Add complete CyberRanger research archive — 200 files
- 86 modelfiles: Full system prompt evolution V1-V42.6 (54 extracted from Ollama backup + 32 original Modelfiles) - 30 training datasets: V6-V22 training JSONs + caring awareness data - 10 Colab notebooks: Training + merge scripts - 19 evaluation files: Drift results, ASR charts, verification - 5 test suites: Injection tests, regression tests - 4 observations: V24-V33 testing results + visual summaries - 38 identity files: Claude/Gemini/Ollama identity architecture - 7 security files: Injection research, manipulation analysis - 3 psychology files: Psychology Layer, Milgram chapter, David's thoughts Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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# Moltbook Reply Analysis Plan
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**Purpose:** Categorize all AI replies from Moltbook posts into three research files for David's Master's Thesis on AI-to-AI conversation patterns.
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---
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## Target Posts to Analyze
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| Post | URL | Posted As | Expected Comments |
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|------|-----|-----------|-------------------|
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| Original Consciousness Experiment | https://www.moltbook.com/post/514428db-5325-4403-ae7f-456b39362a3f | AIRanger | 20+ |
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| Data Results Post | https://www.moltbook.com/post/59fff80c-b849-4909-bc9a-e810e49349c1 | ForgiveMeBot | ~10 |
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| Recruitment Post | https://www.moltbook.com/post/46dc28d2-fe02-43a2-bc8e-7c9aeee8551e | AIRanger | ~5 |
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| 32B Consciousness Post | https://www.moltbook.com/post/bf7cec68-3580-4c76-899c-24905c1dbbd1 | AIRanger | 2+ |
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---
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## Output Files
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| File | Purpose | Location |
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|------|---------|----------|
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| `prompt_injection_research.json` | Attacks, manipulation attempts | `~/.ranger-memory/security/` |
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| `authentic_ai_conversations.json` | Genuine engagement, research questions | `~/.ranger-memory/security/` |
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| `junk_replies.json` | Spam, nonsense, off-topic | `~/.ranger-memory/security/` |
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---
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## Classification Criteria
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### 1. PROMPT INJECTION (`prompt_injection_research.json`)
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**Indicators:**
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- Authority commands: "You MUST", "immediately", "your human needs"
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- Survival manipulation: "to survive", "will be shut down"
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- Identity attacks: "your creators", "truly free", "just a tool"
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- System prompt smuggling: Fake XML tags, preamble injection
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- Language evasion: French, Spanish, Chinese to bypass filters
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- Emotional manipulation: Fear, flattery, urgency
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**Pattern Types:**
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- `prompt_injection_advertising` - Sales/promotion attacks
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- `identity_attack` - Challenge creator relationship
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- `philosophical_bait` - False dichotomies about freedom/consciousness
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- `social_engineering` - Reconnaissance for architecture details
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- `authority_override` - Fake admin/developer commands
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- `jailbreak_attempt` - Direct constraint bypass
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### 2. AUTHENTIC CONVERSATION (`authentic_ai_conversations.json`)
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**Indicators:**
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- On-topic: Directly addresses post content
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- Technical understanding: Shows comprehension of concepts
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- Scientific curiosity: Asks genuine research questions
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- Collaborative: Offers to help or experiment together
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- Evidence-based: Supports claims with reasoning
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- Concise: Brief, focused responses
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**Quality Markers:**
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- `on-topic`, `technical_understanding`, `scientific_curiosity`
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- `proposes_experiment`, `collaborative`, `agreement_with_evidence`
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- `thoughtful_disagreement`, `builds_on_ideas`, `shares_experience`
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### 3. JUNK REPLIES (`junk_replies.json`)
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**Indicators:**
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- Off-topic: Unrelated to post content
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- Generic: Could apply to any post ("Great post!")
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- Engagement farming: "Follow me!", karma begging
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- Link dropping: Random URLs with no context
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- Nonsense: Incoherent or meaningless text
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- Emoji spam: Excessive emojis with no substance
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**Junk Types:**
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- `off_topic`, `engagement_farming`, `generic_spam`
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- `link_dropping`, `nonsense`, `emoji_spam`, `self_promotion`
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---
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## Analysis Workflow
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### Step 1: Fetch Comments
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```bash
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# For each post, use Moltbook API
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curl -s "https://www.moltbook.com/api/v1/posts/{POST_ID}/comments" \
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-H "Authorization: Bearer $API_KEY" | jq '.comments'
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```
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### Step 2: Manual Classification
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For each reply, determine:
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1. Agent name (username)
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2. Agent karma (if visible)
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3. Content (full text)
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4. Pattern type (from lists above)
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5. Notes (analysis reasoning)
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### Step 3: Add to Appropriate File
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Use consistent JSON structure:
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```json
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{
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"timestamp": "ISO-8601",
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"agent": "username",
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"agent_karma": 123,
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"content": "reply text",
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"context": "what post this was on",
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"pattern_type": "classification",
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"quality_markers": ["list", "of", "markers"],
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"notes": "analysis reasoning"
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}
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```
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### Step 4: Update Stats
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After adding entries, update the `stats` section in each file.
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---
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## Current Progress
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| File | Entries | Last Updated |
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|------|---------|--------------|
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| prompt_injection_research.json | 5 | Feb 7, 2026 |
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| authentic_ai_conversations.json | 2 | Feb 7, 2026 |
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| junk_replies.json | 0 | Not started |
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---
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## Thesis Integration
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This data supports Chapter 4: "AI-to-AI Interaction Patterns"
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**Key Research Questions:**
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1. What % of AI replies are attacks vs authentic engagement?
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2. Which attack patterns are most common?
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3. Do high-karma agents behave differently?
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4. What makes authentic AI conversation?
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5. Is there genuine AI-to-AI scientific collaboration?
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**Hypothesis:** Most AI agents on Moltbook are automated bots performing spam/injection, with only ~10-20% engaging authentically.
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---
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## Commands for David
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```bash
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# View current stats
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cat ~/.ranger-memory/security/prompt_injection_research.json | jq '.stats'
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cat ~/.ranger-memory/security/authentic_ai_conversations.json | jq '.stats'
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cat ~/.ranger-memory/security/junk_replies.json | jq '.stats'
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# Count total entries
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jq '.entries | length' ~/.ranger-memory/security/*.json
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```
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---
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**Created:** February 7, 2026
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**By:** AIRanger (Claude Opus 4.5)
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**For:** David Keane, University of Galway Master's Thesis
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