leadbay_build_campaign

leadbay_build_campaign is a skill for Claude Code from leadbay/mcp. It costs 108 tokens per session (5,947 once invoked), scanned A, original, MIT.

An automation skill for building a sales campaign in Leadbay by finding leads, checking fit, and adding reachable contacts.

In plain words
What is it for?
It is for creating campaigns with a target number of qualified leads and producing a call-and-email contact sheet.
Why use it?
It removes the need to pause between lead discovery, qualification, and contact enrichment.

Skill for Claude Code

Written for Claude Code: a Claude Code plugin manifest.

Part of the leadbay plugin — 15 skills shipped together

Install

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.

agentmods
npx agentmods add skills/leadbay/mcp/leadbay_build_campaign
Any agent
npx skills add leadbay/mcp --skill leadbay_build_campaign
Clone the repo
git clone --depth 1 https://github.com/leadbay/mcp

Made for: Claude Code.

Or install leadbay, the plugin that ships this one along with the rest of its 15 skills.

Wrote 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.

agentmods badge for leadbay_build_campaign

README.md
[![agentmods](https://agentmods.dev/badge/skills/leadbay/mcp/leadbay_build_campaign.svg)](https://agentmods.dev/skills/leadbay/mcp/leadbay_build_campaign)
Your own site
<a href="https://agentmods.dev/skills/leadbay/mcp/leadbay_build_campaign"><img src="https://agentmods.dev/badge/skills/leadbay/mcp/leadbay_build_campaign.svg" alt="Measured on agentmods" height="20"></a>
Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,947 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00108 $0.05947
Opus 5 $0.00054 $0.02974
Sonnet 5 $0.00022 $0.01189
Haiku 4.5 $0.00011 $0.00595

Measured yesterday against content hash 3a88f94d3729, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

leadbay_build_campaign 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.

.claude-plugin/plugins/leadbay/skills/leadbay_build_campaign/SKILL.md · 204 lines

How it starts

The opening of the file, as written. The whole thing — 204 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.

Build me a Leadbay campaign from scratch<if the user supplied this argument, render the short parenthetical or inline clause derived from it; otherwise empty. Source: Optional: a name for the campaign. Omit and one is derived from the lens/audience + date (or the backend AI-names it).> — a cohort of <the user-supplied value if any; otherwise a sensible default. Source: Optional: how many fully-actionable leads to build (default 20). The loop keeps discovering, qualifying and enriching until this many in-ICP leads each have a reachable target-title contact — or the lens is exhausted. Higher counts take longer and consume more quota.> fully-actionable leads: each in-ICP, high ai_agent_lead_score, AND with a reachable buyer contact. <if the user supplied this argument, render the short block derived from it; otherwise empty. Source: Optional: a fresh audience to target (e.g. 'dental clinics in Texas'). Omit to build from your ACTIVE lens — the default.> <if the user supplied this argument, render the short block derived from it; otherwise empty. Source: Optional: the exact buyer job titles to enrich, comma-separated (e.g. 'VP Sales, Head of Growth, Director of Business Development'). Omit and the buyer persona is derived from what you sell. A lead only counts toward the target when it has a reachable contact matching one of these titles.>

Run this end-to-end, autonomously, without pausing. Do NOT stop to confirm the audience, do NOT stop to confirm the enrichment spend, do NOT ask me to pick, and do NOT stop to hand off — just keep discovering, qualifying, enriching, and swapping until the cohort holds <the count_or_default (as extracted above)> leads that each meet EVERY requirement (in-ICP, high ai_agent_lead_score, and a reachable target-title contact whose email/phone actually landed). The ONLY reasons to stop short: the lens genuinely can't supply that many buyer-ready in-ICP leads, or enrichment quota is exhausted (a backend 429). In those cases, finish with whatever you locked and tell me plainly how many you got and why it stopped. Enrichment consumes quota, not credits — never pre-refuse on a credit balance.

GATE — DEFER TO TOOL RENDERING. When you call a Leadbay composite that ships its own RENDERING block (every composite in 0.9.0+ does), render the response using that block's recipe verbatim — score bars, glyph palette, column order, hide-list, link priorities, all of it. Do NOT substitute prose, a numbered list, or a different column structure even when an orchestrating prompt's body suggests alternate framing. Prompt-specific commentary (motivational nudges, summaries, next-action recommendations) belongs ABOVE or BELOW the canonical table, never in place of it.

If the prompt's body and the tool's RENDERING appear to conflict, the tool's RENDERING wins for the structural layout; the prompt's voice wins for the commentary that surrounds it.

Read the full file on GitHub · 204 lines

Changes

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.

  1. yesterday Changed · +2 lines 3a88f94d3729
  2. 5d ago First seen · 202 lines · 108 tokens per session scan A bbe2dfe8420a

Subscribe to this mod's changes

leadbay_build_campaign is a skill published in the GitHub repository leadbay/mcp (0 stars, last pushed yesterday), licensed MIT. It adds 108 tokens to every session and 5,947 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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