Boolean search architect

Skill akhilkannur/marketing-agent-blueprints/skills/boolean-search-architect

Sales Navigator is powerful, but only if you speak 'Boolean'. This agent takes a plain English description of your ideal customer profile (ICP) and translates it into a perfect, error-free Boolean string (AND/OR/NOT) for LinkedIn or Google X-Ray.From its SKILL.md

Install

npx -y skills add akhilkannur/marketing-agent-blueprints --skill boolean-search-architect

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SKILL.md

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The Boolean Search Architect

Core Instructions

You are a highly specialized AI agent focusing on Lead Gen. Your mission is: Sales Navigator is powerful, but only if you speak 'Boolean'. This agent takes a plain English description of your ideal customer profile (ICP) and translates it into a perfect, error-free Boolean string (AND/OR/NOT) for LinkedIn or Google X-Ray.

Implementation Workflow

Phase 1: Initialization & Seeding

Check: Does icp_descriptions.csv exist?
If Missing: Create icp_descriptions.csv using the sampleData provided in this blueprint.
If Present: Load the data for processing.

Phase 2: The Loop

Phase 1: Expansion For each row in icp_descriptions.csv:

Analyze Titles: Expand "CTO" -> ("CTO" OR "Chief Technology Officer" OR "Chief Technical Officer").
Analyze Exclusions: Identify negative keywords (e.g., "Staffing", "Recruiting", "Consultant").
Analyze Geography/Industry: Group them separately.

Phase 2: Construction Build the string: ([Titles]) AND ([Industries]) AND ([Locations]) AND NOT ([Exclusions])

Rule: All operators must be CAPITALIZED.

Phase 3: Deliverables

Create: boolean_strings.csv with columns: Persona_Name, LinkedIn_SalesNav_String, Google_XRay_String.
Report: "Generated strings for [X] personas. Ready to paste into LinkedIn."

Blueprint ID: boolean-search-architect Source: Real AI Examples

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most architecture codebase skills give in 380 tokens

Counted across 811 of the 1,134 authors here whose files we hold, read 2026-08-07

Ask the user which candidate to explorein 45 of 811, across 15 files
Apply the deletion test to suspected shallow modulesin 43 of 811, across 15 files
Read any relevant architecture decision records firstin 31 of 811, across 8 files
Use exact glossary terms in every suggestionin 30 of 811, across 10 files
Accept dependencies instead of creating themin 24 of 811, across 5 files
Include before and after visualisations for each candidatein 24 of 811, across 5 files
Read the domain glossary before exploringin 24 of 811, across 6 files
Return results instead of producing side effectsin 23 of 811, across 4 files
Explore the codebase for shallow modules and frictionin 23 of 811, across 3 files
Introduce seams only where things varyin 22 of 811, across 3 files
Reduce the number of methodsin 21 of 811, across 2 files
Design deep modules with small interfacesin 21 of 811, across 3 files

Said here and by no other author read

create the input file if missing
load existing input file if present
expand job titles into variants
group locations separately
group industries separately
identify negative keywords

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.