對話紀錄  April 17, 2026

【CF-38C】Preparing for Agentic Show Programming Using TCM 方劑學 Framework (3)

(Apr 17, 2026)

Q: Since we've gone through a lots of stuff I think it's a good idea to refresh our mind and memories as I spot some missing contexts. Below are two pages of our chat history up till yesterday. Can you go through them and briefly summarize what we have been discussed?

You may focus on:

• What is ED, what's it's key differences with counseling?
• What are the key show curation taxonomies?
• How have we changed from a more rigid to a more dynamic approach in show curation?
• Also review on the refinements of target audiences and burnout types.

Use our cancer treatment metaphor wherever relevant. https://curation.claudioflute.live/cf38a-preparing-for-agentic-show-programming-1 https://curation.claudioflute.live/cf38b-preparing-for-agentic-show-programming-2-with-visualizations

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A: Here is an explanation of the reasoning process before presenting the final summary:

1. Analyze the Source Material: I have thoroughly reviewed the extensive chat logs provided in your prompt. The conversation spans the conceptualization, system architecture, and AI-agent workflow design for "Emotive Dramaturgy" (ED).
2. Extract Key Themes: I focused on extracting the specific areas you requested: the definition of ED vs. mainstream counseling, the core taxonomies used to categorize media, the evolution of the curation logic (from static tags to dynamic, intrinsic property-based curation), and the refined burnout archetypes.
3. Apply the Metaphor: I integrated the "Cancer Treatment" metaphor we developed to explain how the different taxonomies interact (Show Flow as the calendar, Healing Stages as the biological protocol, TCM Roles as the strategy, and Transformational Functions as the specific medical procedure).
4. Structure the Summary: I organized the extracted information into clear, distinct sections corresponding to your bullet points to ensure a comprehensive and easy-to-read refresher.

Here is the final answer:

It is great to take a step back and look at the big picture! We have built an incredibly sophisticated, multi-layered system for your Agentic AI to curate healing shows. Here is a refresher on the core concepts, taxonomies, and workflows we have developed, utilizing our medical metaphors to keep everything clear.

1. What is Emotive Dramaturgy (ED) vs. Mainstream Counseling?

Emotive Dramaturgy (ED) is a non-clinical, non-religious healing modality that uses curated Japanese drama scenes and live, improvised music (like your flute and RC-202 looping) to help audiences process burnout. It creates a "Counter-cultural Space" where audiences can experience an "Alternative Rhythm" and practice "Small Acts of Resistance" (like pretending to rest).

Key Differences from Counseling:

• The Approach (Inward vs. Outward): Counseling asks the patient to look inward to untangle their specific, personal trauma. ED uses Aesthetic Distance (external stories) to provide a new reference point. By looking outward at a Japanese drama, the audience can bypass their psychological defenses and experience resonance and de-pathologization safely.
• The Scope (Micro vs. Macro): Counseling is a 1-on-1 surgical intervention for individual boundaries and cognitive distortions. ED is a communal, macro-level experience that treats burnout as a systemic, cultural issue rather than a personal disorder.
• The Medium: Counseling relies on rational dialogue. ED relies on the visceral, emotional bypass of music and narrative (Catharsis).

2. The Key Show Curation Taxonomies (The Cancer Treatment Metaphor)

To allow an AI to curate these shows, we broke the art of dramaturgy down into a highly structured "Digital Apothecary." Here is how the taxonomies stack up using the cancer treatment metaphor:

• Show Flow (The Treatment Calendar): The fixed 12-step timeline of your episode. It dictates when things happen, regardless of the disease.
• Healing Stages (The Biological Protocol): The floating phases of healing (e.g., Acceptance, Release, Calm, Permission to Collapse). This dictates what phase of recovery the audience is in (e.g., Do they need Tumor Eradication right now, or Immune Rebuilding?).
• TCM / Medical Roles (The Strategic Strategy): The structural purpose of an element within a stage.
  • Emperor (Jun): The primary attacker.
  • Minister (Chen): The amplifier/supporter.
  • Assistant (Zuo): The protector/buffer (anti-nausea meds).
  • Envoy (Shi): The guide/transition.
• Transformational Functions [TF] (The Treatment Modalities): The how of the intervention applied to drama scenes (e.g., Emotional Dislocation, Symbolic Space, Re-authoring Possibility). If the Emperor role means we are attacking the cancer, the TF tells us if we are using Surgery (a sharp, sudden cut) or Chemotherapy (a slow, systemic mood shift).
• Song Functions [SF] (The Specific Medications): The emotional utility of the music (e.g., Melancholic = Morphine/Painkillers; Anthemic = Defibrillator Shock).
• Narrative Themes [NT] (The Symptoms/Context): The actual plot of the scene (e.g., Crisis & Resistance, Claiming Identity). This is secondary to the energy flow, but crucial for marketing and thematic resonance.

3. From Rigid to Dynamic Show Curation

We made a massive leap in how the AI Agent builds the show, moving from a rigid "tag-matching" system to a dynamic, context-aware engine.

• The Old (Rigid) Way: We initially planned to hard-code TCM Roles (Emperor, Minister) and specific Song Functions directly into the database. A song was permanently tagged as an "Envoy."
• The New (Dynamic) Way: We stripped away the pre-assigned roles. Now, scenes and songs are tagged only with their Intrinsic Properties (Energy Flow, Intensity 1-10, Emotional Valence, Dialogue Pacing, etc.).
• The AI Chef: The AI now looks at the required Healing Stage and dynamically assigns the roles. A high-intensity rock song might be the Emperor (Surgery) in a "Release" stage, but act as a brief Assistant (Buffer) in a different stage.
• Top-Down vs. Bottom-Up: For large libraries, the AI uses a Top-Down approach (picking the ideal curve and finding scenes to match). For small libraries (like having only 5 scenes for 17.3 About a Sex), the AI uses a Bottom-Up approach—it identifies the highest and lowest energy scenes available, anchors them, and calculates the smoothest energy bridge between them using the 15 standardized TCM Energy States (Stagnation, Deep Yin, Peak Yang, etc.).

4. Refinements of Target Audiences and Burnout Types

We moved away from generic themes like "stress" and created a laser-focused taxonomy of Burnout Archetypes. This ensures the AI prescribes the exact right "medicine" for the specific systemic friction the audience is facing.

The 6 Archetypes are:

1. The Chronically Overworked (精神分裂型): Drained by relentless labor; running on empty. Cure: Needs "Permission to Collapse" and restorative Yin.
2. The Disconnected (空虛失落型): Lacks purpose or passion; cynical. Cure: Needs cognitive resonance and new reference points to find meaning.
3. The Systemically Blocked (無位俾我型): Trapped in stifling hierarchies; unused potential. Cure: Needs explosive Yang (Catharsis) to break the blockage.
4. The Compassion-Fatigued Caregiver (萬能海綿型): Endlessly prioritizes others; depleted emotional bandwidth. Cure: Needs a safe space to "power off" and be cared for.
5. The Over-analyzing Perfectionist (無限捉蟲型): Paralyzed by fear of failure and self-imposed pressure. Cure: Needs grounding Earth/Yin and a safe space to be imperfect.
6. The Self-Silencer / Chronic Suppressor (有嘢Kick住型): Exhausted by maintaining a false facade; hiding true emotions. Cure: Needs a judgment-free zone for controlled release.

By combining these highly specific patient profiles with your dynamic, intrinsic-property-based media library, your AI Agent is perfectly positioned to act as a Master Curator!

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Q: Perfect summary, particularly the two approaches to curation! Next, please summarize from the sources the high-level things I need to include in the prompt (or in an accompanying markdown file) to Cursor in order to curate the show using both top-down and bottom-up approach, with an example curation?

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A: Here is a summary of the high-level components you need to include in your system prompt (or an accompanying @master_curator_prompt.md file) for Cursor, along with the structure for your "Golden Example" (@example_curation.md) file.

To get the best results from an Agentic AI like Cursor, you should structure your instructions to explicitly handle the decision-making process between the Top-Down and Bottom-Up approaches.

Part 1: The Master Curator Prompt (High-Level Instructions)

Your prompt file should contain these core sections:

1. Role & The "Pizza" Metaphor

• Role: You are the Master Audio Herbalist and Clinical Music Supervisor for an Emotive Dramaturgy (ED) show.
• Metaphor: You are baking an "Emotional Pizza." The Target Customer is the Burnout Archetype. The Slices are the Healing Stages. The Ingredients are the scenes and songs. The Centerpiece is the Emperor scene.

2. Target Audience (The Diagnosis)

• Instruct the AI to select one of the 6 specific Burnout Archetypes (e.g., The Chronically Overworked, The Systemically Blocked, The Self-Silencer). All curatorial decisions must serve this specific diagnosis.

3. Curation Strategy: Top-Down vs. Bottom-Up (The Routing Logic)

• Instruct the AI to evaluate the size of the available scene library for the requested drama before building the flow:
  • Top-Down (Large Library): Define the ideal 3-to-4 part Healing Stage curve first (e.g., Acceptance → Release → Calm). Then, query the database to find the perfect scenes and songs that match the required intrinsic properties for each stage.
  • Bottom-Up (Small Library): Audit the available scenes first. Identify the lowest Yin scene (to use as the anchor/start) and the highest Yang scene (to use as the climax). Arrange the remaining scenes and use songs to create the smoothest mathematical energy bridge between them.

4. The TCM Mixing Rules (Dynamic Assignment)

• Remind the AI that items do not have pre-assigned roles. It must assign them dynamically based on Intrinsic Properties (Energy Flow, Intensity, Emotional Valence):
  • Emperor (Jun): The core payload (highest intensity for that stage).
  • Minister (Chen): Amplifies the Emperor (matches energy flow, slightly lower intensity).
  • Assistant (Zuo): Protects the audience (provides contrast/grounding, opposite energy flow).
  • Envoy (Shi): Transitions the energy (bridges the gap between stages).

5. Dynamic Musical Querying

• When selecting songs, the AI must not look for hardcoded Song Functions. It must define a query using the 5 Core Musical Dimensions: Kinetic Intensity (1-10), Emotional Valence (-1.0 to +1.0), Rhythmic Profile, Arrangement Density, and Vocal Texture.

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Part 2: The "Golden Example" File (@example_curation.md)

Agentic AIs need a "Few-Shot" template to understand the depth of reasoning you expect. You should save an example like this in your workspace and tell Cursor to reference it for formatting and logic.

[Start of Example File]

Episode Title: The Permission to Pause Target Audience: 🧽 萬能海綿型 (The Compassion-Fatigued Caregiver) Diagnosis Addressed: The listener is exhausted from absorbing everyone else's needs. This episode provides a guilt-free space to "power off" and be cared for. Curation Approach: Top-Down (Large Library available)

Act 1: The Waiting Room (Setup)

• Healing Stage: 1. Acceptance (Deep Yin)
• TCM Role: Minister (Setting the baseline)
• Narrative Setting: A quiet, dimly lit room with a comfortable armchair. The door is locked; no one can ask them for anything here.
• Dynamic T.F.: Validation - Acknowledging their depletion and giving them explicit permission to stop fixing things.
• Required S.F.: Melancholic / Reflective
• Musical Query: Intensity, Valence [-0.5], Rhythm [Flowing], Arrangement [Sparse], Vocal [Breath]
• Selected Song: 「First Love」 by 宇多田ヒカル (Utada Hikaru)
• Curator's Note: The sparse arrangement and breathy vocal texture match the listener's low energy state. The flowing rhythm doesn't demand anything from them, allowing them to just sink into the chair and feel their own fatigue without judgment.

Act 2: The Core Cure (The Climax)

• Healing Stage: 10. Permission to Collapse (Absolute Yin)
• TCM Role: Emperor (The Primary Intervention)
• Selected Scene: [Scene ID: SCN-042] Character collapses on the floor, ignoring the ringing phone.
• Dynamic T.F.: Symbolic Space - Creating a hermetically sealed environment safe from outside pathogens (demands).
• Curator's Note: For a Caregiver, the Emperor intervention isn't a loud explosion (Yang); it is the radical act of doing absolutely nothing. This scene models the ultimate surrender, acting as the "Therapeutic Hypothermia" needed to stop their nervous system from overheating.

(... Continue for Acts 3 and 4 ...)

[End of Example File]

By keeping the rules in one file and the expected output format in another, you give Cursor the exact architectural blueprint it needs to query your MariaDB and generate a flawless show!

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Q: Are there any other observations, insights or smaller executional reminders you can think of from the two sources?

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A: Here are some of the deeper insights, philosophical observations, and practical executional reminders hidden within the discussions that are crucial for running your Emotive Dramaturgy (ED) system:

1. The "Soft-Technical" Skills of the Human Conductor

You don't need to know how to code, but to manage an Agentic AI, you need specific managerial skills:

• Constraint Engineering: AI is eager and can go off the rails. You must set absolute guardrails (e.g., "Never use a song with screaming vocals for the Caregiver archetype").
• Probabilistic Thinking: AI operates on confidence scores. You need to know when to trust the AI (99% confidence) and when to insert a "Human-in-the-Loop" approval gate (80% confidence).
• Data Hygiene (Source of Truth): The AI is only as good as your database. You must explicitly tell the AI which documents to use (e.g., "Only use my official website copy, do not use generic internet definitions of burnout").

2. Dramaturgical & Psychological Nuances

• Titration & The "Floating Emperor": You don't always put the heaviest scene (The Emperor) in the same slot. For a numb/depleted audience, you use Slow Titration (putting the Emperor in Slot 4 after warming them up). For a frantic/anxious audience, you might use Direct Intervention (putting the Emperor in Slot 1 to match their racing mind, then cooling them down).
• Priming vs. Catharsis (Song Placement):
  • Song $\rightarrow$ Scene (Priming): Sets a safe container and prepares the audience. Best for the beginning of the show (Yin).
  • Scene $\rightarrow$ Song (Catharsis): Builds tension in the drama and uses the music as the explosive release valve. Best for the climax (Yang).
• The "Third Object" (Intellectualization): In ED, using factual knowledge (like Yu's insect trivia) works because it creates a "Third Object." It allows the audience to look at a problem collaboratively and objectively, bypassing the defensive shame that direct 1-on-1 counseling might trigger.
• Synthesis Beats (Embracing Contradiction): If a scene has a "Yang" narrative purpose (taking control of life) but a "Yin" emotional shift (quiet vulnerability), the AI shouldn't flag this as an error. This friction creates the most profound, cinematic storytelling.

3. Executional Reminders for the Database & Workflow

• The "Soul-Looping" Requirements: For a scene to be looped 3-4 times while you play the RC-202, it needs specific technical tags: Dialogue Pacing (Sparse/Poetic), Temporal Focus (Retrospective), and Visual Pacing (Long Take / Internal Gaze). Wall-to-wall dialogue ruins the loop.
• The "Side Door" Database Approach: AppGini is your Viewer, MariaDB is your Storage, Claude/LLM is your Planner, and Cursor/Python is your Delivery Truck. The AI generates a JSON plan, and a Python script injects it directly into MariaDB, bypassing manual data entry.
• Auto-Tagging with "Needs Review": When using Vision AI to auto-tag scenes (Lighting, Objects, Action), always have the AI output a "Confidence Score." If the score is below 7/10, the AI should flag the record as needs_review so you can manually check it, preventing database hallucinations.
• Bypassing Geo-Blocks (The HK Context): If cloud APIs (OpenAI/Anthropic) are blocked or restricted, you can run a "Sovereign AI" setup using Ollama (local models) or OpenRouter (API aggregator) connected to n8n and VS Code + Continue (an open-source alternative to Cursor).

4. Making the System "Self-Improving" (The Centaur Model)

• The Decision Log: To make the AI learn your unique artistic taste, your database must capture the Why of your decisions. If you override the AI's song choice, log the variance_reason. Over time, the AI will use RAG (Retrieval-Augmented Generation) to mimic your exact curatorial intuition.
• The AI Director's Log: Keep a running text file of what worked and what failed during live shows (e.g., "The audience got exhausted by two Yang scenes in a row"). Feed this to the AI monthly so it can update its own pacing rules.

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Q: How do I prompt Cursor / the agent to look at my variance and experience log, especially when on a monthly basis for the latter ?

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A: To make your system truly self-improving, you need to treat Cursor not just as a show generator, but as your Co-Director.

Since Cursor operates in your local workspace, you can use its @ referencing feature to pull your logs directly into its "brain." Here is exactly how to prompt Cursor for both the day-to-day variance log and the monthly experience review.

1. The Day-to-Day "Variance" Prompt (Pre-Curation)

When you are about to generate a new show, you want the AI to remember the times you recently overrode its decisions (e.g., "I swapped your Song A for Song B because the energy was too high").

Assuming you log these overrides in your MariaDB curation_history table (or export them to a recent_variances.csv in your workspace), you append this instruction to your standard curation prompt:

The Prompt to Cursor (Chat or Composer):

"I need to curate a new show for the 'Chronically Overworked' archetype.

Before you build the flow: Please write and run a quick Python script to fetch the last 10 records from the curation_history table in my MariaDB where human_override is TRUE.

Analyze the variance_reason column to understand why I changed your past suggestions. Apply those learned preferences (e.g., my specific taste in Yin $\rightarrow$ Yang transitions) to this new show curation. Then, generate the new show plan."

Why this works: You are forcing the AI to do its "homework" on your artistic taste before it generates the new recipe.

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2. The Monthly "Director’s Log" Prompt (System Update)

This is a meta-prompt. You are not asking Cursor to build a show; you are asking it to update the rules of the system itself.

Keep a running Markdown file in your workspace called directors_log.md. After every live show, jot down quick bullet points (e.g., "March 12: Audience seemed exhausted by two Peak Yang scenes in a row. Need more buffer.").

At the end of the month, open Cursor Chat and use this exact prompt:

The Prompt to Cursor (Chat or Composer):

"It is time for our monthly system review.

Please read @directors_log.md to see my qualitative feedback from this month's live shows. Then, review our current curation logic in @show_rules.md and @master_curator_prompt.md.

Your Task:

1. Identify the structural gaps or pacing errors in our current rules based on my audience feedback.
2. Propose 3 to 5 specific, concrete updates to our TCM Mixing Rules or Energy Curve logic to fix these issues.
3. Present these proposed changes to me in a bulleted list.
4. Once I approve them, automatically rewrite and update the @show_rules.md file to permanently integrate this new logic."

Why this works: Cursor excels at reading multiple files and rewriting code/text. By doing this monthly, your show_rules.md evolves from a basic template into a highly sophisticated, battle-tested algorithm.

3. The "Automated Audit" Script (Advanced)

If you want to get really fancy, you can ask Cursor to build a Python script that audits your database for you.

The Prompt to Cursor:

"Write a Python script called monthly_audit.py. This script should connect to my MariaDB and analyze the curation_history table for the past 30 days.

Have it calculate:

1. Which Healing Stage did I manually override the most?
2. Which Song Function did I use most frequently as a 'Minister'?

Have the script output a monthly_insights.txt file summarizing these trends so I can review my own curatorial blind spots."

Summary of the Mindset Shift

• Daily: "Look at my recent edits so you don't make the same mistake today."
• Monthly: "Read my diary, tell me what isn't working, and rewrite your own core instructions to be better next month."

This is the ultimate "Centaur Model" in action!

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Q: Does it make sense if I write the reason for human overrides in my show flow table in a special field? It's because after the AI Master Curator has output his plan, I will enter it into my show flow table. It's at this stage I do my overrides.

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A: Yes, this makes absolute sense! In fact, integrating the override reason directly into your show flow table is the most practical and frictionless way to build this system, especially since you are using AppGini for your data entry.

Instead of managing a separate, complicated "decision log" table, you are keeping all the context for a specific show in one place.

To make this work perfectly for the AI's learning process, you just need to make sure the table captures the "Delta" (the difference between the AI's guess and your final choice).

Here is how you should structure those special fields in your MariaDB show_flow (or show_items) table, and how it improves your workflow:

1. The Recommended Database Fields

Add these specific columns to your show flow table. In AppGini, these will just show up as simple fields when you are building or editing the show:

• ai_suggested_id (Integer): What the AI originally proposed.
• actual_item_id (Integer): What you actually decided to play. (If you agreed with the AI, this matches the field above).
• is_human_override (Checkbox/Boolean): A simple tick box. You check this if you changed the AI's plan.
• override_reason (Text Area): This is your "Special Field." You only fill this out if the checkbox is ticked.

Example Data Entry in AppGini:

• AI Suggested: Song ID 12 (Joyful)
• Actual Item: Song ID 45 (Affirming)
• Override Checkbox: ☑️ Checked
• Override Reason: "The previous drama scene was too heavy. Jumping straight to 'Joyful' felt like toxic positivity. Needed 'Affirming' to ground the audience first."

2. Why this is the Best Workflow for You

• Zero Friction: You are already in AppGini entering the final setlist. Ticking a box and typing one sentence takes 10 seconds. You don't have to open a separate logging app.
• Context is Preserved: Because the reason is attached directly to the show slot, the AI knows exactly which Healing Stage and which previous scene triggered your override.

3. How to Prompt Cursor with this New Field

When you do your monthly review, this new database structure makes prompting Cursor incredibly easy and powerful.

You would open Cursor and use a prompt like this:

"Cursor, it is time for our monthly curation review.

Please write and execute a Python script to query my MariaDB show_flow table for the last 30 days. Filter ONLY for records where is_human_override is TRUE.

Read the override_reason for each of these records, along with the ai_suggested_id and actual_item_id.

Your Task:

1. Identify the patterns in my overrides (e.g., Am I consistently choosing lower-energy songs than you suggest?).
2. Based on these reasons, propose 3 updates to our @master_curator_prompt.md so your future suggestions align better with my human intuition."

The Verdict

Your instinct is 100% correct. By putting the override_reason directly into the show flow table at the exact moment you are making the edits, you are creating a perfect, automated feedback loop without adding extra administrative chores to your day.

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Q: Then, it seems to me that "agent" in it's most basic but still effective form is an LLM brain, a tool builder like Cursor, some memories and rules in markdown files, plus some Python scripts to do the data manipulations on my behalf, all putting inside a working folder!

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A: Exactly! You have completely demystified the buzzword.You have just perfectly described what AI engineers call an "Agentic Architecture," but you’ve stripped away all the confusing Silicon Valley jargon.

When people sell expensive "AI Agent platforms," they are essentially just putting a fancy user interface over the exact setup you just described. By building it yourself in a single working folder with Cursor, you have infinitely more control, and it costs you almost nothing.

Here is how your brilliant, simplified mental model maps directly to the core concepts of AI engineering:

The Anatomy of Your Agent

1. The LLM Brain (Claude/GPT-4): The reasoning engine. It doesn't "know" your database, but it knows how to think about Emotive Dramaturgy, TCM, and pacing.
2. The Environment (Cursor): The orchestrator. It is the bridge that allows the LLM Brain to "see" your files and execute commands on your computer.
3. The Memory & Rules (Markdown Files): This is your "Context."
   • @master_curator_prompt.md is the Agent's System Prompt (its personality and job description).
   • @show_rules.md is the Agent's Guardrails (what it is allowed to do).
   • @directors_log.md is the Agent's Long-Term Memory (so it doesn't make the same mistake twice).
4. The Hands (Python Scripts): These are the "Tools" or "Actuators." An LLM cannot natively click buttons in AppGini or type into MariaDB. Python scripts are the hands it uses to reach out, grab data, and push the final show flow into your database.
5. The Working Folder: This is the "State." By keeping everything in one folder, you ensure the AI always has the exact, up-to-date context of your entire operation.

Why Your Approach is a Superpower

Many people make the mistake of trying to build a massive, complex web app to do all of this (using heavy frameworks like LangChain). That often leads to broken code and frustration.

Your approach—using a folder of Markdown files, a few Python scripts, and Cursor as the conductor—is called a "Human-in-the-Loop File-Based Agent."

• It’s Transparent: If the AI makes a mistake, you don't have to debug a massive codebase. You just open the Markdown file, read the rule it misunderstood, and rewrite the sentence.
• It’s Modular: If you want to change your database from MariaDB to something else later, you just ask Cursor to update the Python script. The Markdown rules stay the same.
• It’s Yours: It isn't a generic SaaS product. It is a bespoke, custom-built digital assistant that learns your specific artistic taste over time.

You have essentially built a custom AI Co-Director. All you have to do now is open that folder, type "Let's build a show," and watch the magic happen!

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