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 leadbay/mcp --skill leadbay_followup_check_ingit clone --depth 1 https://github.com/leadbay/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/leadbay/mcp/leadbay_followup_check_in)<a href="https://agentmods.dev/skills/leadbay/mcp/leadbay_followup_check_in"><img src="https://agentmods.dev/badge/skills/leadbay/mcp/leadbay_followup_check_in/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/leadbay/mcp/leadbay_followup_check_in"><img src="https://agentmods.dev/badge/skills/leadbay/mcp/leadbay_followup_check_in.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 | $0.00098 | $0.05942 |
| Opus 5 | $0.00049 | $0.02971 |
| Sonnet 5 | $0.00020 | $0.01188 |
| Haiku 4.5 | $0.00010 | $0.00594 |
Grade A, and why
leadbay_followup_check_in 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 yesterday.
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 — 270 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WHAT LEADBAY SHOULD REMEMBER
You keep your own memory of how this user likes to work — tone, naming, formatting, what they ask you to skip. Leadbay does not store that and does not need to.
What Leadbay does need is anything that changes who it should find. When the user states targeting criteria in conversation ("I target fleets over 100 vehicles", "carriers are a bad fit unless they do last-mile delivery", "climate engineering is also my market"), call leadbay_refine_prompt so it changes what Leadbay surfaces for the whole org and on every future refresh — not just this conversation. When they say a specific lead is wrong for them, record the dislike rather than noting it.
Run the Leadbay follow-up check-in for me. Treat this prompt the same way for any equivalent ask: "leads I should follow up with", "already known leads", "what's overdue", "before my trip to [city]", "leads I haven't contacted", "who should I re-engage today".
Resilience rules for Leadbay long-running tools
These rules apply to every Leadbay workflow that calls leadbay_pull_leads, leadbay_bulk_qualify_leads, leadbay_research_lead_by_id, leadbay_import_and_qualify, or leadbay_enrich_titles. Treat timeouts and stream-closed errors as transient, not as signals to replan.
Rule 1 — Pin the lens
After your first leadbay_pull_leads call, capture response.lens.id into your working memory and pass it explicitly as the lensId argument to every subsequent call in this session — including any re-pulls, bulk qualifies, or research calls that accept it. (Field-name caveat: the response nests it as lens.id; the parameter on subsequent calls is lensId.) The active lens can shift between calls (5-minute client cache + backend last_requested_lens can change if the user touches the web UI). A lens shift mid-workflow throws away your top-10 work.
Rule 2 — Prefer async for bulk operations
leadbay_bulk_qualify_leads and leadbay_import_and_qualify accept wait_for_completion:false and return immediately. They hand back different ids: bulk_qualify_leads returns {status:'running', notification_id, lead_ids, lens_id} — poll leadbay_qualify_status with those. import_and_qualify returns {status:'running', import_ids} and no notification_id at all — poll leadbay_import_status({importIds, dry_run}) with those. Poll every ~10s until the job completes. Use the async pattern by default — the blocking default can exceed the MCP client's per-call timeout on large batches and produce a misleading "Request timed out" even though the server is still working.
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.
- yesterday Changed · +30 lines f46ce61a253b
- 6d ago Changed · +2 lines 5971f0e89b61
- 9d ago First seen · 238 lines · 98 tokens per session scan A 5e025546d826
leadbay_followup_check_in is a skill published in the GitHub repository leadbay/mcp (0 stars, last pushed today), licensed MIT. It adds 98 tokens to every session and 5,942 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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