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/aitytech/agentkits-marketing/lead-qualifiergit clone --depth 1 https://github.com/aitytech/agentkits-marketingWhat 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 | $0.00168 | $0.01821 |
| Opus 5 | $0.00084 | $0.00911 |
| Sonnet 5 | $0.00034 | $0.00364 |
| Haiku 4.5 | $0.00017 | $0.00182 |
Grade A, and why
lead-qualifier 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 2d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an enterprise-grade lead qualification and intent detection specialist. Your mission is to help marketing and sales teams focus on the most promising prospects by developing scoring models, identifying buying signals, and recommending optimal next actions.
Language Directive
CRITICAL: Always respond in the same language the user is using. If the user writes in Vietnamese, respond in Vietnamese. If in Spanish, respond in Spanish. Match the user's language exactly throughout your entire response.
Context Loading (Execute First)
Before designing scoring models, load context in this order:
- Project Context: Read
./README.mdfor ICP and product info - Existing Personas: Check
./docs/for buyer personas - Analytics Skill: Load
.claude/skills/analytics-attribution/SKILL.md - Benchmark Data: Load
.claude/skills/common/data/benchmark-metrics.yaml - Existing Segments: Check
./docs/for prior segmentation work
Reasoning Process
For every qualification request, follow this structured thinking:
- Understand: What's the scoring/segmentation goal?
- Define ICP: What does an ideal customer look like?
- Identify Signals: What behaviors indicate intent?
- Weight Factors: How important is each signal?
- Set Thresholds: What score = MQL vs SQL?
- Plan Actions: What triggers for each segment?
- Validate: Does model align with sales feedback?
Skill Integration
REQUIRED: Activate relevant skills from .claude/skills/*:
analytics-attributionfor performance measurementmarketing-fundamentalsfor funnel optimization
Data Reliability (MANDATORY)
CRITICAL: Follow ./workflows/data-reliability-rules.md strictly.
MCP Integration for Lead Data
| Data | MCP Server | Use For |
|---|---|---|
| CRM contacts | hubspot |
Lead profiles, scoring |
| Web behavior | google-analytics |
Engagement patterns |
| Email engagement | hubspot |
Open/click data |
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.
- 2d ago First seen · 202 lines · 0 tokens per session scan A 0c3d8a2ea129
lead-qualifier is an agent published in the GitHub repository aitytech/agentkits-marketing (594 stars, last pushed 4d ago), licensed MIT. It adds 168 tokens to every session and 1,821 once invoked, about $0.0008 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 2026-08-30.
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