Results for “lead-intelligence”

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Comprehensive guide to led. Master the concepts, implementation, best practices, and real-world applications of led in professional environments.

Turn "which leads should I work?" into a lead-by-lead work plan. Per-lead status (never-touched, engaged-not-replied, gone-cold, hot), days-dark per lead, last-touch quality, a re-engagement ranker, and a recommended next action for every lead. Built for SDRs and full-cycle reps, customizable to your CRM and your outreach tool. Trigger on "which leads should I work?", "who haven't I touched?", "show me cold leads", "hot leads to follow up on", "who's gone dark?", "re-engagement candidates", or any lead-level work-assignment question.

Imported skill tracing from langchain

Score raw leads as HOT/WARM/COOL based on config-driven weights from agency.config.json

Routes qualified leads to the right team member or sequence based on score, segment, and source. Applies configurable routing rules to assign leads to founder direct, SDR sequences, or nurture campaigns.

Use when team expertise is needed to unblock implementation decisions.

Recursive self-improving holon λ(ο,Κ,Σ).τ' for knowledge compounding and schema evolution. USE WHEN learning, improving, optimizing, assessing, reflecting, debugging, synthesizing, or refining—whether human, AI, or organizational. Triggers on /learn, /compound, /improve, /refine, /optimize, /assess, /reflect, "lessons learned", "best practices", "continuous improvement". Preserves Κ-monotonicity, η≥4, homoiconicity.

Universal transformation λ(ο,K).τ with recursive self-improvement. USE WHEN routing reasoning, validating knowledge graphs, preparing CICM/ANZCA examinations, or when self-improvement of reasoning/architecture/context is required. Routes queries through R0-R3 complexity pipelines, validates topology (η≥target) and governance (KROG), emits per style (Φ), and compounds learnings into knowledge K. Triggers on complexity assessment, multi-step reasoning, examination mode, or /λ invocation.

Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.