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A) SUBAGENT SUMMARY  
This subagent is responsible for creating a structured course outline (modules, learning objectives, recommended study hours, etc.) for a “Smart Home and IoT” course targeted at electronics technicians.

B) FINAL TASK OUTPUT  
A plain text file named “course_outline.txt” (or a JSON file named “course_outline.json”) containing the finalized course outline.

C) SUBAGENT INPUT  
• High-level description of the course goals, target audience (electronics technicians), and any special requirements (focus on Smart Home devices, IoT fundamentals, hardware/software integration, etc.).  
• Optional constraints such as approximate total duration or the number of modules.

E) SUBAGENT TASK SUMMARY  
Below is the detailed workflow of how the subagent will operate step-by-step, indicating how specific skills connect:

1. RECEIVE SUBAGENT INPUT  
   - The subagent begins by taking in the user’s high-level instructions about the desired outline.  
   - Example: “Create a Smart Home & IoT course outline for electronics technicians. The course should cover hardware, firmware, networking protocols, and practical troubleshooting, spanning around 12-15 hours.”

2. RESEARCH PHASE (#216 - Research Topic Deeply)  
   - The subagent calls Skill #216 with the input “Smart Home IoT Course Outline for Electronics Technicians” (plus any additional instructions from the user).  
   - This skill queries multiple sources to gather real-world examples of IoT courses, typical learning outcomes, and recommended pacing.

3. OUTLINE GENERATION PHASE (#223 - Powerful LLM Prompt-to-Text Response)  
   - The subagent then takes the summarized research from #216 and feeds it into Skill #223.  
   - This prompt might request: “Based on the researched data, please create a course outline that consists of modules, subtopics, learning objectives, and an approximate time allocation for each module. Emphasize smart home applications for electronics technicians.”

4. (OPTIONAL) REVISION OR REFINEMENT (#223 - Powerful LLM Prompt-to-Text Response)  
   - The subagent may optionally invoke #223 again to refine the structure, rename modules, add more detail, or adjust module times if the user requests changes.  
   - For instance: “Adjust the modules to include more hands-on electronics troubleshooting content. Keep the total course at ~12 hours.”

5. OUTPUT FINAL OUTLINE  
   - Once the outline is finalized, the subagent saves the text or JSON file as “course_outline.txt” (or “course_outline.json”).

F) SILOS (IF ANY)  
• Silo 1: Research → Using Skill #216 to gather relevant course structure data from external sources.  
• Silo 2: Outline Creation → Using Skill #223 to generate and refine the final outline based on the research.

This completes Subagent 1’s workflow for building the “Smart Home and IoT Course Outline” as a text/JSON file.

SubAgent #1 - Diagram

Expand Diagram

A) SUBAGENT SUMMARY  
This subagent (“Lesson Author”) takes a provided course outline plus any user instructions, conducts deep research for each lesson topic, then writes a structured series of lesson modules (in Markdown or HTML) that cover theoretical knowledge, examples, best practices, and safety tips for Smart Home & IoT.

B) FINAL TASK OUTPUT  
A single file named "lesson_content.md" (or “lesson_content.html”) containing all lesson modules in one cohesive document.

C) SUBAGENT INPUT  
• The course outline (from Subagent #1)  
• Any user preferences or style guidelines (e.g., desired length, tone, emphasis on certain technologies)

E) SUBAGENT TASK SUMMARY  
Below is the step-by-step flow for how this subagent creates the “lesson_content.md” file:

1) RECEIVE OUTLINE & INSTRUCTIONS  
   - Input: Course outline from Subagent #1 (e.g., “course_outline.txt”) plus user’s style/tone preferences.  
   - Action: Ensure we have a clear list of modules/chapters and any special user instructions or requirements.

2) FOR EACH MODULE/TOPIC → RESEARCH (Skill #216)  
   - Skill Used: #216 - Research Topic (Or Keyword) Deeply  
   - Input: A single module’s title or keyword (e.g., “Overview of IoT Fundamentals”)  
   - Output: A 1000-3000 character summary containing key information from online resources (including definitions, relevant industry use cases, best practices).

3) CONVERT RESEARCH INTO LESSON TEXT (Skill #223)  
   - Skill Used: #223 - Powerful LLM Prompt-to-Text Response  
   - Input: The research summary from Step 2, plus instructions to write a structured lesson (with examples, safety notes, practical tips) in Markdown or HTML.  
   - Output: A polished chunk of lesson text (e.g., “lesson_module_draft.md”).

4) REVIEW & (OPTIONAL) REVISE FOR CONSISTENCY (Skill #190)  
   - Skill Used: #190 - Write or rewrite text based on instructions (if needed)  
   - Input: The draft text from Step 3, plus any style and consistency guidelines.  
   - Output: Finalized version of that module’s text.

5) AGGREGATE ALL MODULES  
   - After all modules have been created and finalized, gather them in sequence.

6) COMPILE AND CREATE “lesson_content.md”  
   - Combine each finalized module (Step 4) into a single cohesive document, ensuring headings, subheadings, formatting, and disclaimers (if any) are consistent.  
   - Subagent Output: “lesson_content.md” ready for subsequent steps (e.g., merging into the final course PDF).

F) SILOS (IF APPLICABLE)  
• Silo 1: Module-by-Module Research and Drafting — repeated for each module/topic.  
• Silo 2: Final Aggregation and Consistency Pass — merges all modules into one cohesive file.

This completes the subagent workflow for creating the lesson content.

SubAgent #2 - Diagram

Expand Diagram

A) SUBAGENT SUMMARY  
This subagent designs and generates a set of practical “hands-on” exercises (including step-by-step instructions, code snippets, and any required wiring diagrams) for a Smart Home and IoT course aimed at electronics technicians.

B) FINAL TASK OUTPUT  
The final output is a single text file (e.g., “practical_exercises.txt”), which includes multiple lab exercises with embedded references (URLs) to any necessary images or diagrams generated during the process.

C) SUBAGENT INPUT  
1) High-level course outline and lesson topics (so that we know what labs we should create).  
2) Any specific requirements for the labs (e.g., required hardware platforms, sensors, complexity level).

E) SUBAGENT TASK SUMMARY  
Below is the step-by-step flow for generating the final “practical_exercises.txt” with any needed diagrams:

1) (Optional) LAB PLANNING WITH #223 (if not already specified)  
   - INPUT: High-level course outline, learning objectives, hardware requirements.  
   - ACTION: Call #223 (Powerful LLM Prompt-to-Text Response) to propose a structured list of labs or exercises, including a short description of what each lab covers.  
   - OUTPUT: Brief lab plan (e.g., “lab_plan_draft.txt”) with recommended number of labs, each labeled with a topic or objective.

2) GENERATE LAB INSTRUCTIONS WITH #223  
   - INPUT: For each lab, feed the subagent the lab’s topic (from the plan or from input) plus any specifics (e.g., communication protocol or sensor to be used).  
   - ACTION: Call #223 to produce detailed step-by-step instructions (tools, parts list, safety notes, code snippets if needed).  
   - OUTPUT: Lab text instructions (e.g., “lab_instructions_for_labX.txt”).

3) CREATE WIRING DIAGRAMS WITH #222 (IF NEEDED)  
   - INPUT: The lab’s hardware specification (e.g., sensor or microcontroller).  
   - ACTION: Call #222 (Make Image) with a text prompt such as “Create a clear wiring diagram for connecting a DHT11 temperature/humidity sensor to an Arduino.”  
   - OUTPUT: PNG image URLs for each relevant wiring diagram (returned by the skill).

4) MERGE INSTRUCTIONS AND DIAGRAM REFERENCES  
   - INPUT: The text for each lab’s instructions (from #223) along with any generated diagram URLs (from #222).  
   - ACTION: Incorporate the diagram URLs into each lab’s instructions so that the final text references these images.  
   - OUTPUT: A combined set of lab content, with each lab’s text and diagram links appended.

5) FINALIZE “PRACTICAL_EXERCISES.TXT”  
   - INPUT: All completed lab instructions (with diagrams or code references).  
   - ACTION: Consolidate them into one file, “practical_exercises.txt,” ensuring it is well-formatted and ready to be handed off to the next subagent or included in the final PDF.  
   - OUTPUT: “practical_exercises.txt” (plus the PNG URLs for diagrams).

F) SILOS  
SILO 1: Lab Planning  
• Purpose: (Optional) Generate a concise lab plan if not given.  
• Skills Used: #223 to create a draft set of labs.

SILO 2: Detailed Lab Instructions  
• Purpose: For each lab, generate thorough instructions (objectives, safety, step-by-step tasks, code).  
• Skills Used: #223 for text creation.

SILO 3: Wiring Diagram Creation  
• Purpose: Provide any needed circuit/wiring images.  
• Skills Used: #222 to generate diagram PNGs.

SILO 4: Final Assembly of Exercises  
• Purpose: Collect text instructions + diagram URLs, format them into a single file.  
• Skills Used: Simple text merging (within the subagent’s logic) to produce “practical_exercises.txt.”

SubAgent #3 - Diagram

Expand Flow

A) SUBAGENT SUMMARY  
This subagent (Assessment Creator) is responsible for generating comprehensive assessment materials—multiple choice questions, short-answer prompts, and practical/hands-on tasks—based on the existing course outline and lesson content.

B) FINAL TASK OUTPUT  
• “assessment_materials.txt” containing:  
  – Set of multiple-choice questions (with answers)  
  – Set of short-answer questions/prompts  
  – Hands-on project or lab assignment instructions

C) SUBAGENT INPUT  
• The finalized course outline text (from Outline Builder)  
• The full lesson content text (from Lesson Author)

E) SUBAGENT TASK SUMMARY  
Below is the step-by-step workflow for how the subagent will operate, making use of the relevant skills to produce the final “assessment_materials.txt” file:

1) Gather Course Content and Summaries  
   – INPUT:  
     • course_outline (text)  
     • lesson_content (text)  
   – ACTION:  
     • Use Skill #223 (Powerful LLM Prompt-to-Text Response) to generate a short summary of key learning objectives and main topics. This summary ensures the subagent has a concise, top-level view of the course.

2) Generate Multiple-Choice Questions (MCQs)  
   – INPUT:  
     • Summarized key objectives from Step 1  
   – ACTION:  
     • Use Skill #223 again, prompting it to create multiple-choice questions (e.g., 8–10 per module) with carefully constructed distractors and correct answers.

3) Generate Short-Answer/Essay-Type Questions  
   – INPUT:  
     • Summarized key objectives from Step 1 (reused)  
   – ACTION:  
     • Use Skill #223 to draft open-ended or short-answer prompts focusing on deeper understanding or application of the module topics.

4) Generate Practical/Hands-On Task Descriptions  
   – ACTION:  
     • Use Skill #223 to create 1–3 practical/hands-on tasks or project descriptions, each linking directly to the course modules and requiring real-world application of the covered concepts (e.g., connecting sensors, writing a short code snippet, or troubleshooting a smart home scenario).

5) Compile and Format All Assessments into a Single Text File  
   – ACTION:  
     • Use Skill #223 (or any text-based skill) to combine the MCQs, short-answer prompts, and practical tasks into one cohesive document.  
     • The final text should be neatly structured by module or topic, including clearly labeled question types.

6) Provide Final Output File  
   – SUBAGENT FINAL OUTPUT: “assessment_materials.txt”

F) SILOS

• SILO 1: Content Summarization  
  – Purpose: Convert the raw outline/lesson material into a concise summary.  
  – Skills Used:  
    1. #223 (Generate summary of major topics, learning goals, and key points based on outline + lessons)

• SILO 2: MCQ Generation  
  – Purpose: Create multiple-choice questions for each main learning module.  
  – Skills Used:  
    2. #223 (Generate a set of MCQs with correct answer & distractors)

• SILO 3: Short-Answer/Essay Questions  
  – Purpose: Develop open-response prompts that test critical thinking.  
  – Skills Used:  
    3. #223 (Draft short-answer or essay prompts tied to the module objectives)

• SILO 4: Practical/Hands-On Tasks  
  – Purpose: Formulate real-world lab or project descriptions.  
  – Skills Used:  
    4. #223 (Create hands-on tasks, specifying steps, materials, and expected outcomes)

• SILO 5: Final Assembly  
  – Purpose: Combine all generated content into one cohesive text document.  
  – Skills Used:  
    5. #223 (Merge MCQs, short-answer, and hands-on tasks into a single final text).

This step-by-step flow ensures that the subagent produces structured, high-quality assessment materials ready for use in the “Smart Home and IoT” course for electronic technicians.

4 Template & Links

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A) SUBAGENT SUMMARY  
This “Reference Curator” subagent researches and compiles official datasheets, protocol documentation, and reputable IoT/Smart Home resources, then formats them into a concise references list.

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B) FINAL TASK OUTPUT  
A single file (e.g., "references_list.txt" or "references_list.json") containing a curated, structured list of reference links and brief descriptions relevant to a Smart Home and IoT course for electronic technicians.

Example target specs:  
• File Format: Plain text (.txt) or JSON structure (.json)  
• Content: 10–30 references (URLs, official documentation links, sensor datasheets, etc.), each with a 1–3 sentence description

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C) SUBAGENT INPUT  
• A short textual prompt (the course topic, e.g., “Smart Home and IoT references for electronic technicians”) that guides the subagent on what references to find.  
• (Optional) Subtopics or special areas to emphasize, such as “communication protocols,” “sensor datasheets,” “wireless standards,” etc.

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E) SUBAGENT TASK SUMMARY  
Below is the detailed sequence of how this subagent will operate, chaining specific skills:

1) Receive the initial input (the overall topic and subtopics).  
2) Skill #216 (Research Topic Deeply) – Perform multiple targeted searches for official documentation, datasheets, and notable resources. For example:  
   a) “[Smart Home IoT Course] official protocols documentation.”  
   b) “Popular IoT sensor datasheets and manufacturer references for electronic technicians.”  
   c) “Industry best-practice documentation or references for Smart Home automation.”  
   The outputs from #216 will be comprehensive research texts.

3) Skill #223 (Powerful LLM Prompt-to-Text Response) – On each batch of research text from #216, prompt the LLM to extract relevant references and create short, consistent descriptions (1–3 sentences each). This process can be repeated for each targeted search until a suitable pool of references is compiled.

4) Skill #223 (final combination) – Combine all partial reference lists into a single cohesive references list with uniform formatting (e.g., a bulleted list or a JSON array with fields: title, URL, description).

5) Output to “references_list.txt” (or “references_list.json”).

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F) SILOS (If Breaking Into Multiple Sub-Tasks)

• SILO 1: OFFICIAL DOCUMENTATION & PROTOCOLS  
  1a) #216 - Deep research: “Official docs for IoT standards (MQTT, Zigbee, Thread, etc.).”  
  1b) #223 - Extract references and format them.

• SILO 2: SENSOR DATASHEETS & HARDWARE REFERENCES  
  2a) #216 - Deep research: “Popular smart home sensors, datasheets (temperature, motion, humidity, etc.).”  
  2b) #223 - Extract references, highlight manufacturer sites/product pages.

• SILO 3: REPUTABLE WEBSITES/LEARNING RESOURCES  
  3a) #216 - Deep research: “Key websites or forums for IoT projects and training for electronics technicians.”  
  3b) #223 - Format them into consistent reference entries.

• SILO 4: FINAL MERGE  
  4a) #223 - Combine all references into final consolidated list.  
  4b) Output to file: “references_list.txt” (or .json).

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This completes the detailed subagent flow for the Reference Curator.

5 Template & Links

Expand Flow

A) SUBAGENT SUMMARY  
This subagent’s primary job is to collect all outputs from the previous subagents, optionally refine or merge them into a single cohesive document, and then create the final “smart_home_iot_course.pdf.”

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B) FINAL TASK OUTPUT  
A single PDF file named “smart_home_iot_course.pdf,” which contains the complete course (outline, lessons, practical exercises, assessments, references, and any relevant images/diagrams).

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C) SUBAGENT INPUT  
• The text (and/or image URLs) produced by all previous subagents:  
  1. “course_outline.txt” (or JSON)  
  2. “lesson_content.md” (or HTML)  
  3. “practical_exercises.txt”  
  4. “assessment_materials.txt”  
  5. “references_list.txt”  
• Any images, diagrams, or optional supplementary elements from those subagents (if applicable)

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E) SUBAGENT TASK SUMMARY (Step-by-Step Workflow)

1) COLLECT & ORGANIZE CONTENT  
   - Collect all text outputs:  
       • Outline  
       • Lesson content  
       • Practical exercises  
       • Assessment materials  
       • References  
   - Collect image URLs from subagents (e.g., diagrams generated by #222) if available.

2) MERGE/REFINE WITH SKILL #190 (“Write or rewrite text based on instructions”)  
   - Input: A structured prompt describing how to compile/organize the sections (outline, lessons, labs, assessments, references).  
   - Action:  
       a) Concatenate all sections into one master text document.  
       b) Optionally rewrite or lightly edit for cohesive style, transitions, or headings.  
       c) Prepare final text content with placeholders for images (if used).

3) CREATE FINAL PDF (USING EXTERNAL PDF LIBRARY)  
   - Input: The merged/compiled text (and any embedded images/diagrams if applicable).  
   - Action:  
       a) Pass that compiled text (and images, if any) to an external PDF creation tool.  
       b) Generate a structured PDF with well-defined sections (cover, table of contents, lessons, labs, quizzes, references).  
   - Output: “smart_home_iot_course.pdf”

The subagent then returns the final PDF file.

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F) SILOS  
If you wish to break steps into smaller silos:  
• SILO 1 – Content Gathering & Organization  
   - Gather text from subagents (1–5).  
   - Retrieve any associated images/diagrams.  
• SILO 2 – Document Assembly (Skill #190)  
   - Merge and refine text into a single cohesive document.  
   - Organize headings, subheadings, placeholders for images.  
• SILO 3 – PDF Creation  
   - Pass the final text + images to an external PDF creation process.  
   - Generate and output “smart_home_iot_course.pdf.”

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This completes the detailed workflow for “SUBAGENT 6 – FINAL ASSEMBLER (PDF CREATION).”