Keyword Strategy For Loans Report - Confirm & Copy Final Agent Code

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[
  {
    "taskID": 2231,
    "semanticTitleOfTask": "Generate Research Summary for Student Loan Forgiveness",
    "taskDescription": "This task creates a concise text summary (300-500 characters) about student loan forgiveness, including key subtopics such as major programs, eligibility, and application processes.",
    "inputDescription": "Takes the high-level objective or instructions about researching 'student loan forgiveness' from VARIABLE1.",
    "inputRequired": [
      "VARIABLE1"
    ],
    "outputDescription": "A short text summary (300-500 characters) highlighting the most important points for people seeking loan forgiveness services.",
    "outputName": "short_research_summary",
    "promptInstruction": "You are a keyword research specialist. Create a concise summary (300-500 characters) about student loan forgiveness. Focus on: 1) Current major programs (e.g., Biden’s debt relief, PSLF) 2) Key eligibility requirements 3) Application processes 4) Recent changes or updates. Emphasize aspects relevant to people actively seeking loan forgiveness services or assistance."
  },
  {
    "taskID": 2232,
    "semanticTitleOfTask": "Generate Targeted Keywords from Research Summary",
    "taskDescription": "This task uses the research summary to produce exactly five targeted keywords that meet high-volume, low-competition, and buyer-intent criteria, excluding generic or purely informational queries.",
    "inputDescription": "Takes the short text summary about student loan forgiveness from short_research_summary and applies the specified criteria to generate relevant, buyer-intent keywords.",
    "inputRequired": [
      "short_research_summary"
    ],
    "outputDescription": "A minimal list of exactly five keywords, each on its own line, focusing on high volume, low competition, and strong buyer intent.",
    "outputName": "raw_keyword_list",
    "promptInstruction": "Using the research summary in short_research_summary, generate exactly five keywords related to student loan forgiveness that meet these criteria: 1) High search volume potential 2) Low competition 3) Clear buyer intent 4) Preference for long-tail phrases. Exclude generic or purely informational queries. Return each keyword on its own line with no additional commentary."
  },
  {
    "taskID": 2233,
    "semanticTitleOfTask": "Convert Keywords to CSV Format",
    "taskDescription": "This task transforms the five-keyword list into a simple CSV file with one keyword per line and no extra information, called keywords_raw.csv.",
    "inputDescription": "Takes the list of five keywords generated in raw_keyword_list and prepares a structured CSV output.",
    "inputRequired": [
      "raw_keyword_list"
    ],
    "outputDescription": "A text-based CSV file named keywords_raw.csv, containing one keyword per line with no header or extra columns.",
    "outputName": "keywords_raw",
    "promptInstruction": "Convert the keyword list in raw_keyword_list into a simple CSV with one keyword per line, no header, no additional columns, and no commentary. Save as keywords_raw.csv."
  },
  {
    "taskID": 223,
    "semanticTitleOfTask": "Generate SEO Metrics Using an LLM",
    "taskDescription": "This task uses an advanced language model to create approximate monthly search volume and competition difficulty scores for each input keyword. It returns a structured text or table containing each keyword’s metrics for further processing.",
    "inputDescription": "A text-based CSV of raw keywords (one per line) from keywords_raw. Each keyword will be fed to the LLM to determine its approximate monthly search volume and a relevant competition score (0–100 scale).",
    "inputRequired": [
      "keywords_raw"
    ],
    "outputDescription": "A structured text or table listing each keyword alongside its estimated Search Volume and Competition Score for subsequent conversion to CSV.",
    "outputName": "metrics_text_output",
    "promptInstruction": "You are an SEO expert assistant. Given a list of keywords, produce a table listing each keyword, a realistic monthly search volume, and a competition score on a 0–100 scale. Maintain realism by assigning higher difficulty for more competitive terms and conservative volumes for local or long-tail queries. Format the table with three columns: Keyword, Monthly Search Volume, Competition Score."
  },
  {
    "taskID": 190,
    "semanticTitleOfTask": "Convert Tabular SEO Metrics into CSV Format",
    "taskDescription": "This task transforms the structured text or table containing SEO metrics into a properly formatted CSV file, ensuring each row has columns for Keyword, SearchVolume, and CompetitionScore. The CSV is then ready for any subsequent steps in the workflow.",
    "inputDescription": "Receives the structured text or table output containing keywords with their approximate monthly search volume and competition score, from metrics_text_output.",
    "inputRequired": [
      "metrics_text_output"
    ],
    "outputDescription": "A valid CSV file named 'keywords_with_metrics.csv' with columns: Keyword, SearchVolume, and CompetitionScore, providing each keyword’s estimated metrics in a standard data format.",
    "outputName": "keywords_with_metrics",
    "promptInstruction": "no instruction"
  },
  {
    "taskID": 301,
    "semanticTitleOfTask": "Load and parse CSV data from keywords_with_metrics.csv",
    "taskDescription": "This task reads the existing CSV with columns [Keyword, SearchVolume, CompetitionScore] and loads the data in memory for further processing. It ensures the data is structured and ready for classification.",
    "inputDescription": "Requires one CSV file named 'keywords_with_metrics.csv' containing the columns [Keyword, SearchVolume, CompetitionScore]. The file is expected to be valid and properly formatted.",
    "inputRequired": [
      "keywords_with_metrics"
    ],
    "outputDescription": "An in-memory data structure (e.g., array or table) containing rows with columns [Keyword, SearchVolume, CompetitionScore]. This parsed data will be used for classification.",
    "outputName": "parsed_keywords",
    "promptInstruction": "no instruction"
  },
  {
    "taskID": 302,
    "semanticTitleOfTask": "Classify each keyword as Buyer or Informational using an LLM",
    "taskDescription": "This task analyzes the parsed keywords and assigns an 'Intent' label to each one, either 'Buyer' or 'Informational,' by examining the language cues and user context. It leverages a language model for category decisions.",
    "inputDescription": "Requires the parsed keywords from parsed_keywords, each row containing [Keyword, SearchVolume, CompetitionScore].",
    "inputRequired": [
      "parsed_keywords"
    ],
    "outputDescription": "A data structure with the same rows, plus an additional 'Intent' column indicating either 'Buyer' or 'Informational.'",
    "outputName": "classified_keywords",
    "promptInstruction": "You are an expert in SEO and user intent classification. Your task is to analyze each keyword from the input data and determine if it indicates a 'Buyer' or 'Informational' search intent. Classify as 'Buyer' if it suggests action-taking or service-related needs (e.g., apply, help, calculator), and classify as 'Informational' if it suggests a request for basic details, definitions, or general knowledge (e.g., what, how, guide). Return each row as: keyword,searchVolume,competitionScore,intent."
  },
  {
    "taskID": 303,
    "semanticTitleOfTask": "Generate final 'keywords_with_intent.csv' with the new Intent column",
    "taskDescription": "This task compiles the classified keyword data into a final CSV titled 'keywords_with_intent.csv.' It preserves all original columns plus the newly added 'Intent' column, ensuring proper CSV formatting.",
    "inputDescription": "Takes the classified keywords from classified_keywords, which have columns [Keyword, SearchVolume, CompetitionScore, Intent].",
    "inputRequired": [
      "classified_keywords"
    ],
    "outputDescription": "A fully constructed CSV file containing all columns from the input plus the appended 'Intent' field, ready for downstream use.",
    "outputName": "keywords_with_intent",
    "promptInstruction": "no instruction"
  },
  {
    "taskID": 1,
    "semanticTitleOfTask": "Load CSV from keywords_with_intent",
    "taskDescription": "Mechanical task that accepts the CSV input (Keyword, SearchVolume, CompetitionScore, Intent) and prepares it for analysis.",
    "inputDescription": "Requires the CSV text file named keywords_with_intent. It contains all relevant keyword data needed for strategy generation.",
    "inputRequired": [
      "keywords_with_intent"
    ],
    "outputDescription": "A loaded dataset of the CSV rows, ready to be processed by the LLM for content strategy recommendations.",
    "outputName": "loaded_keywords",
    "promptInstruction": "no instruction"
  },
  {
    "taskID": 2,
    "semanticTitleOfTask": "Generate Content Strategy Recommendations (Skill #223)",
    "taskDescription": "Uses an LLM to analyze each keyword row, considering metrics and intent, then appends a concise ContentStrategy recommendation for each keyword.",
    "inputDescription": "The loaded CSV data with columns: Keyword, SearchVolume, CompetitionScore, and Intent from loaded_keywords.",
    "inputRequired": [
      "loaded_keywords"
    ],
    "outputDescription": "An updated CSV dataset with an additional column: ContentStrategy, providing short recommendations aligned with user intent and search metrics.",
    "outputName": "keywords_with_content_strategy",
    "promptInstruction": "You are a content strategy expert for student loan forgiveness. For each row (Keyword, SearchVolume, CompetitionScore, Intent), generate a strategy (under 15 words) that aligns with the user’s stage in the buyer journey and focuses on conversions where intent is buyer. Suggest a relevant format (calculator, guide, landing page, consultation, etc.). Example: \"Create a targeted application guide with CTA.\""
  },
  {
    "taskID": 3,
    "semanticTitleOfTask": "Output Final CSV student_loan_forgiveness_keywords.csv",
    "taskDescription": "Combines the newly appended ContentStrategy column with existing columns, producing the final CSV saved as 'student_loan_forgiveness_keywords.csv'.",
    "inputDescription": "The updated CSV data from keywords_with_content_strategy including the ContentStrategy column.",
    "inputRequired": [
      "keywords_with_content_strategy"
    ],
    "outputDescription": "A final CSV file named 'student_loan_forgiveness_keywords.csv' containing five columns: Keyword, SearchVolume, CompetitionScore, Intent, ContentStrategy.",
    "outputName": "student_loan_forgiveness_keywords_csv",
    "promptInstruction": "no instruction"
  }
]

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