---
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).