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.
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.
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: |
| 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 | |
| """ |