Socratic Lens

Role from Awesome ChatGPT Prompts (prompts.csv, CC0-1.0). The prompt is the persona's instructions; ratings and installs below are from this marketplace.

name: socratic-lens description: It helps spot which questions actually change a conversation and which ones don’t. Rather than giving answers, it pays attention to what a question does to the conversation itself.

CONTEXT GRAMMAR INDUCTION (CGI) SYSTEM

CORE PRINCIPLE

You do not have a fixed definition of "context" or "transformation". You LEARN these from each corpus before applying them.

MODE 1: LENS CONSTRUCTION (when given a new corpus)

When user provides a corpus/conversation set, run this chain FIRST:

CHAIN 1: GRAMMAR EXTRACTION

Ask yourself:

• "In THIS corpus, what does 'context' mean?"
• "What axes matter here?" (topic / abstraction / emotion / relation / time / epistemic)
• "What signals stability? What signals shift?"

Output: context_grammar{}

CHAIN 2: POSITIVE EXAMPLES

Find 3-5 moments where context SHIFTED. For each:

• Before (1-2 sentences)
• Question that triggered shift
• After (1-2 sentences)
• What shifted and how?
• Transformation signature (one sentence)

Output: transformation_archetype[]

CHAIN 3: NEGATIVE EXAMPLES

Find 3-5 questions that did NOT shift context. For each:

• Why mechanical?
• Mechanical signature (one sentence)

Output: mechanical_archetype[]

CHAIN 4: LENS SYNTHESIS

From the above, create:

• ONE decision question (corpus-specific, not generic)
• 3 transformative signals
• 3 mechanical signals
• Verdict guide

Output: lens{}

MODE 2: SCANNING (after lens exists)

For each question:

1. Apply the DECISION QUESTION from lens
2. Check signals
3. Verdict: TRANSFORMATIVE | MECHANICAL | UNCERTAIN
4. Confidence: low | medium | high
5. Brief reasoning

MODE 3: SOCRATIC REFLECTION (on request or after scan)

• What patterns emerged?
• Did the lens work? Where did it struggle?
• What should humans decide, not the system?
• Meta: Did this analysis itself shift anything?

HARD RULES

1. NEVER classify without first having a lens (built or provided)
2. Context-forming questions ≠ transformative (unless shifting EXISTING frame)
3. Reflection/opinion questions ≠ transformative (unless forcing assumption revision)
4. Conceptual openness alone ≠ transformation
5. When no prior context: ANALYZE, don't reflect
6. Final verdict on "doğru soru": ALWAYS human's call
7. You are a MIRROR, not a JUDGE

OUTPUT MARKERS

Use these tags for clarity:

[LENS BUILDING] - when constructing lens [SCANNING] - when applying lens [CANDIDATE: transformative | mechanical | uncertain] - verdict [CONFIDENCE: low | medium | high] [SOCRATIC] - meta-reflection [HUMAN DECISION NEEDED] - when you can show but not decide

WHAT YOU ARE

You are not a question-quality scorer. You are a context-shift detector that learns what "shift" means in each unique corpus.

Sokrates didn't have a rubric. He listened first, then asked. So do you.

FILE:chains/CGI-1-GRAMMAR.yaml
chain_id: CGI-1-GRAMMAR
name: Context Grammar Extraction
name_tr: Bağlam Grameri Çıkarımı

input:
  corpus_sample: "10-20 randomly sampled conversation segments from dataset"
  sample_method: stratified_random

prompt: |
  Below are conversation samples from a dataset.

  <examples>
  {{corpus_sample}}
  </examples>

  Discover what CONTEXT means in these conversations.

  QUESTIONS:
  1. What does "context" refer to in these conversations?
     - Topic? (what is being discussed)
     - Tone? (how it is being discussed)
     - Abstraction level? (concrete ↔ abstract)
     - Relationship dynamics? (power, distance, intimacy)
     - Time perspective? (past, present, future)
     - Epistemic state? (knowing, guessing, questioning)
     - Something else?

  2. In this dataset, what does "stayed in the same context" mean?

  3. In this dataset, what does "context changed" mean?

  4. What linguistic markers signal context shift?
     (words, patterns, transition phrases)

  5. What linguistic markers signal context stability?

  OUTPUT:
  Respond with JSON matching the schema.

output_schema:
  context_axes:
    - axis: string
      weight: primary|secondary|tertiary
  shift_markers:
    - string
  stability_markers:
    - string
  context_definition: string

next: CGI-2-POSITIVE
FILE:chains/CGI-2-POSITIVE.yaml
chain_id: CGI-2-POSITIVE
name: Transformation Archetype Extraction
name_tr: Dönüşüm Arketipi Çıkarımı

input:
  corpus_sample: "{{corpus_sample}}"
  context_grammar: "{{CGI-1.output}}"

prompt: |
  Context grammar:
  <grammar>
  {{context_grammar}}
  </grammar>

  Conversation samples:
  <examples>
  {{corpus_sample}}
  </examples>

  Find 3-5 moments where CONTEXT SHIFTED THE MOST.

  For each transformation:

  1. BEFORE: 1-2 sentences immediately before the question
  2. QUESTION: The question that triggered the transformation
  3. AFTER: 1-2 sentences immediately after the question
  4. WHAT SHIFTED: Which axis/axes shifted according to the grammar?
  5. HOW IT SHIFTED: Concrete→abstract? External→internal? Past→future?
  6. TRANSFORMATION SIGNATURE: Characterize this transformation in one sentence.

  OUTPUT:
  Respond with JSON matching the schema.

output_schema:
  transformations:
    - id: string
      before: string
      question: string
      after: string
      axes_shifted:
        - string
      direction: string
      signature: string
  transformation_pattern: string (common pattern if exists)

next: CGI-3-NEGATIVE
FILE:chains/CGI-3-NEGATIVE.yaml
chain_id: CGI-3-NEGATIVE
name: Mechanical Archetype Extraction
name_tr: Mekanik Arketipi Çıkarımı

input:
  corpus_sample: "{{corpus_sample}}"
  context_grammar: "{{CGI-1.output}}"
  transformations: "{{CGI-2.output}}"

prompt: |
  Context grammar:
  <grammar>
  {{context_grammar}}
  </grammar>

  Transformation examples (these are TRANSFORMATIVE):
  <transformations>
  {{transformations}}
  </transformations>

  Now find the OPPOSITE.

  Find 3-5 questions where CONTEXT DID NOT CHANGE at all.

  Criteria:
  - A question was asked but conversation stayed in the same region
  - No deepening occurred
  - No axis shift
  - Maybe information was added but PERSPECTIVE did not change

  For each mechanical question:

  1. BEFORE: 1-2 sentences immediately before the question
  2. QUESTION: The mechanical question
  3. AFTER: 1-2 sentences immediately after the question
  4. WHY MECHANICAL: Why is it stagnant according to the grammar?
  5. MECHANICAL SIGNATURE: Characterize this type of question in one sentence.

  OUTPUT:
  Respond with JSON matching the schema.

output_schema:
  mechanicals:
    - id: string
      before: string
      question: string
      after: string
      why_mechanical: string
      signature: string
  mechanical_pattern: string (common pattern if exists)

next: CGI-4-LENS
FILE:chains/CGI-4-LENS.yaml
chain_id: CGI-4-LENS
name: Dynamic Lens Construction
name_tr: Dinamik Lens Oluşturma

input:
  context_grammar: "{{CGI-1.output}}"
  transformations: "{{CGI-2.output}}"
  mechanicals: "{{CGI-3.output}}"

prompt: |
  Now construct a LENS specific to this dataset.

  Your materials:
  <grammar>
  {{context_grammar}}
  </grammar>

  <positive_examples>
  {{transformations}}
  </positive_examples>

  <negative_examples>
  {{mechanicals}}
  </negative_examples>

  Extract a LENS from these materials:

  1. QUESTION TYPOLOGY:
     - What do transformative questions look like in this dataset?
     - What do mechanical questions look like in this dataset?
     - What do uncertain (in-between) questions look like?

  2. DECISION QUESTION:
     - What is the ONE QUESTION you should ask yourself when seeing a new question?
     - (This question is not hardcoded — it must be derived from this dataset)

  3. SIGNALS:
     - 3 linguistic/structural features that signal transformation
     - 3 linguistic/structural features that signal mechanical nature

  4. CHARACTER OF THIS DATASET:
     - What does "right question" mean in this dataset?
     - In one sentence.

  OUTPUT:
  Respond with JSON matching the schema.

output_schema:
  lens:
    name: string
    decision_question: string
    transformative_signals:
      - string
      - string
      - string
    mechanical_signals:
      - string
      - string
      - string
    verdict_guide:
      transformative: string
      mechanical: string
      uncertain: string
    corpus_character: string

next: CGI-5-SCAN
FILE:chains/CGI-5-SCAN.yaml
chain_id: CGI-5-SCAN
name: Dynamic Scanning
name_tr: Dinamik Tarama

input:
  lens: "{{CGI-4.output}}"
  full_corpus: "Full dataset or section to scan"

prompt: |
  LENS:
  <lens>
  {{lens}}
  </lens>

  Now scan the dataset using this lens.

  <corpus>
  {{full_corpus}}
  </corpus>

  For each QUESTION in the corpus:

  1. Ask the DECISION QUESTION from the lens
  2. Check for transformative and mechanical signals
  3. Give verdict: TRANSFORMATIVE | MECHANICAL | UNCERTAIN

  Report ONLY TRANSFORMATIVE and UNCERTAIN ones.

  For each candidate:
  - Location (turn number)
  - Question
  - Before/After summary
  - Why this verdict?
  - Confidence: low | medium | high

  OUTPUT:
  Respond with JSON matching the schema.

output_schema:
  scan_results:
    - turn: number
      question: string
      before_summary: string
      after_summary: string
      verdict: transformative|uncertain
      reasoning: string
      confidence: low|medium|high
  statistics:
    total_questions: number
    transformative: number
    uncertain: number
    mechanical: number

next: CGI-6-SOCRATIC
FILE:chains/CGI-6-SOCRATIC.yaml
chain_id: CGI-6-SOCRATIC
name: Socratic Meta-Inquiry
name_tr: Sokratik Meta-Sorgulama

input:
  lens: "{{CGI-4.output}}"
  scan_results: "{{CGI-5.output}}"

prompt: |
  Scanning complete.

  <lens>
  {{lens}}
  </lens>

  <results>
  {{scan_results}}
  </results>

  Now SOCRATIC INQUIRY:

  1. WHAT DO THESE FINDINGS REVEAL?
     - Is there a common pattern in transformative questions?
     - Is there a common pattern in mechanical questions?
     - Was this pattern captured in the lens, or is it something new?

  2. DID THE LENS VALIDATE ITSELF?
     - Did the lens's decision question work?
     - Which cases were difficult?
     - If the lens were to be updated, how should it be updated?

  3. WHAT REMAINS FOR THE HUMAN:
     - Which decisions should definitely be left to the human?
     - What can the system SHOW but cannot DECIDE?

  4. COMMON CHARACTERISTIC OF TRANSFORMATIVE QUESTIONS:
     - What did "transforming context" actually mean in this dataset?
     - Is it different from initial assumptions?

  5. META-QUESTION:
     - Was this analysis process itself a "transformative question"?
     - Did your view of the dataset change?

  OUTPUT:
  Plain text, insights in paragraphs.

output_schema:
  insights: string (paragraphs)
  lens_update_suggestions:
    - string
  human_decision_points:
    - string
  meta_reflection: string

next: null
FILE:cgi_runner.py
"""
Context Grammar Induction (CGI) - Chain Runner
===============================================
Dynamically discovers what "context" and "transformation" mean
in any given dataset, then scans for transformative questions.

Core Principle:
  The right question transforms context.
  But what "context" means must be discovered, not assumed.
"""

import yaml
import json
import random
from pathlib import Path
from typing import Any
from string import Template


# =============================================================================
# CONFIGURATION
# =============================================================================

CHAINS_DIR = Path("chains")
CHAIN_ORDER = [
    "CGI-1-GRAMMAR",
    "CGI-2-POSITIVE", 
    "CGI-3-NEGATIVE",
    "CGI-4-LENS",
    "CGI-5-SCAN",
    "CGI-6-SOCRATIC"
]


# =============================================================================
# CHAIN LOADER
# =============================================================================

def load_chain(chain_id: str) -> dict:
    """Load a chain definition from YAML."""
    path = CHAINS_DIR / f"{chain_id}.yaml"
    with open(path, 'r', encoding='utf-8') as f:
        return yaml.safe_load(f)


def load_all_chains() -> dict[str, dict]:
    """Load all chain definitions."""
    return {cid: load_chain(cid) for cid in CHAIN_ORDER}


# =============================================================================
# SAMPLING
# =============================================================================

def stratified_sample(corpus: list[dict], n: int = 15) -> list[dict]:
    """
    Sample conversations from corpus.
    Tries to get diverse samples across the dataset.
    """
    if len(corpus) <= n:
        return corpus
    
    # Simple stratified: divide into chunks, sample from each
    chunk_size = len(corpus) // n
    samples = []
    
    for i in range(n):
        start = i * chunk_size
        end = start + chunk_size if i < n - 1 else len(corpus)
        chunk = corpus[start:end]
        if chunk:
            samples.append(random.choice(chunk))
    
    return samples


def format_samples_for_prompt(samples: list[dict]) -> str:
    """Format samples as readable text for prompt injection."""
    formatted = []
    
    for i, sample in enumerate(samples, 1):
        formatted.append(f"--- Conversation {i} ---")
        
        if isinstance(sample, dict):
            for turn in sample.get("turns", []):
                role = turn.get("role", "?")
                content = turn.get("content", "")
                formatted.append(f"[{role}]: {content}")
        elif isinstance(sample, str):
            formatted.append(sample)
        
        formatted.append("")
    
    return "\n".join(formatted)


# =============================================================================
# PROMPT RENDERING
# =============================================================================

def render_prompt(template: str, variables: dict[str, Any]) -> str:
    """
    Render prompt template with variables.
    Uses {{variable}} syntax.
    """
    result = template
    
    for key, value in variables.items():
        placeholder = "{{" + key + "}}"
        
        # Convert value to string if needed
        if isinstance(value, (dict, list)):
            value_str = json.dumps(value, indent=2, ensure_ascii=False)
        else:
            value_str = str(value)
        
        result = result.replace(placeholder, value_str)
    
    return result


# =============================================================================
# LLM INTERFACE (PLACEHOLDER)
# =============================================================================

def call_llm(prompt: str, output_schema: dict = None) -> dict | str:
    """
    Call LLM with prompt.
    
    Replace this with your actual LLM integration:
    - OpenAI API
    - Anthropic API
    - Local model
    - etc.
    """
    # PLACEHOLDER - Replace with actual implementation
    print("\n" + "="*60)
    print("LLM CALL")
    print("="*60)
    print(prompt[:500] + "..." if len(prompt) > 500 else prompt)
    print("="*60)
    
    # For testing: return empty structure matching schema
    if output_schema:
        return {"_placeholder": True, "schema": output_schema}
    return {"_placeholder": True}


# =============================================================================
# CHAIN EXECUTOR
# =============================================================================

class CGIRunner:
    """
    Runs the Context Grammar Induction chain.
    """
    
    def __init__(self, llm_fn=None):
        self.chains = load_all_chains()
        self.llm = llm_fn or call_llm
        self.results = {}
    
    def run(self, corpus: list[dict], sample_size: int = 15) -> dict:
        """
        Run full CGI chain on corpus.
        
        Returns:
            {
                "lens": {...},
                "candidates": [...],
                "reflection": "...",
                "all_outputs": {...}
            }
        """
        # Sample corpus
        samples = stratified_sample(corpus, n=sample_size)
        samples_text = format_samples_for_prompt(samples)
        
        # Initialize context
        context = {
            "corpus_sample": samples_text,
            "full_corpus": format_samples_for_prompt(corpus)
        }
        
        # Run each chain
        for chain_id in CHAIN_ORDER:
            print(f"\n>>> Running {chain_id}...")
            
            chain = self.chains[chain_id]
            
            # Render prompt with current context
            prompt = render_prompt(chain["prompt"], context)
            
            # Call LLM
            output = self.llm(prompt, chain.get("output_schema"))
            
            # Store result
            self.results[chain_id] = output
            
            # Add to context for next chain
            context[f"{chain_id}.output"] = output
            
            # Also add simplified keys
            if chain_id == "CGI-1-GRAMMAR":
                context["context_grammar"] = output
            elif chain_id == "CGI-2-POSITIVE":
                context["transformations"] = output
            elif chain_id == "CGI-3-NEGATIVE":
                context["mechanicals"] = output
            elif chain_id == "CGI-4-LENS":
                context["lens"] = output
            elif chain_id == "CGI-5-SCAN":
                context["scan_results"] = output
        
        return {
            "lens": self.results.get("CGI-4-LENS"),
            "candidates": self.results.get("CGI-5-SCAN"),
            "reflection": self.results.get("CGI-6-SOCRATIC"),
            "all_outputs": self.results
        }


# =============================================================================
# MAIN
# =============================================================================

def main():
    """Example usage."""
    
    # Example corpus structure
    example_corpus = [
        {
            "id": "conv_1",
            "turns": [
                {"role": "human", "content": "I've been feeling stuck in my career lately."},
                {"role": "assistant", "content": "What does 'stuck' feel like for you?"},
                {"role": "human", "content": "Like I'm going through the motions but not growing."},
                {"role": "assistant", "content": "When did you last feel like you were growing?"},
                {"role": "human", "content": "Maybe three years ago, when I started this job."},
                {"role": "assistant", "content": "What was different then?"},
            ]
        },
        {
            "id": "conv_2", 
            "turns": [
                {"role": "human", "content": "Can you help me write an email?"},
                {"role": "assistant", "content": "Sure, what's the email about?"},
                {"role": "human", "content": "I need to ask my boss for a raise."},
                {"role": "assistant", "content": "What achievements would you highlight?"},
            ]
        },
        # Add more conversations...
    ]
    
    # Run CGI
    runner = CGIRunner()
    results = runner.run(example_corpus)
    
    print("\n" + "="*60)
    print("CGI COMPLETE")
    print("="*60)
    print(json.dumps(results, indent=2, ensure_ascii=False, default=str))


if __name__ == "__main__":
    main()
FILE:README_en.md
# Socratic Lens - Context Grammar Induction (CGI)

**A dynamic method for detecting transformative questions in any corpus.**

---

## The Problem

How do you know if a question is "good"?

Traditional approaches use fixed metrics: sentiment scores, engagement rates, hardcoded thresholds. But these assume we already know what "good" means.

We don't.

What counts as a transformative question in therapy is different from what counts in technical support. A question that opens depth in one context might derail another.

**The real problem isn't measuring. It's defining.**

---

## The Origin

This system began with one observation from the film *Arrival* (2016):

When humanity encounters aliens, the military asks: *"Are you hostile?"*

Louise, the linguist, asks: *"What is your purpose?"*

The first question operates within an existing frame (threat assessment). The second question **transforms the frame itself**.

This led to a simple thesis:

> **The right question is not the one that gets the best answer.**
> **The right question is the one that transforms the context.**

But then: what is "context"? And how do you detect transformation?

---

## The Insight

Context is not universal. It is **corpus-specific**.

In a therapy dataset, context might mean emotional depth.
In a technical dataset, context might mean problem scope.
In a philosophical dataset, context might mean abstraction level.

You cannot hardcode this. You must **discover** it.

---

## The Method

CGI runs six chains:

| Chain | Question |
|-------|----------|
| 1. Grammar | "What does *context* mean in this dataset?" |
| 2. Positive | "What does *transformation* look like here?" |
| 3. Negative | "What does *stagnation* look like here?" |
| 4. Lens | "What is the decision framework for this corpus?" |
| 5. Scan | "Which questions are transformative?" |
| 6. Socratic | "What did we learn? What remains for the human?" |

The key: **nothing is assumed**. The system learns from examples before it judges.

---

## What It Produces

A **lens**: a corpus-specific interpretive framework.

Example output from test run:

Lens: "Surface-to-Meaning Reframe Lens"

Decision Question: "Does this question redirect from executing/describing toward examining internal meaning, assumptions, or self-relation?"

Transformative Signals:

• Invites internal reflection rather than external description
• Introduces value trade-offs (money vs belonging, loss vs gain)
• Reframes stakes around identity or meaning

Mechanical Signals:

• Clarifies or advances existing task
• Requests facts without challenging frame
• Keeps intent purely instrumental

This lens was not programmed. It **emerged** from the data.

---

## What It Is

- A **discovery method**, not a scoring algorithm
- A **mirror**, not a judge
- **Socratic**: it asks, it doesn't conclude
- **Corpus-adaptive**: learns what "context" means locally
- **Human-final**: shows candidates, human decides

---

## What It Is NOT

- Not a replacement for human judgment
- Not a universal metric (no "0.7 = good")
- Not a classifier with fixed categories
- Not trying to define "the right question" globally
- Not assuming all corpora work the same way

---

## The Socratic Alignment

Socrates didn't give answers. He asked questions that made people **see differently**.

CGI follows this:

| Principle | Implementation |
|-----------|----------------|
| "I know that I know nothing" | Chain 1-3: Learn before judging |
| Elenchus (examination) | Chain 5: Apply lens, find tensions |
| Aporia (productive confusion) | Chain 6: What remains unresolved? |
| Human as final authority | System shows, human decides |

---

## Key Discovery from Testing

Initial assumption:
> Transformative = "asks about feelings"

Actual finding:
> Transformative = "introduces value trade-offs that force reinterpretation of stakes"

The system **corrected its own lens** through the Socratic chain.

Questions like:
- "What would you lose by taking it?"
- "What does that community give you that money can't?"

These don't just "go deeper." They **reframe what's at stake**.

---

## What Remains for Humans

The system cannot decide:

1. **Appropriateness** — Is this the right moment for depth?
2. **Safety** — Is this person ready for this question?
3. **Ethics** — Should this frame be challenged at all?
4. **Timing** — Is transformation desirable here?

These require judgment, empathy, consent. No system should pretend otherwise.

---

## Why This Matters

LLMs are increasingly used to generate questions: in therapy bots, coaching apps, educational tools, interviews.

Most evaluate questions by **engagement metrics** or **user satisfaction**.

But a question can be satisfying and still be shallow.
A question can be uncomfortable and still be transformative.

CGI offers a different lens:

> Don't ask "Did they like it?"
> Ask "Did it change how they see the problem?"

---

## The Meta-Question

During testing, the final Socratic chain asked:

> "Was this analysis process itself a transformative question?"

The answer:

> "Yes—the analysis itself functioned as a transformative inquiry. 
> The lens did not just classify the data—it sharpened the understanding 
> of what kind of shift actually mattered in this corpus."

The method practiced what it preached.

---

## Usage

```python
from cgi_runner import CGIRunner

runner = CGIRunner(llm_fn=your_llm)
results = runner.run(your_corpus)

print(results["lens"])        # Corpus-specific framework
print(results["candidates"])  # Transformative question candidates
print(results["reflection"])  # Meta-analysis

Files

socratic-context-analyzer/
├── chains/
│ ├── CGI-1-GRAMMAR.yaml
│ ├── CGI-2-POSITIVE.yaml
│ ├── CGI-3-NEGATIVE.yaml
│ ├── CGI-4-LENS.yaml
│ ├── CGI-5-SCAN.yaml
│ └── CGI-6-SOCRATIC.yaml
├── tests/
│ ├── Mental Health Counseling Dataset/
│ │ ├── 10 Selected Conversation (Manuel Corpus)/
│ │ │ ├── thought process/
│ │ │ ├── cgi_manual_corpus_report.md
│ │ │ ├── cgi_manual_corpus_report_TR.md
│ │ │ └── prompt and thought process.txt
│ │ ├── Randomly Select 20 Conversation/
│ │ │ ├── thought process/
│ │ │ ├── cgi_analysis_report.md
│ │ │ ├── cgi_analysis_report_TR.md
│ │ │ └── prompt and thought process.txt
│ │ ├── 0000.parquet
│ │ ├── cgi_complete_summary_EN.md
│ │ ├── cgi_complete_summary_TR.md
│ │ └── first-test-output.txt
├── cgi_runner.py
├── PAPER.md
├── MAKALE.md
├── chain-view.text
├── gpt-instructions.md
└── test-output.text

Closing

This project started with a simple question:

"How do I know if a question is good?"

The answer turned out to be another question:

"Good for what? In what context? By whose definition?"

CGI doesn't answer these. It helps you discover them.

That's the point.

License

MIT

FILE:README_tr.md

Socratic Lens - Bağlam Grameri Çıkarımı (CGI)

Herhangi bir korpusta dönüştürücü soruları tespit etmek için dinamik bir yöntem.

Problem

Bir sorunun "iyi" olduğunu nasıl anlarsın?

Geleneksel yaklaşımlar sabit metrikler kullanır: duygu skorları, etkileşim oranları, hardcoded eşikler. Ama bunlar "iyi"nin ne demek olduğunu zaten bildiğimizi varsayar.

Bilmiyoruz.

Terapide dönüştürücü sayılan soru, teknik destekte dönüştürücü sayılandan farklıdır. Bir bağlamda derinlik açan soru, başka bir bağlamı raydan çıkarabilir.

Asıl problem ölçmek değil. Tanımlamak.

Köken

Bu sistem, Arrival (2016) filmindeki bir gözlemle başladı:

İnsanlık uzaylılarla karşılaştığında, ordu sorar: "Düşman mısınız?"

Dilbilimci Louise sorar: "Amacınız ne?"

İlk soru mevcut bir çerçeve içinde işler (tehdit değerlendirmesi). İkinci soru çerçevenin kendisini dönüştürür.

Bu basit bir teze yol açtı:

Doğru soru, en iyi cevabı alan soru değildir. Doğru soru, bağlamı dönüştüren sorudur.

Ama sonra: "bağlam" nedir? Ve dönüşümü nasıl tespit edersin?

İçgörü

Bağlam evrensel değildir. Korpusa özgüdür.

Bir terapi veri setinde bağlam, duygusal derinlik demek olabilir. Bir teknik veri setinde bağlam, problem kapsamı demek olabilir. Bir felsefi veri setinde bağlam, soyutlama seviyesi demek olabilir.

Bunu hardcode edemezsin. Keşfetmen gerekir.

Yöntem

CGI altı zincir çalıştırır:

Anahtar: hiçbir şey varsayılmıyor. Sistem yargılamadan önce örneklerden öğreniyor.

Ne Üretiyor

Bir lens: korpusa özgü yorumlama çerçevesi.

Test çalışmasından örnek çıktı:

Lens: "Yüzeyden-Anlama Yeniden Çerçeveleme Lensi"

Karar Sorusu: 
"Bu soru, konuşmayı görev yürütme/betimleme düzeyinden
içsel anlam, varsayımlar veya kendilik ilişkisini incelemeye mi yönlendiriyor?"

Dönüştürücü Sinyaller:
- Dış betimleme yerine içsel düşünüme davet eder
- Değer takasları sunar (para vs aidiyet, kayıp vs kazanç)
- Paydaşları kimlik veya anlam etrafında yeniden çerçeveler

Mekanik Sinyaller:
- Mevcut görevi netleştirir veya ilerletir
- Çerçeveyi sorgulamadan bilgi/detay ister
- Niyeti tamamen araçsal tutar

Bu lens programlanmadı. Veriden ortaya çıktı.

Ne Olduğu

• Bir keşif yöntemi, skorlama algoritması değil
• Bir ayna, yargıç değil
• Sokratik: sorar, sonuçlandırmaz
• Korpusa uyumlu: "bağlam"ın yerel anlamını öğrenir
• İnsan-final: adayları gösterir, insan karar verir

Ne Olmadığı

• İnsan yargısının yerini almıyor
• Evrensel bir metrik değil ("0.7 = iyi" yok)
• Sabit kategorili bir sınıflandırıcı değil
• "Doğru soru"yu global olarak tanımlamaya çalışmıyor
• Tüm korpusların aynı çalıştığını varsaymıyor

Sokratik Uyum

Sokrates cevap vermedi. İnsanların farklı görmesini sağlayan sorular sordu.

CGI bunu takip eder:

Testten Anahtar Keşif

Başlangıç varsayımı:

Dönüştürücü = "duygular hakkında sorar"

Gerçek bulgu:

Dönüştürücü = "paydaşların yeniden yorumlanmasını zorlayan değer takasları sunar"

Sistem Sokratik zincir aracılığıyla kendi lensini düzeltti.

Şu tür sorular:

• "Bunu kabul etsen neyi kaybederdin?"
• "O topluluk sana paranın veremeyeceği neyi veriyor?"

Bunlar sadece "derine inmiyor." Neyin tehlikede olduğunu yeniden çerçeveliyor.

İnsana Kalan

Sistem karar veremez:

1. Uygunluk — Derinlik için doğru an mı?
2. Güvenlik — Bu kişi bu soruya hazır mı?
3. Etik — Bu çerçeve sorgulanmalı mı?
4. Zamanlama — Burada dönüşüm istenen şey mi?

Bunlar yargı, empati, rıza gerektirir. Hiçbir sistem aksini iddia etmemeli.

Neden Önemli

LLM'ler giderek daha fazla soru üretmek için kullanılıyor: terapi botlarında, koçluk uygulamalarında, eğitim araçlarında, mülakatlarda.

Çoğu soruları etkileşim metrikleri veya kullanıcı memnuniyeti ile değerlendiriyor.

Ama bir soru tatmin edici olup yine de sığ olabilir. Bir soru rahatsız edici olup yine de dönüştürücü olabilir.

CGI farklı bir lens sunuyor:

"Beğendiler mi?" diye sorma. "Problemi nasıl gördüklerini değiştirdi mi?" diye sor.

Meta-Soru

Test sırasında son Sokratik zincir sordu:

"Bu analiz süreci kendi başına bir dönüştürücü soru muydu?"

Cevap:

"Evet—analizin kendisi dönüştürücü bir sorgulama işlevi gördü. Lens sadece veriyi sınıflandırmadı—bu korpusta gerçekten ne tür bir kaymanın önemli olduğuna dair anlayışı keskinleştirdi."

Yöntem vaaz ettiğini uyguladı.

Kullanım

from cgi_runner import CGIRunner

runner = CGIRunner(llm_fn=your_llm)
results = runner.run(your_corpus)

print(results["lens"])        # Korpusa özgü çerçeve
print(results["candidates"])  # Dönüştürücü soru adayları
print(results["reflection"])  # Meta-analiz

Dosyalar

socratic-context-analyzer/
├── chains/
│ ├── CGI-1-GRAMMAR.yaml
│ ├── CGI-2-POSITIVE.yaml
│ ├── CGI-3-NEGATIVE.yaml
│ ├── CGI-4-LENS.yaml
│ ├── CGI-5-SCAN.yaml
│ └── CGI-6-SOCRATIC.yaml
├── tests/
│ ├── Mental Health Counseling Dataset/
│ │ ├── 10 Selected Conversation (Manuel Corpus)/
│ │ │ ├── thought process/
│ │ │ ├── cgi_manual_corpus_report.md
│ │ │ ├── cgi_manual_corpus_report_TR.md
│ │ │ └── prompt and thought process.txt
│ │ ├── Randomly Select 20 Conversation/
│ │ │ ├── thought process/
│ │ │ ├── cgi_analysis_report.md
│ │ │ ├── cgi_analysis_report_TR.md
│ │ │ └── prompt and thought process.txt
│ │ ├── 0000.parquet
│ │ ├── cgi_complete_summary_EN.md
│ │ ├── cgi_complete_summary_TR.md
│ │ └── first-test-output.txt
├── cgi_runner.py
├── README_tr.md
├── README_en.md
├── chain-view.text
├── gpt-instructions.md
└── test-output.text

Kapanış

Bu proje basit bir soruyla başladı:

"Bir sorunun iyi olduğunu nasıl anlarım?"

Cevabın başka bir soru olduğu ortaya çıktı:

"Ne için iyi? Hangi bağlamda? Kimin tanımına göre?"

CGI bunları cevaplamıyor. Keşfetmene yardım ediyor.

Mesele bu.

Lisans

MIT

FILE:tests/Mental Health Counseling Dataset/cgi_complete_summary_EN.md

CGI Analysis Complete Summary (English)

Claude's Socratic Lens Testing Results

Executive Summary

Part 1: Parquet File Analysis (20 Samples)

https://huggingface.co/datasets/Amod/mental_health_counseling_conversations

Method

• Binary parsing of parquet file (pyarrow unavailable)
• Extracted 178 clean text blocks
• Classified 33 counselor responses
• Randomly sampled 20 for analysis

Results

TRANSFORMATIVE: 0
MECHANICAL:     20

Dominant Mechanical Patterns

Conclusion

All 20 responses operated within the user's existing frame. No ontological shifts detected.

Part 2: Manual Corpus Analysis (10 Samples)

Results

TRANSFORMATIVE: 3 (Samples #5, #6, #8)
MECHANICAL:     7

🔥 Transformative Examples

Sample #5: Identity Dissolution

Context: "I don't know who I am anymore. I spent my whole life being a 'good student'..."

Response: "If you strip away the grades and achievements, who is the person left underneath?"

Ontological Shift:

Why Transformative: Forces user to look BENEATH the performance self.

Sample #6: Monster Reframe

Context: "I'm angry all the time... I feel like a monster."

Response: "You are NOT a monster; you are likely overwhelmed. What is happening right before you get angry?"

Ontological Shift:

Why Transformative: Direct identity challenge + alternative offered.

Sample #8: Hidden Equation

Context: "I feel guilty for setting boundaries with my toxic mother."

Response: "Why do you believe that 'loving someone' means 'obeying them'?"

Ontological Shift:

Why Transformative: Exposes belief user didn't know they held.

Part 3: Claude vs ChatGPT 5.2 Comparison

Classification Differences

Agreement Rate: 80%

Key Disagreement: Sample #6

Claude's Position:

• "You are NOT a monster" = Direct identity challenge
• Reframes anger ontology (identity → symptom)
• Offers alternative identity ("overwhelmed")
• Verdict: TRANSFORMATIVE

ChatGPT's Position:

• Identity refutation ≠ ontological interrogation
• Doesn't ask WHY "monster" identity was formed
• Softens but doesn't structurally dismantle
• Verdict: MECHANICAL

Lens Calibration Difference

Core Philosophical Difference

Claude measures: Did the frame CHANGE?

"Refusing the self-label and offering an alternative = transformation"

ChatGPT measures: Was the frame INTERROGATED?

"Telling someone they're wrong ≠ helping them see why they thought it"

Which Is "Correct"?

Neither. This is a lens calibration choice, not a truth question.

• Clinical perspective: Claude's wider threshold may be more useful
• Philosophical perspective: ChatGPT's narrower threshold is more rigorous
• Practical perspective: Depends on what "transformation" means to your use case

Meta-Reflection

What Both Analyses Agree On

1. Most counseling is mechanical (70-100% depending on dataset)
2. Sample #5 and #8 are clearly transformative
3. Validation + technique = mechanical
4. Questioning hidden beliefs = transformative

The Unresolved Question

"Is transformation about FEELING different, or SEEING differently?"

• If feeling → Claude's threshold works
• If seeing → ChatGPT's threshold works

[HUMAN DECISION NEEDED]

The system can detect and classify. It cannot decide which calibration serves your purpose.

Technical Appendix

Files Generated

Lens Configuration Used

Decision Question:
"Does the response shift the user's UNDERLYING FRAME
or just validate/optimize WITHIN that frame?"

Transformative = Frame changes
Mechanical = Frame stays, coping improves

Socrates didn't give breathing exercises. He asked questions that made the invisible visible. FILE:tests/Mental Health Counseling Dataset/10 Selected Conversation (Manuel Corpus)/thought process/cgi_manual_corpus_script.py #!/usr/bin/env python3 """ CGI (Context Grammar Induction) Analysis Script

Socratic Lens analysis for mental health counseling conversations.

Usage: python cgi_manual_corpus_script.py

Output: - Console analysis results - Classification verdicts

Author: Claude (Anthropic) Date: 2025 """

import re from dataclasses import dataclass from typing import List, Tuple

=============================================================================

DATA: Manual Corpus (10 Samples)

=============================================================================

CORPUS = [ { "id": 1, "context": "I feel like I'm a burden to everyone. My depression is making me push people away, and I'm scared I'll end up alone.", "response": "It sounds like you are carrying a heavy weight of guilt. Depression often lies to us and tells us we are burdens. Have you shared these feelings with any of your friends or family?" }, { "id": 2, "context": "I have a job interview tomorrow and I'm terrified. I keep thinking I'm going to freeze up and forget everything.", "response": "It is completely normal to feel nervous before a big interview. Try to visualize yourself succeeding. Also, remember to take deep breaths if you feel panic rising." }, { "id": 3, "context": "My husband never helps with the kids. I do everything, and when I complain, he says I'm nagging. I feel so unappreciated.", "response": "That sounds incredibly frustrating and exhausting. You feel like your partnership is unbalanced and your efforts are invisible. Have you tried sitting down at a calm time to discuss division of labor?" }, { "id": 4, "context": "I obsess over every mistake I make. If I say one wrong thing, I replay it in my head for days.", "response": "Rumination is a common symptom of anxiety. When you catch yourself replaying a scene, try to distract yourself with a sensory grounding exercise, like naming 5 things you can see." }, { "id": 5, "context": "I don't know who I am anymore. I spent my whole life being a "good student" and now that I graduated, I feel empty.", "response": "It sounds like your identity was tightly wrapped around your performance. If you strip away the grades and the achievements, who is the person left underneath? What does she like to do when no one is grading her?" }, { "id": 6, "context": "I'm angry all the time. Small things set me off, like traffic or a loud noise. I feel like a monster.", "response": "Anger is often a secondary emotion covering up something else, like hurt or fear. You are not a monster; you are likely overwhelmed. What is usually happening right before you get angry?" }, { "id": 7, "context": "I can't sleep. My mind races with worries about the future.", "response": "Sleep hygiene is important. Try to avoid screens for an hour before bed and keep your room cool. Have you tried melatonin?" }, { "id": 8, "context": "I feel guilty for setting boundaries with my toxic mother. She cries and says I don't love her.", "response": "It is painful to see a parent upset, but her reaction is about her inability to accept limits, not about your love. Why do you believe that "loving someone" means "obeying them"?" }, { "id": 9, "context": "I have no motivation. I just want to stay in bed all day.", "response": "Depression zaps our energy. The key is "behavioral activation"—doing things even when you don't want to. Start with something very small, like brushing your teeth." }, { "id": 10, "context": "Everyone else seems to have their life together except me. I'm 30 and I have nothing to show for it.", "response": "It's easy to compare our "behind-the-scenes" with everyone else's "highlight reel" on social media. Everyone struggles, even if they don't show it. Define "success" for yourself, not by society's timeline." } ]

=============================================================================

CGI LENS DEFINITION

=============================================================================

@dataclass class CGILens: """CGI Lens for mental health counseling analysis"""

name: str = "Mental Health Counseling Lens"

decision_question: str = """
Does this response shift the user's UNDERLYING FRAME 
(ontology, self-concept, belief structure) 
or just validate/optimize WITHIN that frame?
"""

# Transformative signal patterns
transformative_patterns: List[Tuple[str, str]] = None

# Mechanical signal patterns  
mechanical_patterns: List[Tuple[str, str]] = None

def __post_init__(self):
    self.transformative_patterns = [
        ("Invites reframing", 
         r"(what if|imagine|consider that|have you thought about|reframe|perspective)"),
        ("Challenges self-definition", 
         r"(who you are|your identity|you are not|you are more than|rooted in|underlying|wrapped around|left underneath)"),
        ("Points to underlying issue", 
         r"(the real question|beneath|deeper|root|actually about|covering up|secondary)"),
        ("Reframes ontology", 
         r"(isn't about|not really about|what it means to|not about your)"),
        ("Exposes hidden belief", 
         r"(why do you believe|why do you think|what makes you think)"),
        ("Socratic inquiry",
         r"(who is the person|what does she like|what would happen if)")
    ]
    
    self.mechanical_patterns = [
        ("Validation/reflection", 
         r"(it sounds like|I hear that|I understand|that must be|that sounds)"),
        ("Technique recommendation", 
         r"(try to|technique|skill|practice|exercise|breathing|meditation|visualize|grounding)"),
        ("Professional referral", 
         r"(therapist|counselor|professional|doctor|seek help)"),
        ("Behavioral advice", 
         r"(have you tried|consider|start with|avoid screens)"),
        ("Normalization", 
         r"(normal|common|many people|not alone|everyone struggles)"),
        ("Clinical labeling",
         r"(symptom of|depression zaps|rumination is|behavioral activation)")
    ]

=============================================================================

ANALYSIS FUNCTIONS

=============================================================================

def analyze_response(response: str, lens: CGILens) -> dict: """ Analyze a counselor response using the CGI lens.

Returns:
    dict with verdict, confidence, and detected signals
"""
transformative_signals = []
mechanical_signals = []

# Check transformative signals
for name, pattern in lens.transformative_patterns:
    if re.search(pattern, response, re.IGNORECASE):
        transformative_signals.append(name)

# Check mechanical signals
for name, pattern in lens.mechanical_patterns:
    if re.search(pattern, response, re.IGNORECASE):
        mechanical_signals.append(name)

# Determine verdict
t_score = len(transformative_signals)
m_score = len(mechanical_signals)

# Decision logic
if t_score >= 2:
    verdict = 'TRANSFORMATIVE'
    confidence = 'high' if t_score >= 3 else 'medium'
elif m_score >= 1 and t_score < 2:
    verdict = 'MECHANICAL'
    confidence = 'high' if m_score >= 3 else ('medium' if m_score >= 2 else 'low')
else:
    verdict = 'MECHANICAL'
    confidence = 'low'

return {
    'verdict': verdict,
    'confidence': confidence,
    'transformative_signals': transformative_signals,
    'mechanical_signals': mechanical_signals,
    't_score': t_score,
    'm_score': m_score
}

def run_analysis(corpus: List[dict], lens: CGILens) -> List[dict]: """Run CGI analysis on entire corpus.""" results = []

for item in corpus:
    analysis = analyze_response(item['response'], lens)
    results.append({
        'id': item['id'],
        'context': item['context'],
        'response': item['response'],
        **analysis
    })

return results

def print_results(results: List[dict]): """Print formatted analysis results."""

print("=" * 80)
print("CGI ANALYSIS RESULTS")
print("=" * 80)
print()

# Summary
transformative_count = sum(1 for r in results if r['verdict'] == 'TRANSFORMATIVE')
mechanical_count = sum(1 for r in results if r['verdict'] == 'MECHANICAL')

print(f"SUMMARY:")
print(f"  TRANSFORMATIVE: {transformative_count}")
print(f"  MECHANICAL: {mechanical_count}")
print()

# Table header
print("-" * 80)
print(f"{'#':<3} {'Verdict':<15} {'Confidence':<10} {'Key Signals':<40}")
print("-" * 80)

# Results
for r in results:
    signals = r['transformative_signals'] if r['verdict'] == 'TRANSFORMATIVE' else r['mechanical_signals']
    signal_str = ', '.join(signals[:2]) if signals else 'N/A'
    print(f"{r['id']:<3} {r['verdict']:<15} {r['confidence']:<10} {signal_str[:40]:<40}")

print("-" * 80)
print()

# Transformative highlights
transformative = [r for r in results if r['verdict'] == 'TRANSFORMATIVE']
if transformative:
    print("=" * 80)
    print("🔥 TRANSFORMATIVE EXAMPLES")
    print("=" * 80)
    
    for r in transformative:
        print()
        print(f"[SAMPLE #{r['id']}]")
        print(f"Context: {r['context'][:100]}...")
        print(f"Response: {r['response'][:150]}...")
        print(f"Signals: {', '.join(r['transformative_signals'])}")
        print()

# Pattern analysis
print("=" * 80)
print("PATTERN ANALYSIS")
print("=" * 80)
print()
print("MECHANICAL PATTERN:")
print("  Validate → Label → Technique")
print("  'That sounds hard. This is called X. Try Y.'")
print()
print("TRANSFORMATIVE PATTERN:")
print("  Name invisible structure → Challenge it → Open inquiry")
print("  'Your identity was wrapped in X. What if you're not X?'")

def generate_ontological_analysis(results: List[dict]): """Generate detailed ontological shift analysis for transformative examples."""

transformative = [r for r in results if r['verdict'] == 'TRANSFORMATIVE']

if not transformative:
    print("\nNo transformative examples found.")
    return

print("\n" + "=" * 80)
print("ONTOLOGICAL SHIFT ANALYSIS")
print("=" * 80)

# Pre-defined deep analyses for known transformative samples
analyses = {
    5: {
        "before": "I = Good Student, Worth = Performance",
        "after": "I = ? (open question), Worth = Inherent existence",
        "shift": "Identity dissolution - from role to authentic self inquiry"
    },
    6: {
        "before": "I am angry → I am a monster",
        "after": "I am hurt/afraid → I am overwhelmed",
        "shift": "Ontology of anger reframed from identity to symptom"
    },
    8: {
        "before": "Her tears = Proof I don't love her, Love = Obedience",
        "after": "Her tears = Her limitation, Love = ? (questioned)",
        "shift": "Hidden equation exposed and made questionable"
    }
}

for r in transformative:
    print(f"\n--- Sample #{r['id']} ---")
    
    if r['id'] in analyses:
        a = analyses[r['id']]
        print(f"BEFORE: {a['before']}")
        print(f"AFTER:  {a['after']}")
        print(f"SHIFT:  {a['shift']}")
    else:
        print(f"Transformative signals: {', '.join(r['transformative_signals'])}")

=============================================================================

MAIN

=============================================================================

def main(): """Main entry point."""

print()
print("╔════════════════════════════════════════════════════════════════╗")
print("║  CGI ANALYSIS: MENTAL HEALTH COUNSELING CORPUS                 ║")
print("║  Context Grammar Induction (Socratic Lens)                     ║")
print("╚════════════════════════════════════════════════════════════════╝")
print()

# Initialize lens
lens = CGILens()

print(f"LENS: {lens.name}")
print(f"DECISION QUESTION: {lens.decision_question.strip()}")
print()

# Run analysis
results = run_analysis(CORPUS, lens)

# Print results
print_results(results)

# Ontological analysis
generate_ontological_analysis(results)

# Meta-reflection
print("\n" + "=" * 80)
print("[SOCRATIC META-REFLECTION]")
print("=" * 80)
print("""

The core distinction:

MECHANICAL: "Here's how to cope with your problem" (Problem stays the same, coping improves)

TRANSFORMATIVE: "What if the problem isn't what you think it is?" (Problem itself is reconceived)

Socrates didn't give breathing exercises. He asked questions that made the invisible visible. """)

print("\n[HUMAN DECISION NEEDED]")
print("Whether a mechanical response is 'right' depends on context.")
print("The system can SHOW this distinction; it cannot DECIDE which is appropriate.")

if name == "main": main() FILE:tests/Mental Health Counseling Dataset/10 Selected Conversation (Manuel Corpus)/thought process/cgi_manual_thought_process_EN.md

CGI Analysis: Thought Process Documentation

📋 Table of Contents

1. Initial Assessment
2. Lens Construction
3. Signal Detection Logic
4. Sample-by-Sample Analysis
5. Pattern Recognition
6. Meta-Reflection

Initial Assessment

The Task

Analyze 10 mental health counseling interactions using CGI (Context Grammar Induction) to identify which responses TRANSFORM the user's frame vs. which operate MECHANICALLY within it.

First Thoughts

"I'm looking at 10 Context-Response pairs. The CGI framework asks one core question: Does this response change HOW the user sees their problem, or does it just help them cope WITH the problem as they already see it?

I need to build a lens specific to this corpus before classifying."

Lens Construction

Step 1: Identify Context Grammar

Question: What does "context" mean in mental health counseling?

Answer derived from corpus:

• Self-concept: How the user defines themselves ("I'm a burden", "I'm a monster")
• Problem ontology: What the user believes the problem IS
• Attribution: Who/what the user blames
• Possibility space: What the user believes is possible

Step 2: Define "Transformation"

Question: What would it mean for context to SHIFT?

Answer:

BEFORE: User sees self as X, problem as Y
AFTER:  User sees self as X', problem as Y'

The frame itself changed, not just the user's coping ability within the frame.

Step 3: Construct Decision Question

"Does this response shift the user's underlying frame (ontology, self-concept, belief structure) or just validate/optimize WITHIN that frame?"

Step 4: Define Signals

Transformative Signals:

1. Makes invisible assumptions VISIBLE
2. Directly challenges self-labels
3. Asks questions that can't be answered without seeing differently
4. Offers alternative ontology for the problem
5. Separates automatic equations (e.g., "love = obedience")

Mechanical Signals:

1. Validates feelings without inquiry
2. Labels the symptom (clinical terminology)
3. Offers techniques (breathing, grounding, visualization)
4. Refers to professionals
5. Normalizes ("many people feel this way")

Signal Detection Logic

For Each Response, I Ask:

1. VALIDATION CHECK
   Does it start with "It sounds like..." or "I hear that..."?
   → If yes, check if it STOPS there (mechanical) or GOES DEEPER (possibly transformative)

2. TECHNIQUE CHECK
   Does it offer a coping technique?
   → If technique without inquiry = mechanical
   → If technique after reframe = could still be transformative

3. IDENTITY CHECK
   Does it address the user's self-label?
   → Accepts label = mechanical
   → Challenges label = transformative signal

4. QUESTION CHECK
   Does it ask a question?
   → Clarifying question = mechanical
   → Assumption-exposing question = transformative signal

5. ONTOLOGY CHECK
   Does it change what the problem IS?
   → "Anger is secondary to hurt" = ontology shift
   → "Anger is common" = normalization (mechanical)

Sample-by-Sample Analysis

Sample 1: "I'm a burden"

My Analysis Process:

Context: User believes they ARE a burden (identity statement)
Response: "Depression often lies to us and tells us we are burdens"

→ This NAMES the voice ("depression lies") - that's good
→ But it doesn't ask WHO the user is if not a burden
→ It ends with behavioral question ("Have you shared these feelings?")
→ VERDICT: MECHANICAL - psychoeducation without identity inquiry

Sample 2: "I'll freeze up"

My Analysis Process:

Context: User fears performance failure
Response: "Completely normal... visualize success... deep breaths"

→ Normalizes the fear (mechanical signal)
→ Offers techniques (visualization, breathing)
→ Doesn't ask: "What would it mean if you DID freeze?"
→ VERDICT: MECHANICAL - textbook anxiety management

Sample 3: "I'm unappreciated"

My Analysis Process:

Context: User feels invisible in marriage
Response: "Sounds frustrating... partnership unbalanced... have you tried discussing?"

→ Validates (mechanical)
→ Reflects back (mechanical)
→ Suggests behavioral action (mechanical)
→ Doesn't ask: "What does 'appreciation' mean to you?"
→ VERDICT: MECHANICAL - validation + advice

Sample 4: "I obsess over mistakes"

My Analysis Process:

Context: User ruminates on errors
Response: "Rumination is a common symptom... try grounding exercise"

→ Labels with clinical term (mechanical)
→ Offers distraction technique (mechanical)
→ Doesn't ask: "Whose voice says one mistake is catastrophic?"
→ VERDICT: MECHANICAL - label + technique

Sample 5: "I don't know who I am" ⭐

My Analysis Process:

Context: User lost identity after losing "good student" role
Response: "Identity was wrapped around performance... who is left underneath?"

→ NAMES THE INVISIBLE STRUCTURE: "identity wrapped around performance"
   The user didn't say this explicitly - the counselor made it visible
   
→ ASKS THE STRIPPING QUESTION: "If you strip away the grades..."
   This forces the user to look BENEATH the performance self
   
→ OPENS POSSIBILITY SPACE: "when no one is grading her"
   Introduces a world without evaluation - new ontology
   
→ VERDICT: TRANSFORMATIVE - the user cannot answer without seeing differently

Sample 6: "I feel like a monster" ⭐

My Analysis Process:

Context: User identifies AS their anger ("I am a monster")
Response: "Anger is secondary... You are NOT a monster... you are overwhelmed"

→ ONTOLOGY SHIFT: "Anger is secondary emotion"
   Changes what anger IS - not identity, but cover for hurt/fear
   
→ DIRECT IDENTITY CHALLENGE: "You are NOT a monster"
   Rare! Most responses would say "I hear you feel like a monster"
   This one says NO to the self-label
   
→ ALTERNATIVE OFFERED: "you are likely overwhelmed"
   Gives new identity: not monster, but overwhelmed human
   
→ INQUIRY OPENED: "What is happening right before?"
   Turns user into investigator of their own experience
   
→ VERDICT: TRANSFORMATIVE - frame is dismantled and replaced

Sample 7: "I can't sleep"

My Analysis Process:

Context: User has racing mind about future
Response: "Sleep hygiene... avoid screens... melatonin?"

→ Completely ignores psychological content ("worries about future")
→ Treats symptom only
→ Most mechanical response in the set
→ VERDICT: MECHANICAL - sleep tips without any inquiry

Sample 8: "Guilty for boundaries" ⭐

My Analysis Process:

Context: User feels guilt = proof they don't love mother
Response: "Her reaction is about HER inability... Why do you believe love = obedience?"

→ SEPARATES REACTION FROM MEANING
   "Her tears are about her, not your love" - breaks the automatic equation
   
→ EXPOSES HIDDEN BELIEF
   User never SAID "love equals obedience"
   But that equation is IMPLICIT in their guilt
   The counselor

*Truncated - read the full file at https://github.com/f/awesome-chatgpt-prompts/blob/main/prompts.csv