← Mission Control Updated from 2026-06-22-1114.md
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Snowflake Cost Optimization Loop

One active producer loop builds the Snowflake cost-management business. The current emphasis is converting demo assets into buyer conversations while keeping delivery tooling ready for trial, story, and approved account reviews.

Automation State

Active loop
snowflake-cost-optimization-builder-loop
Schedule
Hourly cron automation
Status
Active
Retired duplicate
snowflake-builder-thread-heartbeat
Status
Deleted to prevent two hourly Snowflake producers from selecting overlapping work, writing competing reports, or obscuring value attribution.

Latest Output

Latest report
snowflake-cost-optimization/reports/hourly/2026-06-22-1114.md
Completed
- Added `source_use_boundary` to `dashboard/cortex_cost_command_center.py` and inserted a `## Source Use Boundary` section into generated executive briefs.
Novelty
first required source-boundary section and source-label-aware validation for generated Snowflake executive briefs.
Human bottleneck
Lee should fill `snowflake-cost-optimization/data/personal-network-response-intake.csv` or `snowflake-cost-optimization/data/friday-network-reconciliation-template.csv` with the approximately 20 Friday personal-network touches, then run: ```bash snowflake-cost-optimization/.venv/bin/python snowflake-cost-optimization/dashboard/run_sales_ops_refresh.py ``` Before a buyer or partner demo where Snowflake-native AI credibility matters, run: ```bash snowflake-cost-optimization/.venv/bin/python snowflake-cost-optimization/dashboard/run_sales_ops_refresh.py --test-snow-connection ``` Only rows that validate as ready should be logged or worked.

Monday Response Queue

Waiting on real replies
0 ready
17 needs-fix
0 parked
0 duplicates
20 skipped blanks
Immediate step
If worksheet freshness is stale, rebuild before Lee enters responses. If ready rows exist, work only confirmed real outcomes. If ready rows are zero, fill real contact, response, next-action, and follow-up fields before logging.
Refresh status
PASS from snowflake-cost-optimization/reports/sales-ops-refresh.json
Worksheet
fresh
Demo readiness
Green for synthetic buyer demos. Use `Local CSV demo`, `Story test data`, or `SCO_DEMO trial mart` for pre-outreach and first-call demonstrations. Do not use `ACCOUNT_USAGE` output with a buyer until metadata access is approved and sensitive identifiers are reviewed.
Story validation
9/9 passed from snowflake-cost-optimization/reports/demo-readiness-report.md
Generated
2026-06-22T11:17:53-05:00
Source report
snowflake-cost-optimization/reports/response-action-queue.md

Tracker Follow-Up Queue

No live follow-up gaps
0 overdue
0 due today
0 upcoming
0 live missing follow-up date
29 placeholder setup gaps
Immediate step
Placeholder setup gaps remain; do not treat them as live missed follow-ups.
Tracker activity
0 live tracker rows; 29 placeholder rows
Source report
snowflake-cost-optimization/reports/followup-queue.md
Use
Shows tracker-derived follow-up gaps after sends, replies, referrals, or calls are logged.

Independent Review

Continue, but keep scope constrained
Latest score
11.7/15
Review report
snowflake-cost-optimization/reports/recent-cycle-review.md
Use
Checks whether recent automation is compounding business value or drifting while Lee-owned response data is pending.

Demo Stories

All discovered story scenarios have validation-plan coverage.

dataforge-growth-spike

DataForge Growth Spike

DataForge launched a new deterministic demo-data workflow.

Validation plan
Passed
Dashboard signal
`DF_PROD_WH` is the largest spend center and is growing through the week.; `DF_AGENT_WH` is a new agent-orchestration workload with meaningful cost but unclear guardrails.
Demo line
Lee can say: This is the pattern I expect in adolescent AI/data teams. The cost problem is not that the platform is useless. The problem is that successful workloads get expensive before finance, data leadership, and owners have a shared control model.
dataforge-serverless-surprise

DataForge Serverless Surprise

DataForge leadership thinks warehouse spend is under control because no single warehouse looks terrifying.

Validation plan
Passed
Dashboard signal
Warehouse spend is moderate and spread across normal workloads.; Serverless spend is larger than warehouse spend.
Demo line
Lee can say: The question is not whether Snowflake AI and serverless features are good. The question is whether finance can see who owns them, why they refresh, and what threshold turns experimentation into a business decision.
dataforge-zombie-warehouse

DataForge Zombie Warehouse

A DataForge experiment warehouse was created during a sprint to validate larger scenario generation.

Validation plan
Passed
Dashboard signal
`DF_EXPERIMENT_WH` dominates warehouse credits.; It has no auto-suspend and no resource monitor.
Demo line
Lee can say: This is why the first pass is valuable even before deep tuning. Some savings are not clever. They are governance gaps that need a controlled owner review and a same-day fix.

Buyer Impact

  • This improves buyer demo quality and delivery reliability by making AI-assisted executive briefs safer to reuse after demos.

Next Work

Agent
Continue narrowly only if the next cycle reacts to real response/tracker data or improves a concrete demo, delivery, validation, or reviewer path.
Lee
On Monday 2026-06-22, fill the Friday response worksheet, run validation, build the action queue, and only log confirmed real outcomes.
Demo prep
Select Story test data in the Cortex Cost Command Center before trial account validation.

Chief of Staff Visibility

Cycle Reports

Review snowflake-cost-optimization/reports/hourly/ for what each cron cycle changed and why it mattered.

Mission Status

Use status/snowflake-cost-optimization/status.md as the board-level Snowflake mission state.

Local Operating Status

Use snowflake-cost-optimization/status.md for the loop's working status, automation details, and next best action.

Loop Rules

  • Use AutoAgentFlow and the business-loop-steering skill.
  • Every cycle must state the business lever advanced and what is meaningfully new.
  • Prefer code, diagnostics, dashboards, trial validation, buyer-send support, and delivery automation over generic collateral.
  • Do not stop because Lee is needed; reduce send friction, improve the demo, strengthen proof, or improve execution instrumentation.
  • Chief of Staff reviews should compare cycle reports to actual revenue, outreach, demo, and delivery progress.