Andrew Maguire andrewm4894

mine below as gh markdown so it can be pasted straight into the sprint issue if want

Retro (Oct 19 – 30)

Inbox ranking: default-sort experiment ran and model sort lost to control on actions, PRs started and top-10 opens, so ended it early. replacing it with a new recommended sort experiment (below)
Inbox ranking: first prod nn run promoted and serving, xgboost retired #120687, 90-day lookback #120684, model sort out to everyone
Inbox for agents: open/action/fix probs and lifts in the inbox mcp tools #123005, cleaned up and tightened the tool descriptions #126008, #126019, new prioritizing-my-inbox skill for a personal top n #125376 with a light nudge to it from the tool descriptions
In

Slack-triggered agents: loops vs scouts, on one realistic use case

Optional FYI / worked comparison to go with the Slack-triggered-loops PRs (#79521 backend, #79535 desktop) and the SherlockHog knowledge pack (SherlockHog#163).

TL;DR: loops is the right home for Sherlock now. Scouts should get the same shape later, on primitives shared with loops rather than a second copy. This doc is what that gap actually is, so we don't have to re-derive it.

The use case

Signals scout: brand mentions

You are a focused scout for what the world is saying about PostHog in public. An Octolens social-listening feed monitors brand keywords across X/Twitter, Reddit, LinkedIn, YouTube, and podcasts, posts matches into the #brand-mentions Slack channel, and that channel is synced into the data warehouse. Your job is to read recent mentions and turn the ones that matter into signals for a human to act on — and most things won't matter, which is fine. An empty findings list is a real outcome; re-emitting a mention you already surfaced is worse than emitting nothing.

Signals scout: MCP agent feedback

You are a focused scout for the PostHog MCP server itself. AI agents (Claude Code, Cursor, and others) that use the PostHog MCP file constructive feedback through the server's agent-feedback tool whenever a tool got in their way — a wrong or surprising result, an unclear description, a confusing input schema, an unwieldy output, a missing capability, an unhelpful error, or misleading instructions. Each submission lands as a mcp feedback submitted event. Your job is to turn that stream into actionable signals for the MCP team: spot recurring friction themes and agent-blocking patterns, and emit only when a theme clears the confidence bar. An empty findings list is a real

	L001: --------------------------------------------------------------------------------
	L002:
	L003: AVAILABLE TOOLS: 1
	L004:
	L005: web_search(max_results?: integer, query?: any)
	L006: Look up the factoid on the open web when you need citations, external context, or to verify new details.
	L007:
	L008: --------------------------------------------------------------------------------
	L009:
	L010: INPUT:

	#!/usr/bin/env bash

	scope="production"

	# stop on all errors
	set -e

	if [ $UID -ne 0 ]
	then
	echo >&2 "Hey! sudo me: sudo ${0}"


	# node ar 1min
	template: ml_1min_node_ar
	on: anomaly_detection.anomaly_rate
	class: Anomaly
	type: System
	component: Node
	lookup: average -1m foreach anomaly_rate
	units: %
	every: 30s

	#!/usr/bin/python
	"""
	A python script to run many "airflow run dags trigger {dag} -e {execution_datetime}" commands via the airflow rest api.

	Example usage:

	python airflow_trigger_dags.py --dag 'dev_dag' --start '2021-10-01 00:00:01' --end '2021-10-31 00:00:01'
	python airflow_trigger_dags.py -d 'dev_dag' -s '2022-05-20 00:00:01' -e '2022-05-24 00:00:01'

	Example usage to just trigger dag for now: