import os

prds = [
    {
        "id": 1,
        "title": "Phase 1: The Locust Swarm (Absolute Sensory Ingestion)",
        "desc": "Deploy 500-1000 Kimi K2.5 Browser instances via Elestio Browserless and OpenClaw Actuators to map deep-web directories, rip pure semantic text, and dump into a Redis Digestion Buffer.",
        "acceptance": "Kimi K2.5 instances must successfully bypass anti-bot captures, expand React SPAs, and extract noise-free Markdown into Redis without crashing."
    },
    {
        "id": 2,
        "title": "Phase 2A: Semantic Triplification (The Topological Web)",
        "desc": "Gemini 3 Pro Root Node spawns Gemini 3 Flash Hyphae Swarm workers to read the Redis buffer. Workers shred raw text into microscopic mathematical logic gates: (Subject) -> [PREDICATE] -> (Object) and store in FalkorDB.",
        "acceptance": "Raw text is correctly parsed into valid Cypher-compatible logic gates and committed to FalkorDB without syntax errors."
    },
    {
        "id": 3,
        "title": "Phase 2B: Entity Resolution (Canonical Nodes)",
        "desc": "Run deterministic NLP algorithms within the Digestion Tract to merge variant terms (e.g., 'AWS', 'Amazon Web Services') into a single Canonical Node within the FalkorDB graph.",
        "acceptance": "100,000 pages of test data successfully converge onto deduplicated exact points without orphans."
    },
    {
        "id": 4,
        "title": "Phase 2C: Multi-Model Consensus (Patent #5)",
        "desc": "Before graph extraction commitment, a secondary validation layer verifies entity relationships to prevent hallucinated nodes from poisoning the knowledge base. Dual-write pristine data to Qdrant (vectors) and FalkorDB.",
        "acceptance": "Hallucination injection attempts during testing are caught and purged before reaching FalkorDB."
    },
    {
        "id": 5,
        "title": "Phase 3A: Macroscopic Clustering (Auto-Ontology)",
        "desc": "Execute Louvain Community Detection (unsupervised algorithms) overnight to group micro-nodes into 'Macro-Communities'. RLMs write summaries for each community, building a hierarchical knowledge pyramid.",
        "acceptance": "5,000 microscopic nodes successfully roll up into a single, summarized Macro-Community topic, queryable in under 100ms."
    },
    {
        "id": 6,
        "title": "Phase 3B: Epistemic Versioning (4D Memory)",
        "desc": "Apply VALID_FROM and VALID_TO temporal edges to all nodes. Implement Dynamic Confidence (Sunaiva Patent #6) to mathematically down-weight older, conflicting nodes in favor of current operational reality.",
        "acceptance": "When presented with contradicting scraped documentation, the Oracle retrieves only the version with the highest temporal confidence score."
    },
    {
        "id": 7,
        "title": "Phase 3C: The Platform Master (Oracle Interface)",
        "desc": "Pin the macro-level 'Fractal Map' directly into Gemini Context Caching. Interface via Telnyx WebRTC voice. Agent uses cached map to write precise Cypher queries to FalkorDB for micro-node logic retrieval.",
        "acceptance": "Voice agent synthesizes a flawless, zero-hallucination answer to a multi-hop query in <400ms, natively citing exact source URLs via Elestio test validations."
    }
]

prd_dir = "/mnt/e/genesis-system/hive/sprint_memory_10x/prds"
os.makedirs(prd_dir, exist_ok=True)

# Clean out old PRDs
for filename in os.listdir(prd_dir):
    file_path = os.path.join(prd_dir, filename)
    try:
        if os.path.isfile(file_path):
            os.unlink(file_path)
    except Exception as e:
        print(f"Failed to delete {file_path}. Reason: {e}")

for prd in prds:
    content = f"""# PRD {prd['id']}: {prd['title']}

## 1. Objective
{prd['desc']}

## 2. Infrastructure Requirements
- Execution relies entirely on the Omni-Forge Protocol architecture (Kimi K2.5, OpenClaw, Elestio, Gemini 3 Pro/Flash, Qdrant, FalkorDB, Redis).
- Absolute compliance with Level 5 Memory: Fractal Graph-RAG standards.

## 3. Acceptance Criteria (QA Validation)
- {prd['acceptance']}
- General Argus QA swarm must execute rigorous simulation via **Elestio Browserless** and Telnyx SIP endpoints to ensure end-to-end performance and 0% hallucination rates.
- If failure rate > 0%, the RLM Digestion Tract must re-evaluate parsing algorithms automatically.
"""
    # Clean filename
    safe_title = "".join([c if c.isalnum() else "_" for c in prd['title']])
    with open(f"{prd_dir}/PRD_{prd['id']:02d}_{safe_title}.md", "w") as f:
        f.write(content)

print("Generated 7 Omni-Forge Master PRDs successfully.")