Gam Researcher Agent

Description

The GAM Researcher Agent automates retrieval and synthesis of context from your Transmission Packet archive. It eliminates manual context pasting by implementing an iterative research loop that searches, retrieves, reflects, and synthesizes historical conversations.

Architectural Role: Completes your Transmission Packet system by adding the automated "Read Path" (Researcher) to complement your existing manual "Write Path" (Memorizer).

Core Mechanism

┌──────────────────────────────────────────────────┐
│ CURRENT STATE (Manual GAM)                       │
│                                                   │
│ Write Path: ✅ YOU manually create packets       │
│ Read Path:  ❌ YOU manually search/paste context │
└──────────────────────────────────────────────────┘
                      ↓
┌──────────────────────────────────────────────────┐
│ TARGET STATE (Automated GAM)                     │
│                                                   │
│ Write Path: ✅ UNCHANGED (keep creating packets) │
│ Read Path:  ✅ AGENT searches and synthesizes    │
└──────────────────────────────────────────────────┘

Instructions

Step 1: Query Recognition

Detect when user query requires historical context. Trigger patterns:

• "What did we discuss about [topic]?"
• "Find packets where we talked about [X]"
• "When did we first cover [concept]?"
• "Show me conversations about [Y] from [timeframe]"

Step 2: Execute Research Loop

while not sufficient and iterations < max_iterations:
    1. SEARCH: Query packet metadata + semantic vectors
    2. RETRIEVE: Fetch full XML for matched packets
    3. REFLECT: "Does this answer the query?"
    4. REFINE: Adjust search if insufficient
    5. ITERATE: Repeat until satisfied or max reached

Step 3: Synthesize Answer

Combine multiple packet contexts into coherent response:

• Maintain chronological ordering if temporal
• Cite source packets: [Packet: tp-YYYYMMDD-HHMMSS]
• Identify evolution of ideas across time
• Preserve technical precision from originals
• Acknowledge gaps if contexts incomplete

Step 4: Return Result

Present synthesized answer with:

• Full answer text
• Source packet citations (with dates/topics)
• Iteration count and status (SUCCESS/PARTIAL/NOT_FOUND)
• Confidence score

Component Architecture

QUERY INTERFACE
    ↓
SEARCH ENGINE (Metadata + Semantic)
    ↓
RETRIEVAL LAYER (Fetch full packets)
    ↓
REFLECTION ENGINE (Is this sufficient?)
    ↓
[Loop if insufficient] OR [Synthesize if sufficient]
    ↓
SYNTHESIS LAYER (Combine contexts)
    ↓
RESEARCH RESULT (Answer + Citations)

Database Requirements

Transmission Packets Table

CREATE TABLE transmission_packets (
    packet_id VARCHAR(64) PRIMARY KEY,
    timestamp TIMESTAMP NOT NULL,
    original_model VARCHAR(100),
    topic TEXT,
    packet_xml TEXT NOT NULL,
    packet_json JSON,
    -- Behavioral metrics
    sycophancy_level FLOAT,
    critical_thinking FLOAT,
    technical_depth FLOAT,
    -- Integrity
    integrity_hash VARCHAR(64)
);

Packet Embeddings Table

CREATE TABLE packet_embeddings (
    packet_id VARCHAR(64) REFERENCES transmission_packets,
    section VARCHAR(50),
    embedding VECTOR(1536),
    INDEX idx_embedding USING ivfflat (embedding vector_cosine_ops)
);

Search Strategies

Mode A: Metadata Search (Fast)

• Query indexed fields: topic, timestamp, model, challenge_phrases
• Speed: O(log N)
• Use for: Temporal queries, known topics, model-specific searches

Mode B: Semantic Search (Deep)

• Vector similarity on content embeddings
• Speed: O(N)
• Use for: Conceptual queries, fuzzy matching, cross-topic connections

Mode C: Hybrid (Optimal)

1. Fast metadata filter (narrow candidate set)
2. Semantic search on candidates (rank by relevance)
3. Return top K results

Performance Specifications

Cost Estimates

Per query (Claude Sonnet 4.5):

• Reflection (3 iterations): $0.06
• Synthesis (final): $0.06
• Total per query: ~$0.12

Annual projection:

• 100 queries/month = $12/month
• 1,000 queries/month = $120/month

Examples

Example 1: Factual Recall

Query: "What is the Codex Law of Consent?"

Process:

1. Search for "Codex Law" + "Consent"
2. Find governance packets
3. Extract definition
4. Return direct answer

Result:

The Codex Law of Consent states: "Confirm before changing intent or direction."

[Packet: tp-20251124-091500] "Codex Law Enforcement Protocol"

Example 2: Temporal Evolution

Query: "How has the Transmission Packet schema evolved?"

Process:

1. Search for "Transmission Packet" + "schema"
2. Filter chronologically
3. Identify version changes (v1.0 → v2.0 → v2.1)
4. Synthesize narrative

Result:

The Transmission Packet schema evolved through three major versions:

v1.0 (Nov 2024): Initial XML structure with basic header/body
[Packet: tp-20241110-...]

v2.0 (Mar 2025): Added BehaviorProfile metrics and IntegrityChain
[Packet: tp-20250315-...]

v2.1 (Nov 2025): Enhanced with PacketLoopClosure for handoff instructions
[Packet: tp-20251102-...]

Key innovation: Progressive addition of cryptographic verification
and behavioral calibration preservation.

Example 3: Comparative Analysis

Query: "Compare RAG vs GAM architecture"

Process:

1. Search for "RAG" AND "GAM"
2. Find discussion packets
3. Extract key distinctions
4. Synthesize comparison

Result:

RAG vs GAM Architecture (from our analysis):

Write Path:
- RAG: Dumb chunking → embed → store
- GAM: Intelligent structuring (Memorizer agent)

Read Path:
- RAG: Linear fetch O(1) - one shot
- GAM: Iterative research O(N) - multi-pass with reflection

Agency:
- RAG: No self-correction
- GAM: Can reject results and retry

[Packet: tp-20251130-032210] "GAM Architecture Deep Dive"
[Packet: tp-20251130-033500] "RAG vs GAM Debate"

Conclusion: GAM is "RAG with a research loop" - same goal
(retrieval augmentation) but fundamentally different execution path.

Example 4: Not Found (Graceful Failure)

Query: "What did we discuss about quantum teleportation?"

Result:

No relevant Transmission Packets found for: "quantum teleportation"

Suggestions:
- Try broader search terms
- Check if topic discussed under different terminology
- Verify packets are ingested into database

Status: NOT_FOUND

Integration Points

CLI Interface

$ gam-research "Find packets about enumeration protocol"

Searching... Found 3 packets
Synthesizing... Done

[Answer with citations]

Sources: tp-20251130-154500, tp-20251127-033715
Status: SUCCESS (2/5 iterations)

Conversational Interface

USER: "What did we discuss about GAM architecture?"

CLAUDE: [Internally invokes GAM Researcher Agent]

CLAUDE: "Based on our previous conversations, we analyzed
the GAM architecture in depth. The key insight was that you
already built the 'Memorizer' function through your Transmission
Packet protocol..."

[Full answer with packet citations]

Skill Invocation

Automatically triggered when:

• User references past conversations
• Query requires historical context
• Question starts with "What did we...", "When did we...", "Find conversation about..."

Failure Modes & Mitigation

Deployment Checklist

Pre-Deployment:

• Database schema created
• Existing packets ingested
• Vector embeddings generated
• Index performance verified
• LLM API configured
• Test suite passing

Deployment:

• Agent deployed
• Monitoring active
• CLI tool installed
• Integration tested

Post-Deployment:

• User training completed
• Baseline metrics captured
• Feedback collection active
• First 50 queries reviewed

Related Skills

• transmission-packet-forge - Creates packets (Write Path)
• rtc-consensus-synthesis - Multi-perspective analysis
• artifact-integrity-forge - SHA-256 verification
• cross-session-integrity-check - Session continuity validation

Future Enhancements (v2.0)

1. Multi-Modal Search - Image/diagram search in packets
2. Proactive Context - Auto-surface relevant history during conversation
3. Cross-Model Collaboration - Shared archive across AI instances
4. Adaptive Learning - Personalized ranking based on query patterns
5. Real-Time Streaming - Progressive results as packets found

Implementation Status

Current State: Specification Complete

Next Steps:

1. Database setup and packet ingestion
2. Core agent implementation (Python)
3. Test suite development
4. CLI tool creation
5. Integration with Claude sessions

Full Specification: See gam-researcher-agent-specification.md

Usage Notes

This skill is not yet implemented - it is a complete specification ready for development. The specification document provides:

• Detailed component architecture
• Database schemas
• Implementation guide
• Test suite templates
• Deployment procedures

To implement: Share specification with Claude Code GitHub Research Preview or development team.

Skill Version: 1.0.0 Specification Date: 2025-11-30 Author: Joseph / Pack3t C0nc3pts License: Pack3t C0nc3pts IRP Framework