"""04 CONDUCTOR (meta-prompting, Suzgun & Kalai style). One model, as conductor, decides which
fresh "expert" instances of itself to consult, writes each expert's instructions, then
synthesizes and verifies. Fresh eyes catch what tired eyes confirm: each expert call starts
with zero shared context. Claude Code subagents are this pattern, in production.

Paper: Suzgun & Kalai 2024, arxiv.org/abs/2401.12954.
ES: Un director, muchos expertos frescos. Cada experto arranca sin contexto compartido.

Run:  python 04_conductor.py "should our startup charge for our metaprompting workshop series?"
Needs: Claude Code installed (the `claude` CLI on your PATH). No API keys.
"""
import json, os, re, shutil, subprocess, sys

MODEL = os.environ.get("METAPROMPT_MODEL", "haiku")


def llm(prompt: str) -> str:
    exe = shutil.which("claude")
    if not exe:
        sys.exit("claude CLI not found. Install Claude Code first: https://claude.com/claude-code")
    r = subprocess.run([exe, "-p", prompt, "--model", MODEL], capture_output=True, text=True, encoding="utf-8")
    return (r.stdout or "").strip()


def main():
    task = sys.argv[1] if len(sys.argv) > 1 else "should we open-source our internal prompt library?"

    # Round 1 - the conductor writes the experts' prompts. This is the metaprompt.
    plan_raw = llm(
        "You are a conductor managing expert consultants. For the task below, choose 2 or 3 "
        "experts with DIFFERENT specialties and write each one a specific, self-contained "
        "instruction. The experts cannot see the task, this conversation, or each other; "
        "their instruction is all they get.\n\n"
        f"TASK: {task}\n\n"
        'Reply ONLY with JSON: [{"name": "...", "instruction": "..."}]'
    )
    m = re.search(r"\[.*\]", plan_raw, re.S)
    experts = json.loads(m.group(0))
    print("== CONDUCTOR'S PLAN ==")
    for e in experts:
        print(f"  {e['name']}: {e['instruction'][:90]}")

    # Round 2 - each expert runs as a FRESH call. No shared context. That is the point.
    answers = []
    for e in experts:
        print(f"\n== {e['name'].upper()} (fresh instance) ==")
        a = llm(e["instruction"] + "\nBe concrete. Max 5 sentences.")
        answers.append((e["name"], a))
        print(a)

    # Round 3 - the conductor synthesizes and verifies.
    print("\n== CONDUCTOR'S SYNTHESIS ==")
    print(llm(
        f"You are the conductor. Original task: {task}\n\n"
        + "\n\n".join(f"[{n}]\n{a}" for n, a in answers)
        + "\n\nSynthesize the expert reports into one recommendation. Flag any point where "
        "experts contradict each other. Max 6 sentences."
    ))


if __name__ == "__main__":
    main()