"""Generate blameless post-mortems for Snowdrop incidents."""
from __future__ import annotations
from datetime import datetime, timezone
from typing import Any
TOOL_META: dict[str, Any] = {
"name": "post_mortem_generator",
"description": "Creates structured post-mortem Markdown with action items and lessons learned.",
"inputSchema": {
"type": "object",
"properties": {
"incident": {
"type": "object",
"description": (
"Incident dictionary containing title, severity, timeline, root cause, contributing"
" factors, detection_method, resolution, and duration_minutes."
),
}
},
"required": ["incident"],
},
"outputSchema": {
"type": "object",
"properties": {
"status": {"type": "string"},
"data": {"type": "object"},
"timestamp": {"type": "string"},
},
},
}
def post_mortem_generator(incident: dict[str, Any], **_: Any) -> dict[str, Any]:
"""Render a blameless post-mortem for the provided incident."""
try:
required = [
"title",
"severity",
"timeline",
"root_cause",
"contributing_factors",
"detection_method",
"resolution",
"duration_minutes",
]
missing = [field for field in required if field not in incident]
if missing:
raise ValueError(f"Incident missing fields: {', '.join(missing)}")
timeline_md = "\n".join(
f"- {item.get('timestamp')}: {item.get('event')}" for item in incident.get("timeline", [])
)
summary = (
f"Incident '{incident['title']}' ({incident['severity']}) lasted "
f"{incident['duration_minutes']} minutes and was detected via {incident['detection_method']}."
)
impact = "Affected systems: " + ", ".join(incident.get("affected_systems", [])) if incident.get("affected_systems") else "Impact details pending."
action_items = _derive_actions(
incident.get("contributing_factors", []),
incident.get("resolution", ""),
)
lessons = [
"Codify detection improvements",
"Validate runbooks against scenario",
]
markdown = f"""# Post-Mortem: {incident['title']}
**Severity:** {incident['severity']} \\
**Duration:** {incident['duration_minutes']} minutes \\
**Detection:** {incident['detection_method']} \\
**Summary:** {summary}
## Impact
{impact}
## Timeline
{timeline_md}
## Root Cause
{incident['root_cause']}
## Resolution
{incident['resolution']}
## Action Items
{_actions_md(action_items)}
## Lessons Learned
- {lessons[0]}
- {lessons[1]}
"""
data = {
"post_mortem_md": markdown.strip(),
"action_items": action_items,
}
return {
"status": "success",
"data": data,
"timestamp": datetime.now(timezone.utc).isoformat(),
}
except Exception as exc: # noqa: BLE001
_log_lesson("post_mortem_generator", str(exc))
return {
"status": "error",
"data": {"error": str(exc)},
"timestamp": datetime.now(timezone.utc).isoformat(),
}
def _derive_actions(factors: list[str], resolution: str) -> list[dict[str, Any]]:
actions: list[dict[str, Any]] = []
for factor in factors:
actions.append(
{
"action": f"Address contributing factor: {factor}",
"owner": "Snowdrop Ops",
"due_date": None,
"status": "pending",
}
)
if resolution:
actions.append(
{
"action": f"Backtest and automate fix: {resolution[:120]}",
"owner": "Automation Guild",
"due_date": None,
"status": "pending",
}
)
return actions
def _actions_md(items: list[dict[str, Any]]) -> str:
if not items:
return "- No follow-up required"
lines = []
for item in items:
lines.append(
f"- [ ] {item['action']} (Owner: {item['owner']}, Status: {item['status']})"
)
return "\n".join(lines)
def _log_lesson(skill_name: str, error: str) -> None:
with open("logs/lessons.md", "a", encoding="utf-8") as handle:
handle.write(f"- [{datetime.now(timezone.utc).isoformat()}] {skill_name}: {error}\n")
