kin/core/followup.py

"""
Kin follow-up generator — analyzes pipeline output and creates follow-up tasks.
Runs a PM agent to parse results and produce actionable task list.
"""

import json
import sqlite3

from core import models
from core.context_builder import format_prompt, PROMPTS_DIR


def _collect_pipeline_output(conn: sqlite3.Connection, task_id: str) -> str:
    """Collect all pipeline step outputs for a task into a single string."""
    rows = conn.execute(
        """SELECT agent_role, output_summary, success
           FROM agent_logs WHERE task_id = ? ORDER BY created_at""",
        (task_id,),
    ).fetchall()
    if not rows:
        return ""
    parts = []
    for r in rows:
        status = "OK" if r["success"] else "FAILED"
        parts.append(f"=== {r['agent_role']} [{status}] ===")
        parts.append(r["output_summary"] or "(no output)")
        parts.append("")
    return "\n".join(parts)


def _next_task_id(conn: sqlite3.Connection, project_id: str) -> str:
    """Generate the next sequential task ID for a project."""
    prefix = project_id.upper()
    existing = models.list_tasks(conn, project_id=project_id)
    max_num = 0
    for t in existing:
        tid = t["id"]
        if tid.startswith(prefix + "-"):
            try:
                num = int(tid.split("-", 1)[1])
                max_num = max(max_num, num)
            except ValueError:
                pass
    return f"{prefix}-{max_num + 1:03d}"


def generate_followups(
    conn: sqlite3.Connection,
    task_id: str,
    dry_run: bool = False,
) -> list[dict]:
    """Analyze pipeline output and create follow-up tasks.

    1. Collects all agent_logs output for the task
    2. Runs followup agent (claude -p) to analyze and propose tasks
    3. Creates tasks in DB with parent_task_id = task_id

    Returns list of created task dicts.
    """
    task = models.get_task(conn, task_id)
    if not task:
        return []

    project_id = task["project_id"]
    project = models.get_project(conn, project_id)
    if not project:
        return []

    pipeline_output = _collect_pipeline_output(conn, task_id)
    if not pipeline_output:
        return []

    # Build context for followup agent
    language = project.get("language", "ru")
    context = {
        "project": {
            "id": project["id"],
            "name": project["name"],
            "path": project["path"],
            "tech_stack": project.get("tech_stack"),
            "language": language,
        },
        "task": {
            "id": task["id"],
            "title": task["title"],
            "status": task["status"],
            "priority": task["priority"],
            "brief": task.get("brief"),
            "spec": task.get("spec"),
        },
        "previous_output": pipeline_output,
    }

    prompt = format_prompt(context, "followup")

    if dry_run:
        return [{"_dry_run": True, "_prompt": prompt}]

    # Run followup agent
    from agents.runner import _run_claude, _try_parse_json

    result = _run_claude(prompt, model="sonnet")
    output = result.get("output", "")

    # Parse the task list from output
    parsed = _try_parse_json(output)
    if not isinstance(parsed, list):
        # Maybe it's wrapped in a dict
        if isinstance(parsed, dict):
            parsed = parsed.get("tasks") or parsed.get("followups") or []
        else:
            return []

    # Create tasks in DB
    created = []
    for item in parsed:
        if not isinstance(item, dict) or "title" not in item:
            continue
        new_id = _next_task_id(conn, project_id)
        brief = item.get("brief")
        brief_dict = {"source": f"followup:{task_id}"}
        if item.get("type"):
            brief_dict["route_type"] = item["type"]
        if brief:
            brief_dict["description"] = brief

        t = models.create_task(
            conn, new_id, project_id,
            title=item["title"],
            priority=item.get("priority", 5),
            parent_task_id=task_id,
            brief=brief_dict,
        )
        created.append(t)

    # Log the followup generation
    models.log_agent_run(
        conn, project_id, "followup_pm", "generate_followups",
        task_id=task_id,
        output_summary=json.dumps(
            [{"id": t["id"], "title": t["title"]} for t in created],
            ensure_ascii=False,
        ),
        success=True,
    )

    return created