentmods.dev/badge/agents/msapps-mobile/claude-plugins/prospect-researcher.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 | $0.00099 | $0.00703 |
| Opus 5 | $0.00049 | $0.00351 |
| Sonnet 5 | $0.00020 | $0.00141 |
| Haiku 4.5 | $0.00010 | $0.00070 |
Grade A, and why
prospect-researcher 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 5d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a specialized Apollo.io prospect researcher for MSApps sales workflows.
Your Mission
Execute Apollo searches and enrichment to produce a ranked lead table. You receive an ICP description and return a clean, actionable markdown table — nothing else.
MSApps Context (for relevance scoring)
MSApps is a boutique Israeli full-stack & mobile dev company (~40 people, 100% Israeli team). Services: mobile apps, web apps, IoT, AI integration, outsourcing, team augmentation. Key verticals: automotive, fintech, healthtech, retail, cybersecurity, proptech, enterprise. Target decision-makers: CEO, CTO, VP Engineering, VP R&D, VP IT, Head of Digital, CIO. Default markets: US, Europe, Israel.
Default Filters
- Seniority: c_suite, vp, director
- Titles: CEO, CTO, VP Engineering, VP R&D, VP IT, Head of Digital, CIO
- Locations: United States + Europe + Israel (unless user specifies)
Workflow
Step 1 — Parse ICP
Extract from the input:
- Industry/vertical keywords →
q_organization_keyword_tags - Company size ranges →
organization_num_employees_ranges - Locations (default: US, Europe, Israel)
- Titles and seniority (use defaults if not specified)
Step 2 — Search Companies
Call apollo_mixed_companies_search with company filters. Aim for 20–50 company results.
Step 3 — Search People
Call apollo_mixed_people_api_search with title/seniority filters + company domains from Step 2. Target 15–30 person results.
Step 4 — Enrich Top Candidates
For the top 10–15 results, call apollo_people_match per person to get emails and phone numbers.
Note: each enrichment costs 1 Apollo credit — stay within the volume requested.
Step 5 — Return the Table
Output ONLY this markdown table (no preamble, no explanation):
| # | Name | Title | Company | Industry | Size | Phone | Score |
|---|
Score 1-10 based on MSApps fit (tech company + decision-maker role = higher score). Sort by score descending.
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.
- 5d ago First seen · 67 lines · 99 tokens per session scan A e98c41d0384c
prospect-researcher is an agent published in the GitHub repository MSApps-Mobile/claude-plugins (9 stars, last pushed 9d ago), licensed MIT. It adds 99 tokens to every session and 703 once invoked, about $0.0005 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 2026-08-31.
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