andrewm4894

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.

andrewm4894

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

andrewm4894

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:

andrewm4894

#!/usr/bin/env bash

scope="production"

# stop on all errors
set -e

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

andrewm4894

# 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

andrewm4894

andrewm4894

andrewm4894

#!/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:

andrewm4894

import sqlite3
from sqlite3 import Error

 
def create_connection(db_file):
    """ create a database connection to the SQLite database
        specified by the db_file
    :param db_file: database file
    :return: Connection object or None
    """

andrewm4894

sudo apt-get update

# install python3
#sudo apt-get install python3.7
#sudo apt install python3
sudo apt-get install python3-pip
sudo update-alternatives --install /usr/bin/python python /usr/bin/python3 10

# update
sudo apt-get update