Local SEO Keywords Generator

Local SEO research to discover search terms and keywords that can be used for pages in websites.

First, pre-entered info...

final-output2

A single comma-separated values file named local_keyword_plan.csv. Every row represents one web page (new or to be optimised) and contains, at minimum: • page_slug – proposed URL or existing URL • primary_keyword – main local search term the page should target • supporting_keywords – semicolon-separated list of closely related terms • avg_monthly_searches – local search volume for the primary keyword • difficulty_score – relative competition metric • location – city/area modifier for the query • intent – informational / transactional / navigational / "near-me" The file can be opened by humans in Excel/Sheets or ingested by another script/CMS.

subagents-extracted2

OVERVIEW OF SUBAGENTS • Sub-Agent 1 ProfileBuilder → produces Component 1 • Sub-Agent 2 SeedGenerator → produces Component 2 • Sub-Agent 3 KeywordAnalyzer → produces Component 3 • Sub-Agent 4 PlanMaker → produces Component 4 (final) SUBAGENT 1 — ProfileBuilder Name: ProfileBuilder Final Output: business_profile.json Dependency: none SUBAGENT REQUIRED INPUT: {variable1}, {variable2}, {variable3}, {variable4} {variable1} = business_name {variable2} = services (comma-separated) {variable3} = service_area (comma-separated) {variable4} = website_url (optional) SUBAGENT FINAL OUTPUT: [business-profile-json] Action sequence 1) Skill #223 – Parse the raw user facts (variables 1-4) and structure them into clean JSON with keys: business_name, services[], service_area[], website. 2) Skill #185 – Save the JSON as business_profile.json (UTF-8). SUBAGENT 2 — SeedGenerator Name: SeedGenerator Final Output: seed_keywords.csv Dependency: business_profile.json SUBAGENT REQUIRED INPUT: [business-profile-json] SUBAGENT FINAL OUTPUT: [seed-keywords-csv] Action sequence 1) Skill #223 – Read business_profile.json and generate up to 25 deduplicated seed phrases such as "[service] in [city]", "best [service] [city]", "[service] near me [city]". 2) Skill #185 – Write the one-column list to seed_keywords.csv (UTF-8). SUBAGENT 3 — KeywordAnalyzer Name: KeywordAnalyzer Final Output: keyword_metrics.csv Dependency: seed_keywords.csv SUBAGENT REQUIRED INPUT: [seed-keywords-csv] SUBAGENT FINAL OUTPUT: [keyword-metrics-csv] Action sequence 1) Skill #223 – For each seed term, expand with up to three closely related variants (≤ 100 total). Estimate avg_monthly_searches (local) and difficulty_score (0–100) for every keyword (heuristics or API). 2) Skill #185 – Save a three-column table (keyword, avg_monthly_searches, difficulty_score) to keyword_metrics.csv (UTF-8). SUBAGENT 4 — PlanMaker Name: PlanMaker Final Output: local_keyword_plan.csv Dependencies: keyword_metrics.csv, business_profile.json SUBAGENT REQUIRED INPUT: [keyword-metrics-csv], [business-profile-json] SUBAGENT FINAL OUTPUT: [local-keyword-plan-csv] Action sequence 1) Skill #223 – Load keyword_metrics.csv and business_profile.json. Cluster keywords (≤ 30 clusters). For each cluster: • choose a primary_keyword (best volume vs. difficulty) • list supporting_keywords (semicolon-separated) • determine intent (informational / transactional / navigational / near-me) • create a page_slug (e.g. /service-in-city/ or reuse existing URL) • pull avg_monthly_searches & difficulty_score for the primary_keyword • set location (city/area token) 2) Skill #185 – Write all rows to local_keyword_plan.csv (UTF-8, columns in the order specified in section B).

subagentnumber

[subagentnumber]

Then, agent response...

subagent1refined-new-json-core

I'll add the G) TASKFLOW SUMMARY section to SUBAGENT 1's specification, maintaining all existing sections and extracting the task IDs from the original flow: SUBAGENT 1 - PROFILEBUILDER -------------- A) SUBAGENT SUMMARY: "ProfileBuilder" converts the raw user-supplied business details (name, services, service areas, optional URL) into a well-formed UTF-8 JSON file called business_profile.json. B) FINAL TASK OUTPUT: business_profile.json – a single UTF-8 JSON file with the following schema { "business_name": string, "services": [string, …], // trimmed, lowercase, deduplicated "service_area": [string, …], // trimmed, lowercase, deduplicated "website": string | null // null if not supplied } C) SUBAGENT INPUT (from end-user): {variable1} = business_name (string) {variable2} = services (comma-separated string) {variable3} = service_area (comma-separated string) {variable4} = website_url (optional string, may be blank) E) SUBAGENT TASK SUMMARY: [as provided in original] F) SILOS: No additional silos required – the subagent is a single linear flow. G) TASKFLOW SUMMARY: Task 1: - ID: #223 - Title task: Parse and Structure Business Profile Data - Description: Takes raw business inputs and transforms them into a structured JSON format with specific schema requirements, including array processing, text normalization, and data validation - Input(s) involved: {variable1}, {variable2}, {variable3}, {variable4} - Output token name: [structured-json-text] Task 2: - ID: #185 - Title task: Save Business Profile JSON File - Description: Writes the structured JSON data to a UTF-8 encoded file named business_profile.json, ensuring proper file encoding and format preservation - Input(s) involved: [structured-json-text] - Output token name: [business-profile-json] This completes the taskflow summary, capturing both tasks from the original specification while maintaining their original ID numbers and establishing clear input/output relationships between them.

subagent1mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> graph TD subgraph SubAgent1[ProfileBuilder] A[User Input] --> B[variable1: business_name] A --> C[variable2: services] A --> D[variable3: service_area] A --> E[variable4: website_url] B & C & D & E --> F[Task #223: Parse and Structure] F -->|structured-json-text| G[Task #185: Save JSON File] G -->|business-profile-json| H[Final Output: business_profile.json] end style SubAgent1 fill:#f5f5f5,stroke:#333,stroke-width:2px style H fill:#98FB98,stroke:#333,stroke-width:2px style A fill:#ADD8E6,stroke:#333,stroke-width:2px </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>

Mermaid Image - https://static.aiz.ac/1757027817-mermaid/mermaid1.png

prompts-required

[prompts-required]

subagent1-json-array

[ { "taskID": 223, "semanticTitleOfTask": "LLM - Parse & Normalize Business Profile Inputs", "taskDescription": "This task employs a language model to convert raw business details—name, comma-separated services, service areas, and optional website—into a strictly formatted JSON object. It splits lists, trims whitespace, lowercases text, removes duplicates, and assigns null when no website is provided.", "inputDescription": "Four text inputs are supplied by the end-user: (1) business_name, (2) services list, (3) service_area list, and (4) website_url (may be blank). Services and service_area arrive as comma-delimited strings that must be parsed and cleaned before structuring.", "inputRequired": ["variable1", "variable2", "variable3", "variable4"], "outputDescription": "A single block of UTF-8 JSON text containing keys business_name, services[], service_area[], and website, fully normalized and compliant with the required schema, with no extra characters or commentary.", "outputName": "structured-json-text", "promptInstruction": "Take the inputs below and output ONLY valid UTF-8 JSON that matches this schema: {\"business_name\":string, \"services\":[], \"service_area\":[], \"website\":string|null}. • Split comma-separated lists for services and service_area into arrays. • Trim whitespace, lowercase each item, and remove duplicates. • If website_url is blank, set \"website\" to null. Do not add any explanations or formatting—return the pure JSON object only.\nINPUTS:\nbusiness_name: {{variable1}}\nservices: {{variable2}}\nservice_area: {{variable3}}\nwebsite_url: {{variable4}}" }, { "taskID": 185, "semanticTitleOfTask": "Save Business Profile JSON to UTF-8 File", "taskDescription": "This task writes the structured JSON text from Task #223 to disk as a file named business_profile.json, ensuring UTF-8 encoding and preserving the content exactly as received without modification.", "inputDescription": "Requires the JSON text token produced by Task #223 that contains the cleaned and structured business profile data.", "inputRequired": ["structured-json-text"], "outputDescription": "Creates a file called business_profile.json in the working directory, encoded in UTF-8, containing precisely the JSON text supplied by the previous task.", "outputName": "business-profile-json", "promptInstruction": "no instruction" } ]

subagent1-json-array-mermaid

<!DOCTYPE html> <html> <head> <title>Mermaid Diagram Example</title> <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script> </head> <body> <div class="mermaid"> flowchart TD START[Start ProfileBuilder SubAgent] --> IN[User Input] IN --> ip1[business_name] IN --> ip2[services list] IN --> ip3[service_area list] IN --> ip4[website_url] ip1 & ip2 & ip3 & ip4 --> T223[Task #223: LLM Parse & Normalize] T223 --> json[structured-json-text] json --> T185[Task #185: Save JSON File] T185 --> file[business_profile.json] file --> END[End ProfileBuilder SubAgent] style START fill:#a8e6cf style END fill:#ffd3b6 style T223 fill:#dcedc1 style T185 fill:#dcedc1 style json fill:#ffaaa5 style file fill:#ffaaa5 </div> <script> mermaid.initialize({ startOnLoad: true }); </script> </body> </html>