ltx-leadership-weekly-report

name	ltx-leadership-weekly-report
description	Generate the LTX weekly leadership Slack post covering Open Source (HuggingFace LTX-2 family), API (revenue, endpoints, leads, industries), and Studio Enterprise (contracts, pilots, LTX-model consumption share). Runs every Monday via n8n: BigQuery → JSON parse → Slack render → LLM TL;DR → Slack webhook. Use when: weekly exec update, leadership Slack channel automation, or when asked for 'last week's LTX numbers'.
tags	["reporting","weekly-metrics","ltx","oss","ltxvapi","ltxstudio","n8n","slack","leadership"]
compatibility	["n8n self-hosted or cloud (workflow uses native BigQuery, Anthropic, and Slack nodes)","GCP project access (`ltx-dwh-prod-processed`) for the n8n BigQuery credential","Anthropic API key (Claude Sonnet 4.5 recommended) for the TL;DR node","Slack incoming webhook configured for the destination channel"]
metadata	{"author":"aheden","version":"1.0","created":"2026-05-06"}

LTX Leadership Weekly Report

Overview

Weekly snapshot of LTX across all three product surfaces, posted every Monday morning to a leadership Slack channel:

• Open Source — HuggingFace download/likes momentum on LTX 2 / 2.3 / 2.3-fp8, plus community feedback themes
• API (LTXV API) — revenue split (Resellers / Contracts / Self-serve), new endpoints, top gen types, self-serve funnel, PQL/PEL counts, top inbound industries
• Studio Enterprise — active vs total contract orgs, new contracts/pilots, total + LTX-model token consumption with WoW%, share-of-video-tokens, share-of-gens

The post leads with an LLM-generated 2-3 sentence TL;DR that follows strict materiality and "no filler" rules — see n8n/tldr_system_prompt.md.

Architecture

Schedule Trigger (Mon 09:00 UTC)
    ↓
Run BigQuery       ← queries/weekly_report_combined.sql  (10 rows: section, data_json)
    ↓ (10 items)
Parse Sections     ← n8n/parse_sections.js               (1 item: { sections: {...} })
    ↓
Render Body        ← n8n/render_body.js                  (1 item: { header, body, ... })
    ↓
Generate TLDR      ← n8n/tldr_system_prompt.md           (Anthropic / OpenAI node)
    ↓
Combine            ← n8n/combine.js                      (1 item: { final_message, ... })
    ↓
Slack Message      (HTTP Request → incoming webhook)

Critical detail: the BigQuery script returns a single result set of 10 rows, each carrying one report block as a JSON-encoded string in data_json. n8n's native BigQuery node (jobs.query) only surfaces the last statement's output, so the original multi-statement script in dwh-data-model-transforms was rewritten into one SELECT ... UNION ALL ... here. KPI logic is unchanged; only the wrapping is different.

Repository layout

ltx-leadership-weekly-report/
├── SKILL.md                          ← this file
├── queries/
│   └── weekly_report_combined.sql    ← single-output SQL for n8n's BQ node
├── n8n/
│   ├── parse_sections.js             ← Code node #1 (parse 10 JSON rows → object)
│   ├── render_body.js                ← Code node #2 (render Slack body)
│   ├── tldr_system_prompt.md         ← System prompt for the LLM node
│   └── combine.js                    ← Code node #3 (TL;DR + body → Slack post)
└── references/
    └── report-template.md            ← visual reference for the rendered post

Instructions — first-time setup

1. Create credentials in n8n

• BigQuery service account — JSON key for a service account with bigquery.jobs.create and read on ltx-dwh-prod-processed.*. Set the credential's default project to ltx-dwh-explore (where the query bills against, no required dataset).
• Anthropic API key — for the TL;DR node.
• Slack incoming webhook — one URL per destination channel. Create the webhook at api.slack.com/apps → your bot → Incoming Webhooks. The webhook is bound to a specific channel; switching channels means a different webhook URL.

2. Build the workflow

Create the 7 nodes in this exact order, matching the names below (the names matter — combine.js references $('Render Body').first().json by name):

#	Node name	Type	Configuration
1	Schedule Trigger	n8n Schedule trigger	Mondays, 09:00 UTC
2	Run BigQuery	Google BigQuery → "Execute a SQL query"	Paste queries/weekly_report_combined.sql. Use Standard SQL.
3	Parse Sections	Code (JavaScript)	Paste n8n/parse_sections.js.
4	Render Body	Code (JavaScript)	Paste n8n/render_body.js.
5	Generate TLDR	Anthropic → "Message a model"	Model claude-sonnet-4-5, temperature 0.2, max tokens 300. System message: paste content between the BEGIN/END markers in n8n/tldr_system_prompt.md. User message: ={{ $json.body }}.
6	Combine	Code (JavaScript)	Paste n8n/combine.js.
7	Slack Message	HTTP Request	POST to your Slack webhook URL. Body type JSON: { "text": "{{ $json.final_message }}" }.

3. Test

Execute manually (Monday morning is fine, or any time — the SQL is parameterized off current_date). Inspect each node:

• Run BigQuery: 10 items, each with { section, data_json }.
• Parse Sections: 1 item with sections keyed by A_oss_summary … J_studio_summary.
• Render Body: header, body, week_start, week_end. Body should look like references/report-template.md.
• Generate TLDR: 2-3 sentences, numbers in *bold*, no em-dashes, no banned filler.
• Combine: final_message ready to post.
• Slack Message: posts to the channel.

If you switch LLM provider, the TL;DR field path may change — combine.js already tries the common Anthropic / OpenAI / LangChain shapes, but verify in the run log.

Knobs

Knob	Where	Current value	Why
Schedule	Schedule Trigger node	Mon 09:00 UTC	Reflects ISO-week ending the previous Sunday
Materiality threshold (general)	tldr_system_prompt.md	|WoW%| ≥ 25%	Below this is exec-noise
Materiality threshold (totals)	tldr_system_prompt.md	|WoW%| ≥ 15%	Total revenue / total tokens move slowly
PEL volatility	tldr_system_prompt.md	Always skip	Marketing PEL swings are noisy
Section coverage	tldr_system_prompt.md	TL;DR may omit quiet sections	Don't pad with "OSS was steady"
Bold rule (body)	render_body.js	Bold headline numbers only	Not WoW%, not "was X%" context
Direction arrow	render_body.js	↑ ↓ 0%	Visual scanning, no +/- chars
Em-dash policy	both	Banned everywhere	Avoids "AI slop" appearance

Constraints

DO NOT

• Run the original multi-statement weekly_report_combined.sql from dwh-data-model-transforms through n8n's BQ node — only the last block (J_studio_summary) will come out. Use queries/weekly_report_combined.sql here.
• Rename the n8n nodes "Render Body" or "Generate TLDR" without updating the references in combine.js.
• Add bolding to WoW percentages, "was X%" context, or non-numeric words. Body must follow the locked layout in references/report-template.md.
• Introduce em-dashes (—) or en-dashes (–) anywhere in the body or TL;DR. Use commas / parentheses / colons.
• Trust a single-week PEL move > ±50% — confirm with the body's other numbers before letting the LLM headline it.
• Hardcode the Slack webhook URL inside combine.js. Pass it via the Slack Message node only.

DO

• Update references/report-template.md whenever render_body.js layout changes — the template is the visual contract.
• Bump the Reference TL;DR block in tldr_system_prompt.md after every prompt change so we have a regression anchor.
• Keep queries/weekly_report_combined.sql in lockstep with dwh-data-model-transforms/queries/weekly_report_combined.sql. KPI changes there must be ported here.

References

• Source SQL (multi-statement, dev/EDA version): dwh-data-model-transforms/queries/weekly_report_combined.sql
• Spec doc with KPI definitions: dwh-data-model-transforms/queries/weekly_report_combined.md
• BigQuery cost: ~537 MB scanned per run (dry-run upper bound).