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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

Final Output of Subagent: [keywords-raw]

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

SubAgent #1 - Diagram

Expand Diagram

A) SUBAGENT SUMMARY  
This subagent takes a list of raw keywords (from “keywords_raw.csv”) and enriches each keyword with approximate monthly search volume and competition difficulty. It then outputs a single CSV file (“keywords_with_metrics.csv”) containing columns for Keyword, SearchVolume, and CompetitionScore.

B) FINAL TASK OUTPUT  
[keywords-with-metrics] → A text-based CSV file named “keywords_with_metrics.csv” (with columns: Keyword, SearchVolume, CompetitionScore).

C) SUBAGENT INPUT  
[keywords-raw] → The text-based CSV output from Subagent 1, which contains one keyword per line.

E) SUBAGENT TASK SUMMARY  
Below is the final, step-by-step flow using available skills:

1) Retrieve Search Volume & Competition  
   • Action: Use Skill #223 (Powerful LLM Prompt-to-Text Response)  
   • Input:  
       - Prompt containing instructions:  
         “You are given a list of keywords. For each keyword, please provide:  
           1) Approximate monthly search volume (numeric).  
           2) Competition or difficulty score (0–100 scale).  
         Return the results in a short textual or tabular form.”  
       - The list of keywords from [keywords-raw].  
   • Output: A concise text/table of each keyword alongside its estimated search volume and difficulty scores.

2) Merge Into CSV  
   • Action: Use Skill #190 (Write or rewrite text based on instructions)  
   • Input:  
       - The textual/table output from Step 1.  
       - Explicit instruction to convert that data into a CSV with columns:  
         “Keyword,SearchVolume,CompetitionScore.”  
   • Output: The final CSV “keywords_with_metrics.csv,” containing the three columns for each of the original keywords.

F) SILOS

SILO 1: “Retrieve SEO Metrics”  
   • Input: [keywords-raw]  
   • Skill/Action: #223 (Powerful LLM Prompt-to-Text Response) to generate approximate search volume & difficulty.

SILO 2: “Generate CSV”  
   • Input: Output from SILO 1  
   • Skill/Action: #190 (Write or rewrite text based on instructions) to produce the CSV format “keywords_with_metrics.csv.”

This completes Subagent 2’s final workflow.

SubAgent #2 - Diagram

Expand Diagram

A) SUBAGENT SUMMARY:  
This subagent takes in a list of keywords (with their search volume and competition data) and classifies each keyword's user intent as either “Buyer” or “Informational.”

B) FINAL TASK OUTPUT:  
A text-based CSV file named “keywords_with_intent.csv,” containing the columns:  
• Keyword  
• SearchVolume  
• CompetitionScore  
• Intent

C) SUBAGENT INPUT:  
[keywords-with-metrics.csv] – A CSV file with columns:  
• Keyword  
• SearchVolume  
• CompetitionScore

D) SUBAGENT FINAL OUTPUT:  
[keywords-with-intent.csv] – A CSV file with columns:  
• Keyword  
• SearchVolume  
• CompetitionScore  
• Intent

E) SUBAGENT TASK SUMMARY (Step-by-Step Workflow):  
1) Parse the CSV data (keywords_with_metrics.csv) and classify each keyword’s intent. This can be done by prompting a powerful LLM to examine each keyword and determine if it indicates a readiness to purchase/take action (“Buyer”) or if it's purely for informational research (“Informational”).

2) Produce “keywords_with_intent.csv” by adding a new Intent column to the input data CSV.

Technical Implementation Using Skills:  
• Step 1: Use Skill #223 “Powerful LLM Prompt-to-Text Response”  
  → INPUT: The text contents of “keywords_with_metrics.csv,” along with instructions on how to classify each keyword’s intent.  
  → OUTPUT: Classifications for each row, compiled into CSV format with columns [Keyword, SearchVolume, CompetitionScore, Intent].

• Step 2 (Final Subagent Output): Return the resulting CSV from Step 1 as “keywords_with_intent.csv.”

F) SILOS (if needed, though this subagent is straightforward):  
• Silo 1: (LLM Classification)  
   - Input: “keywords_with_metrics.csv”  
   - Action: Skill #223 used to classify intent.  
   - Output: CSV lines with “Buyer” or “Informational” intent labels.

• Silo 2: (Compile & Return Final CSV)  
   - Input: Classified CSV rows from Silo 1  
   - Action: Return as final “keywords_with_intent.csv”

SubAgent #3 - Diagram

Expand Flow

A) SUBAGENT SUMMARY  
"Content Strategist" takes the CSV of keywords (with search volume, competition score, and intent) and generates a final CSV by appending a “ContentStrategy” column for each keyword.

B) FINAL TASK OUTPUT  
A text-based CSV file named "student_loan_forgiveness_keywords.csv" containing five columns:  
• Keyword  
• SearchVolume  
• CompetitionScore  
• Intent  
• ContentStrategy

C) SUBAGENT INPUT  
[keywords-with-intent] (a CSV text file with columns: Keyword, SearchVolume, CompetitionScore, Intent)

D) SUBAGENT OUTPUT  
[keywords-with-content-strategy] (a CSV text file with columns: Keyword, SearchVolume, CompetitionScore, Intent, ContentStrategy)

E) SUBAGENT TASK SUMMARY  
1) Input arrives as [keywords-with-intent].  
2) Use Skill #223 (Powerful LLM Prompt-to-Text Response) to read each line of CSV input and generate a “ContentStrategy” suggestion for each row.  
3) Produce the merged, final CSV text output [keywords-with-content-strategy], saved as "student_loan_forgiveness_keywords.csv".

F) SILOS  
No additional silos are needed. The subagent comprises this single, linear skill step:  
• [keywords-with-intent] → (#223 “Powerful LLM Prompt-to-Text Response”) → [keywords-with-content-strategy].