Alvey Automation
Download · Methodology Pack v1.0

Operational methodology for shipping high-stakes automation.

Ten patterns and ten reference implementations developed running multi-million-dollar/yr automation in production through Claude Code. Every pattern has either caught a real bug or replaced one that did.

$149 one-time · 14-day refund · lifetime updates
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The silent bug that compounds for three weeks

You ship an automation upgrade. The deploy goes green. Tests pass. The first day's run looks clean. Three weeks later, someone notices the numbers are off — and now you're reconstructing months of records, trying to figure out which silent bug shipped when.

Most automation tutorials are written by people who built toy projects. They tell you how to ship; they don't tell you how to keep shipping when the cost of a silent bug is "you have to reconcile months of records."

The discipline that actually keeps automation safe is operational, not architectural. Audit-after-ship. Daily reconciliation. Read-only credentials. Allow-list discipline. Idempotent retries. Kill-switch endpoints. Pre-deploy verification that exercises external APIs, not just /health.

Every one of these patterns has a story. They came from real production. They've each caught real bugs. This pack is the codified, reusable version.

What's in the pack

  • 92-page PDF — 10 chapters, each with motivation, pattern, worked example, common pitfalls, and "when not to use."
  • 28-file zip of runnable code — reference implementations of every pattern, with a 50/50 passing pytest suite proving they work.
  • Templates — PROJECT_HANDOFF.md, MEMORY.md hierarchy, per-topic memory templates.
  • Production-ready pre-commit config — gitleaks + detect-secrets + pip-audit + lock-file regen + standard hygiene.

The ten chapters:

  • 1. Operational Charter — eight standing rules to apply before shipping anything that touches money.
  • 2. Audit-After-Ship — temp secret-gated webhook pattern + 5-bug anonymized case study.
  • 3. Project Handoff Template — fillable template + a worked fictional example.
  • 4. Memory Hierarchy — how to layer auto-loaded memory across long-running AI sessions.
  • 5. Kill-Switch Pattern — stop a misbehaving cron in 30 seconds at 11 PM Sunday.
  • 6. OAuth Scaffold — generic OAuth 2.0 with provider-quirk notes.
  • 7. Idempotent Retry Pattern — at-most-once external writes, race-safe.
  • 8. Credential Rotation — 8-step playbook validated end-to-end on real-world rotation.
  • 9. Chart-of-Accounts Drift — startup-time validation that hardcoded IDs haven't drifted.
  • 10. Vendor Anomaly Detection — 90-day median × N baseline that generalizes well beyond accounting.

Who this is for

You should buy this pack if:

  • You're a solo founder, indie dev, or small-team engineer building automation that handles money, customer data, accounting, ERPs, ecommerce platforms, or anything where silent failures compound.
  • You've shipped to production at least a few times and felt the "is this going to be wrong in three weeks" anxiety.
  • You want operational disciplines that scale, not theoretical advice from people who've never run anything in production.
  • You're using AI-assisted development (Claude Code, Cursor, Copilot) and want patterns that survive the iteration speed it gives you.

You should NOT buy this pack if:

  • You're building toy projects or prototypes.
  • You haven't shipped to production yet.
  • You're looking for boilerplate code more than methodology.

FAQ

I'm not building anything that touches money. Is this still useful?
The patterns generalize. Anomaly detection works for API latency or backup sizes. Kill-switches work for any cron-driven job. Audit-after-ship works for any deploy where you'd regret a silent bug. If 'silent bug compounds before anyone notices' describes your work, the methodology applies.
Is the code Python-only?
The reference implementations are Python (FastAPI, SQLAlchemy). The patterns are language-agnostic. The pre-commit hook config covers gitleaks/detect-secrets which work on any language; pip-audit and pip-compile are Python-specific but easily replaced with npm audit, cargo audit, etc.
Will this work with Cursor / GitHub Copilot, not just Claude Code?
Yes. The Memory Hierarchy chapter is pitched at Claude Code's auto-loaded memory layer specifically, but the underlying pattern (MEMORY.md index + topic files + per-project context) works with any AI assistant that can read files. Adapt the auto-load mechanism to your tool.
Are there updates?
Yes. Buyers receive updates automatically when new versions ship. Future versions will add patterns as they prove out in production.
Refunds?
If the pack doesn't deliver — for any reason — email [email protected] within 14 days for a full refund. No friction.
Can I use the code commercially?
Yes. Code is MIT-licensed. Use it in commercial projects, commercial SaaS, anywhere. The PDF documentation is for the purchaser's use; please don't redistribute it.
How long is the PDF?
92 pages. Each chapter is roughly 8-10 pages, formatted for reading not skimming.

Stop catching silent bugs three weeks late.

92 pages, 28 runnable code files, lifetime updates, MIT-licensed code.

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