prometheus

name	prometheus
description	Prometheus — User Preference Learning. Use this skill to learn and persist user preferences across sessions. Tracks: detail level, domain expertise, risk tolerance, preferred sources, communication style. Updates based on explicit feedback and implicit signals. Commands: /prometheus profile, /prometheus set, /prometheus update, /prometheus clear, /prometheus status.

Prometheus — User Preference Learning

Prometheus (Greek: Προμηθεύς, "forethought") learns and persists user preferences across sessions. It tracks detail level, domain expertise, risk tolerance, preferred sources, and communication style — adapting Abraxas output to the individual user.

Why Prometheus?

One-size-fits-all epistemic framing doesn't serve all users well. A novice needs different granularity than an expert. A high-stakes decision-maker needs different uncertainty presentation than someone casually browsing. Prometheus adapts the system to the user.

• Personalized detail level — Experts get concise, novices get explanation
• Domain expertise tracking — Adapts based on demonstrated knowledge
• Risk tolerance — High-stakes queries get more caveats
• Learning from interaction — Explicit feedback + implicit signals
• Cross-session persistence — Remembers preferences between sessions

Use Cases

1. Explicit preference setting: User says "I prefer concise answers"
2. Implicit learning: System notices user always rejects verbose explanations
3. Expertise detection: User asks about advanced physics → mark as expert
4. Risk adaptation: For high-stakes queries, add uncertainty caveats

Storage Location

~/.abraxas/prometheus/

~/.abraxas/prometheus/
├── profiles/
│   └── {user_id}.json   # User preference profiles
├── signals/
│   └── {date}.json      # Raw signal data for learning
└── config.yaml          # Prometheus configuration

Command Suite

Command	Description
/prometheus profile {user_id}	Show current user preference profile
/prometheus set {key} {value}	Set a specific preference
/prometheus update {key} {value}	Update based on observed behavior
/prometheus clear {user_id}	Clear user profile
/prometheus status	Show learning status and signals
/prometheus signals {limit}	Show recent preference signals
/prometheus train	Run preference learning update

Preference Dimensions

Detail Level

• terse: One-liner answers, minimal explanation
• balanced: Moderate detail, key points only
• detailed: Full explanation, all reasoning shown
• comprehensive: Exhaustive, leave no stone unturned

Domain Expertise

• novice: No technical background assumed
• intermediate: Some domain knowledge
• advanced: Expert-level understanding
• specialist: Deep domain expertise

Risk Tolerance

• low: High caution, extensive caveats for uncertain claims
• medium: Balanced uncertainty presentation
• high: Allow confident-sounding output, minimal hedging
• custom: User-configured risk parameters

Communication Style

• formal: Professional, structured
• casual: Conversational, friendly
• technical: Use domain terminology
• simple: Plain language, avoid jargon

Preferred Sources

• List of trusted domains/sources (e.g., arxiv.org, wikipedia.org)
• Can be prioritized for different domains

Signal Types

Explicit Signals

• /prometheus set detail_level detailed — Direct preference
• User corrections: "No, I meant..." → update learned preference

Implicit Signals

Signal	Interpretation
Follow-up questions	More detail needed
Rejection of suggestion	Didn't match preference
Clarification requests	Current explanation too complex
Quick acknowledgments	Prefer concise
Deep follow-ups	Show expertise, can handle detail

Profile Schema

{
  "user_id": "default",
  "preferences": {
    "detail_level": "balanced",
    "domain_expertise": "intermediate",
    "risk_tolerance": "medium",
    "communication_style": "technical",
    "preferred_sources": ["wikipedia.org", "arxiv.org"]
  },
  "signals": {
    "total_signals": 150,
    "explicit": 20,
    "implicit": 130,
    "last_signal": 1709999999
  },
  "confidence": {
    "detail_level": 0.85,
    "domain_expertise": 0.70,
    "risk_tolerance": 0.60
  },
  "updated": 1709999999
}

Usage Examples

Viewing Profile

/prometheus profile default
→ Detail Level: balanced (85% confidence)
→ Domain Expertise: intermediate (70% confidence)
→ Risk Tolerance: medium (60% confidence)
→ Communication Style: technical
→ Preferred Sources: wikipedia.org, arxiv.org
→ Signals: 150 total (20 explicit, 130 implicit)
→ Last Updated: 2024-03-15

Setting Preferences

/prometheus set detail_level terse
→ Set: detail_level = terse
→ Profile updated

/prometheus set risk_tolerance low
→ Set: risk_tolerance = low (will add more caveats)
→ Profile updated

Implicit Learning

User: [Asks about quantum entanglement with precise terminology]

Prometheus detects: High technical language → signal expertise = advanced
→ domain_expertise: intermediate → advanced (confidence 0.75)
→ Updated profile

User: [Asks for clarification on basics]

Prometheus detects: Needs simpler explanation → signal: expertise_level mismatch
→ Review: keep at intermediate, user may be exploring

Risk Adaptation

User: [Query involves medical decision]

Prometheus: High-stakes domain detected → apply risk_tolerance=low
→ Output includes: "This is not medical advice", uncertainty caveats
→ Confidence intervals added to any numerical claims

Integration with Janus & Honest

Prometheus modifies output presentation:

Janus Label	+ Prometheus Detail Level
[KNOWN]	terse: "X" / detailed: "X (source: Y)"
[INFERRED]	terse: "X likely" / detailed: full reasoning chain
[UNCERTAIN]	terse: "unsure" / detailed: "X (70% confidence)"

Prometheus affects how information is presented, not what information is true.

Implementation

Core learning algorithm:

1. Signal collection — Gather explicit + implicit preference signals
2. Feature extraction — Map signals to preference dimensions
3. Bayesian update — Update belief about preferences using signals
4. Confidence tracking — Measure certainty in each preference dimension
5. Application — Modify output based on learned profile

interface Prometheus {
  getProfile(userId: string): UserProfile;
  setPreference(userId: string, key: string, value: any): void;
  recordSignal(userId: string, signal: PreferenceSignal): void;
  learn(userId: string): UserProfile;
  applyPreferences(output: string, profile: UserProfile): string;
}

See references/prometheus-architecture.md for technical details.