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 toflow-ai/toflow-mcp --skill toflow-prospect-and-listgit clone --depth 1 https://github.com/toflow-ai/toflow-mcpWrote 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/toflow-ai/toflow-mcp/toflow-prospect-and-list)<a href="https://agentmods.dev/skills/toflow-ai/toflow-mcp/toflow-prospect-and-list"><img src="https://agentmods.dev/badge/skills/toflow-ai/toflow-mcp/toflow-prospect-and-list/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/toflow-ai/toflow-mcp/toflow-prospect-and-list"><img src="https://agentmods.dev/badge/skills/toflow-ai/toflow-mcp/toflow-prospect-and-list.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 | $0.00072 | $0.01221 |
| Opus 5.5 | $0.00029 | $0.00488 |
| Sonnet 5.5 | $0.00014 | $0.00244 |
| Haiku 4.5 | $0.00007 | $0.00122 |
Grade A, and why
toflow-prospect-and-list 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 4d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
toflow — Prospect and Build Lists
Find the right people before reaching out, then stage them in a list so they're ready for enrichment and sequencing.
Part 1 — Finding prospects on LinkedIn
- Call
list_message_accounts(provider_type='linkedin')to get the connected account, andget_linkedin_search_guidefor the full filter reference. For any filter needing an ID (location, industry, company, school, etc.), calllinkedin_search_parametersfirst to resolve the correct IDs rather than guessing them. - Choose the API mode with the user:
classic(keywords, location, industry, company, network distance, company size, etc.) orsales_navigator(richer — seniority, function, tenure, revenue, technologies, recent activity, saved/recent searches, include/exclude on most filters) — Sales Navigator requires that tier of LinkedIn account. - Call
search_linkedinwith the confirmed filters, and pass the returnedcursorback on the next call to paginate. Never mention internal fields likeapi,category,message_account_id, orcursorto the user. Respect the account's own pacing — there's a jittered gap between pages and a capped number of pages per 15 minutes and per 24 hours; if rate-limited, stop and tell the user when it'll reset rather than retrying immediately. - When importing a result into the CRM, map fields explicitly:
name→first_name/last_name,headline→job_title,public_profile_url→linkedin_url. Skip any result with a nullpublic_identifier. - For warm leads, use
list_linkedin_connections(optionally withcheck_linkedin_connectionto confirm connection status with a specific person) instead of cold search. - For intent-based prospecting from a specific post:
get_linkedin_post/get_linkedin_post_comments/get_linkedin_post_reactions/get_linkedin_person_postssurface people who engaged with relevant content — these are warmer than cold search since they've already shown topical interest.react_to_linkedin_post/comment_on_linkedin_postlet you engage first if the user wants to warm up a prospect before reaching out directly. - Show the user a sample of results before staging a large batch into a list.
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.
- 4d ago First seen · 46 lines · 72 tokens per session scan A 7f87c6196d2e
toflow-prospect-and-list is a skill published in the GitHub repository toflow-ai/toflow-mcp (6 stars, last pushed 5d ago), licensed MIT. It adds 72 tokens to every session and 1,221 once invoked, about $0.0003 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 2026-10-10.
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