import sqlite3, json, os, time

DB_PATH = '/root/.hermes/kanban/boards/research-hub/kanban.db'
TS = int(time.time())
project_path = "/opt/ai-os/products/ceo/content/research/educational_planning_positions"

conn = sqlite3.connect(DB_PATH)
c = conn.cursor()

tasks = [
    {
        "id": f"ep_p1a_{TS}",
        "title": "[Phase 1a] Khao sat & Kham pha -- Xac dinh danh sach truong (US, UK, China, Aus)",
        "assignee": "r-and-d",
        "status": "ready",
        "skills": json.dumps(["web_search", "paper_search_mcp"]),
        "body": f"""[CONTEXT]
Thu muc du an: {project_path}
Luan de trung tam: Cac vi tri hoach dinh giao duc bac dai hoc/sau dai hoc duoc thiet ke ra sao...

[MUC TIEU]
1. BO SUNG THI TRUONG UC (Australia) vao danh sach nghien cuu.
2. Cap nhat research_plan.md de bao gom:
   - Danh sach truong cho US, UK, China (giu nguyen nhu lan chay truoc)
   - Danh sach truong cho Australia (tim them ~5-8 truong co vi tri hoach dinh/chien luoc)
   - Methodology ap dung them cho Australia
   - Expected TOC cho tat ca cac Phase bao gom ca bao cao Australia.

[OUTPUT]
Ghi vao file: {project_path}/research_plan.md
"""
    },
    {
        "id": f"ep_p1b_{TS}",
        "title": "[Phase 1b] Xay dung Research Output Template",
        "assignee": "r-and-d",
        "status": "todo",
        "skills": json.dumps([]),
        "body": f"""[CONTEXT]
Doc research_plan.md
Thiet ke template: {project_path}/research_output_template.md cho Phase 2.
"""
    },
    {
        "id": f"ep_p2_us_{TS}",
        "title": "[Phase 2a] Deep Research -- My (US)",
        "assignee": "r-and-d",
        "status": "todo",
        "skills": json.dumps(["web_search", "notebooklm-research"]),
        "body": f"""Output: {project_path}/master_reports/educational_planning_us.md"""
    },
    {
        "id": f"ep_p2_uk_{TS}",
        "title": "[Phase 2b] Deep Research -- Anh (UK)",
        "assignee": "r-and-d",
        "status": "todo",
        "skills": json.dumps(["web_search", "notebooklm-research"]),
        "body": f"""Output: {project_path}/master_reports/educational_planning_uk.md"""
    },
    {
        "id": f"ep_p2_cn_{TS}",
        "title": "[Phase 2c] Deep Research -- Trung Quoc (China)",
        "assignee": "r-and-d",
        "status": "todo",
        "skills": json.dumps(["web_search", "notebooklm-research"]),
        "body": f"""Output: {project_path}/master_reports/educational_planning_china.md"""
    },
    {
        "id": f"ep_p2_au_{TS}",
        "title": "[Phase 2d] Deep Research -- Uc (Australia)",
        "assignee": "r-and-d",
        "status": "todo",
        "skills": json.dumps(["web_search", "notebooklm-research"]),
        "body": f"""Output: {project_path}/master_reports/educational_planning_australia.md"""
    },
    {
        "id": f"ep_p3_{TS}",
        "title": "[Phase 3] Tong hop & So sanh (US, UK, CN, AU)",
        "assignee": "writers",
        "status": "todo",
        "skills": json.dumps(["research-critism"]),
        "body": f"""Output: {project_path}/master_reports/comparative_analysis.md & index.md"""
    }
]

for t in tasks:
    c.execute("""
        INSERT INTO tasks (id, title, assignee, status, body, skills, created_at, goal_mode)
        VALUES (?, ?, ?, ?, ?, ?, ?, 1)
    """, (t["id"], t["title"], t["assignee"], t["status"], t["body"], t.get("skills", None), TS))

conn.commit()
conn.close()
print("7 tasks created.")