Keyword Strategy For Loans Report

AI Agent Training Prompt Objective: Identify keywords related to "student loan forgiveness" that meet the following criteria: High Search Volume (prioritize keywords with consistent monthly searches). Low Competition (keywords with low difficulty scores or fewer authoritative domains ranking for them). Buyer Intent (keywords indicating users are ready to take action, e.g., "apply," "consultation," "services," "calculator," "eligibility check"). Key Instructions for the Agent: Focus on Keyword Types: Long-tail phrases (e.g., "student loan forgiveness application requirements"). Question-based queries (e.g., "How to qualify for Biden student loan forgiveness?"). Buyer-intent keywords (e.g., "student loan forgiveness lawyer consultation" or "best loan forgiveness programs for teachers"). Localized terms (e.g., "student loan forgiveness California"). Avoid: Generic terms (e.g., "student loans"). Overly competitive keywords (e.g., "FAFSA"). Informational-only queries (e.g., "what is student loan forgiveness?"). Data Sources & Tools (hypothetical): Use SEO tools (e.g., SEMrush, Ahrefs, Ubersuggest) to analyze search volume and competition. Scrape forums, Reddit, or Q&A sites (e.g., Quora) for recurring questions/pain points. Analyze competitor gaps (websites ranking for loan forgiveness content). Prioritization Framework: Score Keywords: Combine search volume, competition, and buyer intent signals. Content Angles: Highlight keywords that align with conversion goals (e.g., "student loan forgiveness application help," "PSLF certification specialist"). Example Output Format: Keyword: "student loan forgiveness eligibility calculator" Search Volume: [e.g., 5k/mo] Competition: Low (Difficulty Score: 25/100) Intent: Buyer (users likely seeking tools/services) Content Strategy: Create a free eligibility calculator tool with a lead magnet. Keyword: "PSLF form help 2024" Search Volume: [e.g., 3k/mo] Competition: Medium (Difficulty Score: 40/100) Intent: Buyer (users needing assistance with paperwork) Content Strategy: Offer a paid document review service or webinar. Additional Analysis to Include: Competitor Gaps: Identify keywords competitors rank for but lack detailed/updated content. Trends: Highlight rising keywords (e.g., "new SAVE plan loan forgiveness"). Content Ideas: Blog posts, landing pages, or tools tailored to high-priority keywords.

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OVERVIEW OF SUBAGENTS  
• Subagent 1: "Keyword Researcher" - Generates the initial list of raw keywords.  
• Subagent 2: "Metrics Extractor" - Retrieves search volume and competition data for the keywords.  
• Subagent 3: "Intent Classifier" - Classifies each keyword's user intent (buyer vs. informational).  
• Subagent 4: "Content Strategist" - Recommends content strategies for each keyword and produces the final CSV.

SUBAGENT 1: "Keyword Researcher"  
Final Output: keywords_raw.csv

Sequence of Actions:  
1) Research Topic  
   • Description: Briefly research "student loan forgiveness" to gather core subtopics.  
   • Output: A short research summary (roughly 300-500 characters).

2) Brainstorm Keywords  
   • Description: From the research summary, provide about 5 relevant keywords aligned with the user's criteria.  
   • Output: A brief list of 5 keywords with minimal commentary.

3) Compile Raw CSV  
   • Description: Convert the 5 keywords into a text-based CSV file (keywords_raw.csv) with one keyword per line.

SUBAGENT REQUIRED INPUT: {taskObjective}  
SUBAGENT FINAL OUTPUT: [keywords-raw]

SUBAGENT 2: "Metrics Extractor"  
Final Output: keywords_with_metrics.csv

Sequence of Actions:  
1) Retrieve Search Volume & Competition  
   • Description: For each keyword in the input CSV, get approximate monthly search volume and difficulty (from an SEO tool or database).  
   • Output: A short text response or table listing each keyword with its volume and difficulty score.

2) Merge Into CSV  
   • Description: Combine the search volume and difficulty data into a single CSV file (keywords_with_metrics.csv) with columns:  
     - Keyword  
     - SearchVolume  
     - CompetitionScore

SUBAGENT REQUIRED INPUT: [keywords-raw]  
SUBAGENT FINAL OUTPUT: [keywords-with-metrics]

SUBAGENT 3: "Intent Classifier"  
Final Output: keywords_with_intent.csv

Sequence of Actions:  
1) Classify Keyword Intent  
   • Description: Label each keyword as Buyer or Informational, based on the keyword text.  
   • Output: A text-based CSV (keywords_with_intent.csv) with columns:  
     - Keyword  
     - SearchVolume  
     - CompetitionScore  
     - Intent

SUBAGENT REQUIRED INPUT: [keywords-with-metrics]  
SUBAGENT FINAL OUTPUT: [keywords-with-intent]

SUBAGENT 4: "Content Strategist"  
Final Output: student_loan_forgiveness_keywords.csv

Sequence of Actions:  
1) Recommend Content Strategy  
   • Description: For each keyword, add a "ContentStrategy" column (e.g., "calculator tool," "application help," "consultation service").  
   • Output: A text-based CSV (student_loan_forgiveness_keywords.csv) with columns:  
     - Keyword  
     - SearchVolume  
     - CompetitionScore  
     - Intent  
     - ContentStrategy

SUBAGENT REQUIRED INPUT: [keywords-with-intent]  
SUBAGENT FINAL OUTPUT: [keywords-with-content-strategy]

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Then, agent response...

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I'll create the G) TASKFLOW SUMMARY section based on the skills/tasks identified in sections E) and F) for Subagent 1. Here's the complete updated summary:

A) SUBAGENT SUMMARY  
This subagent ("Keyword Researcher") creates a short (300–500-character) research summary on "student loan forgiveness" and then uses that summary to generate five relevant raw keywords, which it compiles into a single CSV file.

B) FINAL TASK OUTPUT  
[keywords-raw]: A text-based CSV file named "keywords_raw.csv," with one keyword per line.

C) SUBAGENT INPUT  
{taskObjective} – High-level objective or instructions about researching "student loan forgiveness" and aligning keywords with the user's criteria.

E) SUBAGENT TASK SUMMARY  
1) Receive {taskObjective}.  
2) Skill #223 (Powerful LLM Prompt-to-Text Response):  
   • Prompt: "Create a short, ~300–500 character summary about 'student loan forgiveness' and its most important associated subtopics."  
   • Output: shortResearchSummary (a concise text summary).

3) Skill #223 (Powerful LLM Prompt-to-Text Response):  
   • Prompt: "Using the short research summary about 'student loan forgiveness,' provide exactly five relevant keywords that fit the criteria (high volume, low competition, clear buyer intent). Provide minimal commentary."  
   • Output: rawKeywordList (five keywords with brief notes).

4) Skill #223 (Powerful LLM Prompt-to-Text Response):  
   • Prompt: "Convert this list of five keywords into a text-based CSV (named keywords_raw.csv) with one keyword per line, no additional columns."  
   • Output: keywords_raw.csv

F) SILOS  
• Silo 1: Generate short research summary (Skill #223)  
• Silo 2: Brainstorm five keywords (Skill #223)  
• Silo 3: Compile raw CSV (Skill #223)

G) TASKFLOW SUMMARY

Task 1:
- ID: #223
- Title: Generate Research Summary for Student Loan Forgiveness
- Description: Creates a concise 300-500 character summary about student loan forgiveness and its key subtopics, focusing on core concepts and current programs
- Input(s): {taskObjective}
- Output: [shortResearchSummary]

Task 2:
- ID: #223
- Title: Generate Targeted Keywords from Research Summary
- Description: Analyzes the research summary to produce exactly five relevant keywords matching specified criteria for volume, competition, and buyer intent
- Input(s): [shortResearchSummary]
- Output: [rawKeywordList]

Task 3:
- ID: #223
- Title: Convert Keywords to CSV Format
- Description: Transforms the five-keyword list into a simple CSV file format with one keyword per line, no headers or additional columns
- Input(s): [rawKeywordList]
- Output: [keywords-raw]

subagent1mermaid

Mermaid Image - https://static.aiz.ac/1740306357-mermaid/mermaid1.png

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subagent1-json-array

[
  {
    "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' and aligning keywords with the user’s criteria.",
    "inputRequired": [
      "taskObjective"
    ],
    "outputDescription": "A short text summary (300-500 characters) highlighting the most important points for people seeking loan forgiveness services.",
    "outputName": "shortResearchSummary",
    "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 and applies the specified criteria to generate relevant, buyer-intent keywords.",
    "inputRequired": [
      "shortResearchSummary"
    ],
    "outputDescription": "A minimal list of exactly five keywords, each on its own line, focusing on high volume, low competition, and strong buyer intent.",
    "outputName": "rawKeywordList",
    "promptInstruction": "Using the research summary in shortResearchSummary, 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 the previous step and prepares a structured CSV output.",
    "inputRequired": [
      "rawKeywordList"
    ],
    "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 rawKeywordList into a simple CSV with one keyword per line, no header, no additional columns, and no commentary. Save as keywords_raw.csv."
  }
]

subagent1-json-array-mermaid

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<html>
<head>
  <title>Mermaid Diagram Example</title>
  <script src="https://cdnjs.cloudflare.com/ajax/libs/mermaid/9.4.3/mermaid.min.js"></script>
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<body>
  <div class="mermaid">
flowchart TD
    Start(Start) --> Input[Input taskObjective]
    Input --> Sub1[Subagent 1: Keyword Researcher]
    
    subgraph Sub1
        Task1[Task 2231: Generate Research Summary]
        Task2[Task 2232: Generate Keywords]
        Task3[Task 2233: Create CSV]
        
        Task1 --> |shortResearchSummary| Task2
        Task2 --> |rawKeywordList| Task3
    end
    
    Sub1 --> Output[Output: keywords_raw.csv]
    Output --> End(End)
    
    style Start fill:#f9f,stroke:#333,stroke-width:2px
    style End fill:#f9f,stroke:#333,stroke-width:2px
    style Sub1 fill:#e6f3ff,stroke:#333,stroke-width:2px
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