Status: In Review
Risk: Tier 2
Stop losing AI coding work inside chat threads.
Turn Codex, Claude Code, Cursor, and local agents into a governed workflow system: every task gets a record, every risky action gets a gate, and every run leaves a review packet.
The AI Programming Factory gives technical operators the doctrine, templates, artifact examples, acceptance tests, and 30 / 60 / 90 sequence for turning agent work into a governed software-factory loop.
- Task ledger
- Approval gates
- Review packets
- Cost records
- Golden runs
- 30 / 60 / 90 roadmap
TASK-0042 -> APPROVAL GATE -> REVIEW PACKET -> COST RECORD -> PROMOTION DECISION
Owner review required
Tests run
Rollback path
Workflow: docs-update
Estimated cost: $0.42
Candidate: reusable skill
The strongest proof is the record a workflow leaves behind.
The factory is not a longer prompt. It is the operating layer that turns agent work into visible, reviewable, recoverable records.
TASK-0042
Add model usage telemetry
Risk tier 2 / owner review required
Owner, status, acceptance criteria, artifact links, and next decision stay attached to the work.
External writes, spending, deployments, credentials, and public publishing stop for owner review.
Summary, files changed, checks run, risks remaining, rollback path, and owner decision.
Model, workflow, estimated cost, value signal, and routing lesson for the next run.
Built from live operating records, not theory.
This kit was produced from the same loop it teaches: request -> task -> plan -> approval -> build -> review packet -> decision log -> cost record -> promotion decision.
Source record
DI-0012 / Launch funnel readiness
Track: Operator / Launch. Status: Active until gated delivery, Stripe webhook, beehiiv automation, and live PostHog evidence are verified.
Workflow evidence
Landing-page rebuild loop
Request captured, offer proof revised, counter QA added, analytics events aligned, and delivery blockers kept separate from market validation.
Decision rule
Do not scale traffic before the trust layer is clean.
The public funnel now separates artifact proof, preview claims, paid checkout, and unresolved external evidence instead of pretending the system is already validated.
Most AI work is still handled like a pile of clever fragments.
Before
- Scattered chat threads
- Agent roleplay
- Premature automation
- Risky external actions
- Invisible model spend
- No proof of quality
After
- Recorded tasks, plans, and decisions
- Worker profiles with tools, limits, and output contracts
- Manual run -> runbook -> skill -> script -> workflow
- Approval gates before publishing, spending, deployment, or API writes
- Model usage records tied to workflow value
- Acceptance tests, review packets, rollback paths, and golden runs
The difference is not intelligence. The difference is operational structure.
Install the loop before adding autonomy.
Request -> Task Record -> Context Manifest -> Plan -> Risk Classification -> Approval Gate -> Execution -> Tests / Checks -> Review Packet -> Decision Log -> Cost Record -> Promotion Decision
-
01
Intake
Request, task record, context manifest, and plan.
-
02
Control
Risk classification and approval gate before important action.
-
03
Execution
Implementation, tests, checks, and review packet.
-
04
Learning
Decision log, cost record, and promotion decision.
Use the coding agents. Do not confuse them with the factory.
They do the work. The factory preserves the work.
Execution surfaces are useful, but they do not give you a durable task ledger, review-packet standard, approval model, artifact record, or promotion rule.
They give agents a gateway. The factory gives operators a control system.
Orchestration needs an operating standard around it: records, risk gates, cost logs, rollback paths, and decisions that survive the run.
They give reusable instructions. The factory gives reusable evidence.
The kit ties prompts to task state, tests, approval gates, artifacts, cost telemetry, and workflow maturity.
They teach concepts. The factory gives operating artifacts.
You get the doctrine, template set, acceptance tests, and 30 / 60 / 90 sequence for implementing the loop in a real repo.
The AI Programming Factory Implementation Kit
A premium operating manual and template system for turning AI coding-agent work into governed, inspectable, repeatable software workflows.
Delivery package
Main Doctrine PDF plus standalone implementation templates
Format: PDF + Markdown / CSV / Notion-compatible templates. Delivery: instant digital download. Updates: included for 12 months after purchase.
Record layer
- Main Doctrine PDF
- Task ledger template
- Context manifest template
- Artifact manifest template
- Example records from the Digital Infrastructure build
Control layer
- Worker profile template
- Approval gate template
- Review packet template
- Model usage / cost record template
- Public-positioning and safety controls
Promotion layer
- Hardcode candidate checklist
- Golden-run checklist
- Factory acceptance tests
- 30 / 60 / 90 implementation roadmap
Build records and gates before automating anything important.
Build the record layer
Create the repo, canonical docs, task ledger, approval records, review packet template, decision log, model usage record, worker profiles, and first supervised self-improvement run.
Make repeated work reusable
Expand workflow library, refine skills, track cost, preserve golden runs, and document successful loops.
Expand carefully
Add read-only integrations, draft-only product/content workflows, tool adapter specs, cost routing, security enforcement, and the first controlled automation.
Choose the first setup track after the doctrine.
The next product layer is the Factory Starter Pack: a setup path that turns the doctrine into the first repo-native operating loop for the tool stack you already use.
Get The AI Programming Factory Implementation Kit.
The paid kit comes after the proof: operating doctrine, templates, records, tests, and roadmap for technical operators building governed AI workflows.
1. Claim the free preview
2. Inspect the artifact model
3. Buy the full implementation kit
This is an informational and operational planning resource. It is not legal, financial, tax, cybersecurity, platform-compliance, employment, or professional engineering advice. It does not guarantee revenue, productivity gains, autonomous business operation, or business outcomes.
Questions operators ask before they build.
Is this just a prompt pack?
No. Prompt patterns can help, but this is an operating model: records, gates, templates, tests, review packets, cost logs, and promotion rules.
Why not just use ChatGPT, Claude, Cursor, or Codex directly?
Use them. The kit shows how to build the operating layer around them so their work becomes traceable, reviewable, permissioned, cost-aware, and repeatable.
Does this only work with Codex?
No. Codex, Claude Code, Cursor, OpenClaw, GitHub workflows, CLI tools, and local agents are execution surfaces. The operating layer is tool-neutral.
What do I receive after checkout?
The Main Doctrine PDF plus standalone implementation templates for the task ledger, context manifest, approval gate, review packet, cost record, worker profile, artifact manifest, checklists, acceptance tests, roadmap, and sample records.
Why does this cost $197?
The price is for the implementation asset set, not a short PDF: doctrine, templates, tests, checklists, examples, and the 30 / 60 / 90 sequence for installing governed AI coding workflows without rebuilding the operating model from scratch.
Are the templates separate files or only inside the PDF?
The offer is framed as PDF plus standalone implementation templates in Markdown / CSV / Notion-compatible formats so buyers can adapt the records directly inside their own repo or workspace.
Can I use this with my current repo?
Yes. The operating layer is meant to sit beside an existing repo, docs folder, task system, or local coding-agent workflow. You can start with the record templates before changing any automation.
Will I need API keys or paid tools?
No paid tool is required to understand or start the loop. Tool-specific integrations, API usage, and automation should come later, after records, gates, review packets, and rollback paths are working manually.
What is the refund process?
Use the contact link in the receipt or email [email protected] within 7 days. Refund reviews follow the posted refund policy for digital products.
What is included in the free preview?
The factory loop, sample task ledger, approval-gate sample, review-packet sample, cost-record sample, and 30 / 60 / 90 implementation map.
How does the founding counter work?
The public count is backed by server-side claim records. It increments only after a valid claim is created. Duplicate claims do not reduce remaining inventory. The counter does not randomly decrement and does not reset on refresh.
Does the counter reset?
No. It reads the server-side count for the launch offer and updates after real claim creation.
Does this guarantee revenue or productivity?
No. It does not guarantee revenue, profit, productivity gains, business outcomes, safe unsupervised operation, or platform compliance.
Do I need to know how to code?
You should be comfortable with technical workflows, GitHub or local files, command-line or IDE tools, and systems thinking.
What should I build first?
Build the first working loop: request -> task -> context -> plan -> approval -> implementation -> tests -> review packet -> decision log -> cost record.