Vibecode Infracost Cloud

You are building a lean indie version of Infracost Cloud.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# Infracost Cloud indie build

## Goal

Build the smallest trustworthy replacement for the core Infracost Cloud workflow for one developer or a tiny team.

## Scope

Watch Terraform pull requests, estimate the AWS monthly cost delta for a bounded resource set, run basic FinOps checks, and update one GitHub pull-request comment.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- AWS, Azure, and Google Cloud coverage across the full Infracost resource catalog
- Terragrunt, CloudFormation, AWS CDK, modules, and complex Terraform expression coverage
- custom price books, negotiated discounts, and organization-specific pricing
- Issue Explorer, campaigns, AutoFix, dashboards, audit trails, and team workflows
- enterprise identity, security, support, and broad CI/CD integrations

If those capabilities are essential, use Infracost instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build a self-hosted GitHub App called CostLens in an empty repository.
Use Python 3.12, FastAPI, SQLite, python-hcl2, httpx, PyJWT, and Docker Compose; do not offer alternative stacks.
The core loop is: when a pull request changes Terraform, estimate the AWS monthly cost delta, run basic FinOps checks, and update one PR comment.
Authenticate as a GitHub App using an app ID, installation token flow, webhook secret, and private key stored outside the repository.
Handle pull_request opened, synchronize, and reopened events, and verify every webhook signature before processing.
Analyze the base commit and head commit separately so the comment shows previous cost, proposed cost, and monthly delta.
Parse Terraform HCL locally and support a deliberately bounded first set: EC2 Linux on-demand instances, EBS gp2/gp3 volumes, and RDS on-demand instances plus allocated storage.
Use the public AWS Price List Bulk files as the pricing source, cache downloaded price data, and record the source publication timestamp.
Assume 730 hours per month for hourly resources and show every assumption in the result.
Never price an unsupported or ambiguous resource as zero; list it as unsupported and exclude it from totals.
Support count when it resolves to a literal integer; mark for_each, unknown expressions, modules, and unresolved variables as unsupported in v1.
Add configurable required-tag checks and a configurable monthly cost-increase warning threshold.
Post a Markdown PR comment with total before, total after, delta, the five largest increases, policy warnings, unsupported resources, and pricing timestamp.
Use a hidden HTML marker so repeated webhook deliveries update the existing comment instead of creating duplicates.
Store repository settings and analysis history in SQLite, with a small admin page for recent PR analyses and configuration.
Add retry and timeout handling for GitHub and AWS pricing downloads, structured logs, and a health endpoint.
Ship .env.example, a Dockerfile, docker-compose.yml, database migrations, and one-command local startup.
Include unit tests for pricing normalization and Terraform resource extraction plus one end-to-end webhook happy path using fixtures.
Document the minimum GitHub App permissions and events in the README, including pull requests read, contents read, and issues write.
Do not send Terraform source, secrets, or repository contents to any AI or third-party analysis API.
Deliberately leave out Azure, GCP, Terragrunt, CloudFormation, CDK, negotiated discounts, Savings Plans, Reserved Instances, and usage-based cost forecasting.
Deliberately leave out SSO, billing, organization dashboards, campaigns, AutoFix, enterprise RBAC, and GitLab or Azure DevOps integrations.
Finish by running the tests, starting the stack locally, processing the included sample webhook, and listing the exact commands used.

## Required capabilities

- GitHub App credentials and a public HTTPS webhook endpoint
- Docker with persistent storage for SQLite
- Internet access to GitHub and the public AWS Price List files

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a lean indie version of Infracost Cloud.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== README.md =====
# Infracost Cloud indie build

## Goal

Build the smallest trustworthy replacement for the core Infracost Cloud workflow for one developer or a tiny team.

## Scope

Watch Terraform pull requests, estimate the AWS monthly cost delta for a bounded resource set, run basic FinOps checks, and update one GitHub pull-request comment.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- AWS, Azure, and Google Cloud coverage across the full Infracost resource catalog
- Terragrunt, CloudFormation, AWS CDK, modules, and complex Terraform expression coverage
- custom price books, negotiated discounts, and organization-specific pricing
- Issue Explorer, campaigns, AutoFix, dashboards, audit trails, and team workflows
- enterprise identity, security, support, and broad CI/CD integrations

If those capabilities are essential, use Infracost instead of pretending the gap is solved.

===== AGENTS.md =====
# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

===== BUILD_PLAN.md =====
# Build plan

## Original build brief

Build a self-hosted GitHub App called CostLens in an empty repository.
Use Python 3.12, FastAPI, SQLite, python-hcl2, httpx, PyJWT, and Docker Compose; do not offer alternative stacks.
The core loop is: when a pull request changes Terraform, estimate the AWS monthly cost delta, run basic FinOps checks, and update one PR comment.
Authenticate as a GitHub App using an app ID, installation token flow, webhook secret, and private key stored outside the repository.
Handle pull_request opened, synchronize, and reopened events, and verify every webhook signature before processing.
Analyze the base commit and head commit separately so the comment shows previous cost, proposed cost, and monthly delta.
Parse Terraform HCL locally and support a deliberately bounded first set: EC2 Linux on-demand instances, EBS gp2/gp3 volumes, and RDS on-demand instances plus allocated storage.
Use the public AWS Price List Bulk files as the pricing source, cache downloaded price data, and record the source publication timestamp.
Assume 730 hours per month for hourly resources and show every assumption in the result.
Never price an unsupported or ambiguous resource as zero; list it as unsupported and exclude it from totals.
Support count when it resolves to a literal integer; mark for_each, unknown expressions, modules, and unresolved variables as unsupported in v1.
Add configurable required-tag checks and a configurable monthly cost-increase warning threshold.
Post a Markdown PR comment with total before, total after, delta, the five largest increases, policy warnings, unsupported resources, and pricing timestamp.
Use a hidden HTML marker so repeated webhook deliveries update the existing comment instead of creating duplicates.
Store repository settings and analysis history in SQLite, with a small admin page for recent PR analyses and configuration.
Add retry and timeout handling for GitHub and AWS pricing downloads, structured logs, and a health endpoint.
Ship .env.example, a Dockerfile, docker-compose.yml, database migrations, and one-command local startup.
Include unit tests for pricing normalization and Terraform resource extraction plus one end-to-end webhook happy path using fixtures.
Document the minimum GitHub App permissions and events in the README, including pull requests read, contents read, and issues write.
Do not send Terraform source, secrets, or repository contents to any AI or third-party analysis API.
Deliberately leave out Azure, GCP, Terragrunt, CloudFormation, CDK, negotiated discounts, Savings Plans, Reserved Instances, and usage-based cost forecasting.
Deliberately leave out SSO, billing, organization dashboards, campaigns, AutoFix, enterprise RBAC, and GitLab or Azure DevOps integrations.
Finish by running the tests, starting the stack locally, processing the included sample webhook, and listing the exact commands used.

## Required capabilities

- GitHub App credentials and a public HTTPS webhook endpoint
- Docker with persistent storage for SQLite
- Internet access to GitHub and the public AWS Price List files

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

===== .env.example =====
# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

You are building a production product version of Infracost Cloud.

Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt.

===== PRODUCT.md =====
# Infracost Cloud product brief

## Problem

The core developer loop is a credible weekend build if the replacement is intentionally narrow: one GitHub App, Terraform on AWS, a small resource catalog, public list prices, basic tag and cost guardrails, and one idempotent pull-request comment. A faithful Infracost Cloud replacement is much harder because the paid product combines broad IaC and multi-cloud coverage with continuously maintained pricing data, policy depth, custom price books, organization-wide visibility, workflows, and enterprise integrations.

## Product outcome

Watch Terraform pull requests, estimate the AWS monthly cost delta for a bounded resource set, run basic FinOps checks, and update one GitHub pull-request comment.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- GitHub App credentials and a public HTTPS webhook endpoint
- Docker with persistent storage for SQLite
- Internet access to GitHub and the public AWS Price List files

## Explicit non-goals for v1

- AWS, Azure, and Google Cloud coverage across the full Infracost resource catalog
- Terragrunt, CloudFormation, AWS CDK, modules, and complex Terraform expression coverage
- custom price books, negotiated discounts, and organization-specific pricing
- Issue Explorer, campaigns, AutoFix, dashboards, audit trails, and team workflows
- enterprise identity, security, support, and broad CI/CD integrations

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

===== ARCHITECTURE.md =====
# Architecture

## Starting brief

Build a self-hosted GitHub App called CostLens in an empty repository.
Use Python 3.12, FastAPI, SQLite, python-hcl2, httpx, PyJWT, and Docker Compose; do not offer alternative stacks.
The core loop is: when a pull request changes Terraform, estimate the AWS monthly cost delta, run basic FinOps checks, and update one PR comment.
Authenticate as a GitHub App using an app ID, installation token flow, webhook secret, and private key stored outside the repository.
Handle pull_request opened, synchronize, and reopened events, and verify every webhook signature before processing.
Analyze the base commit and head commit separately so the comment shows previous cost, proposed cost, and monthly delta.
Parse Terraform HCL locally and support a deliberately bounded first set: EC2 Linux on-demand instances, EBS gp2/gp3 volumes, and RDS on-demand instances plus allocated storage.
Use the public AWS Price List Bulk files as the pricing source, cache downloaded price data, and record the source publication timestamp.
Assume 730 hours per month for hourly resources and show every assumption in the result.
Never price an unsupported or ambiguous resource as zero; list it as unsupported and exclude it from totals.
Support count when it resolves to a literal integer; mark for_each, unknown expressions, modules, and unresolved variables as unsupported in v1.
Add configurable required-tag checks and a configurable monthly cost-increase warning threshold.
Post a Markdown PR comment with total before, total after, delta, the five largest increases, policy warnings, unsupported resources, and pricing timestamp.
Use a hidden HTML marker so repeated webhook deliveries update the existing comment instead of creating duplicates.
Store repository settings and analysis history in SQLite, with a small admin page for recent PR analyses and configuration.
Add retry and timeout handling for GitHub and AWS pricing downloads, structured logs, and a health endpoint.
Ship .env.example, a Dockerfile, docker-compose.yml, database migrations, and one-command local startup.
Include unit tests for pricing normalization and Terraform resource extraction plus one end-to-end webhook happy path using fixtures.
Document the minimum GitHub App permissions and events in the README, including pull requests read, contents read, and issues write.
Do not send Terraform source, secrets, or repository contents to any AI or third-party analysis API.
Deliberately leave out Azure, GCP, Terragrunt, CloudFormation, CDK, negotiated discounts, Savings Plans, Reserved Instances, and usage-based cost forecasting.
Deliberately leave out SSO, billing, organization dashboards, campaigns, AutoFix, enterprise RBAC, and GitLab or Azure DevOps integrations.
Finish by running the tests, starting the stack locally, processing the included sample webhook, and listing the exact commands used.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

===== AGENTS.md =====
# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

===== MILESTONES.md =====
# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

===== OPERATIONS.md =====
# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted Infracost Cloud capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# Infracost Cloud indie build

## Goal

Build the smallest trustworthy replacement for the core Infracost Cloud workflow for one developer or a tiny team.

## Scope

Watch Terraform pull requests, estimate the AWS monthly cost delta for a bounded resource set, run basic FinOps checks, and update one GitHub pull-request comment.

## Quick start

1. Install the documented dependencies.
2. Copy `.env.example` to `.env`.
3. Run the development command chosen during implementation.
4. Complete the acceptance checks in `BUILD_PLAN.md`.

## Honest limits

This build deliberately does not replace:
- AWS, Azure, and Google Cloud coverage across the full Infracost resource catalog
- Terragrunt, CloudFormation, AWS CDK, modules, and complex Terraform expression coverage
- custom price books, negotiated discounts, and organization-specific pricing
- Issue Explorer, campaigns, AutoFix, dashboards, audit trails, and team workflows
- enterprise identity, security, support, and broad CI/CD integrations

If those capabilities are essential, use Infracost instead of pretending the gap is solved.

# Agent instructions

- Optimize for a working, understandable weekend build.
- Prefer the fewest moving parts that satisfy the brief.
- Do not invent cryptography, security guarantees, APIs, or compliance claims.
- Keep secrets out of source control and logs.
- Add focused tests for destructive, security-sensitive, and data-loss paths.
- Run the project checks before declaring the build complete.
- Record any deliberate shortcut in the README under "Tradeoffs".

# Build plan

## Original build brief

Build a self-hosted GitHub App called CostLens in an empty repository.
Use Python 3.12, FastAPI, SQLite, python-hcl2, httpx, PyJWT, and Docker Compose; do not offer alternative stacks.
The core loop is: when a pull request changes Terraform, estimate the AWS monthly cost delta, run basic FinOps checks, and update one PR comment.
Authenticate as a GitHub App using an app ID, installation token flow, webhook secret, and private key stored outside the repository.
Handle pull_request opened, synchronize, and reopened events, and verify every webhook signature before processing.
Analyze the base commit and head commit separately so the comment shows previous cost, proposed cost, and monthly delta.
Parse Terraform HCL locally and support a deliberately bounded first set: EC2 Linux on-demand instances, EBS gp2/gp3 volumes, and RDS on-demand instances plus allocated storage.
Use the public AWS Price List Bulk files as the pricing source, cache downloaded price data, and record the source publication timestamp.
Assume 730 hours per month for hourly resources and show every assumption in the result.
Never price an unsupported or ambiguous resource as zero; list it as unsupported and exclude it from totals.
Support count when it resolves to a literal integer; mark for_each, unknown expressions, modules, and unresolved variables as unsupported in v1.
Add configurable required-tag checks and a configurable monthly cost-increase warning threshold.
Post a Markdown PR comment with total before, total after, delta, the five largest increases, policy warnings, unsupported resources, and pricing timestamp.
Use a hidden HTML marker so repeated webhook deliveries update the existing comment instead of creating duplicates.
Store repository settings and analysis history in SQLite, with a small admin page for recent PR analyses and configuration.
Add retry and timeout handling for GitHub and AWS pricing downloads, structured logs, and a health endpoint.
Ship .env.example, a Dockerfile, docker-compose.yml, database migrations, and one-command local startup.
Include unit tests for pricing normalization and Terraform resource extraction plus one end-to-end webhook happy path using fixtures.
Document the minimum GitHub App permissions and events in the README, including pull requests read, contents read, and issues write.
Do not send Terraform source, secrets, or repository contents to any AI or third-party analysis API.
Deliberately leave out Azure, GCP, Terragrunt, CloudFormation, CDK, negotiated discounts, Savings Plans, Reserved Instances, and usage-based cost forecasting.
Deliberately leave out SSO, billing, organization dashboards, campaigns, AutoFix, enterprise RBAC, and GitLab or Azure DevOps integrations.
Finish by running the tests, starting the stack locally, processing the included sample webhook, and listing the exact commands used.

## Required capabilities

- GitHub App credentials and a public HTTPS webhook endpoint
- Docker with persistent storage for SQLite
- Internet access to GitHub and the public AWS Price List files

## Delivery order

1. Scaffold the smallest runnable application and document its commands.
2. Implement the primary data model and core workflow.
3. Add validation, safe failure states, and persistence.
4. Cover the critical path with automated tests.
5. Exercise a clean install from the README and fix every missing step.

## Done when

- A new user can go from clone to first successful workflow using only the README.
- The core workflow works without paid infrastructure unless the brief requires it.
- Tests cover the highest-risk behavior.
- Known limitations are explicit rather than hidden.

# Copy to .env and document every variable when it is introduced.
# Never put real credentials in this file.

APP_ENV=development
# Add only values required by the selected implementation.

# Infracost Cloud product brief

## Problem

The core developer loop is a credible weekend build if the replacement is intentionally narrow: one GitHub App, Terraform on AWS, a small resource catalog, public list prices, basic tag and cost guardrails, and one idempotent pull-request comment. A faithful Infracost Cloud replacement is much harder because the paid product combines broad IaC and multi-cloud coverage with continuously maintained pricing data, policy depth, custom price books, organization-wide visibility, workflows, and enterprise integrations.

## Product outcome

Watch Terraform pull requests, estimate the AWS monthly cost delta for a bounded resource set, run basic FinOps checks, and update one GitHub pull-request comment.

## Target user

A serious builder who needs a maintainable product foundation rather than a one-off demo.

## Required capabilities

- GitHub App credentials and a public HTTPS webhook endpoint
- Docker with persistent storage for SQLite
- Internet access to GitHub and the public AWS Price List files

## Explicit non-goals for v1

- AWS, Azure, and Google Cloud coverage across the full Infracost resource catalog
- Terragrunt, CloudFormation, AWS CDK, modules, and complex Terraform expression coverage
- custom price books, negotiated discounts, and organization-specific pricing
- Issue Explorer, campaigns, AutoFix, dashboards, audit trails, and team workflows
- enterprise identity, security, support, and broad CI/CD integrations

## Success criteria

- The primary workflow is measurable end to end.
- Setup is reproducible in a clean environment.
- Failure, recovery, and support paths are documented.
- Product claims match what the implementation actually guarantees.

# Architecture

## Starting brief

Build a self-hosted GitHub App called CostLens in an empty repository.
Use Python 3.12, FastAPI, SQLite, python-hcl2, httpx, PyJWT, and Docker Compose; do not offer alternative stacks.
The core loop is: when a pull request changes Terraform, estimate the AWS monthly cost delta, run basic FinOps checks, and update one PR comment.
Authenticate as a GitHub App using an app ID, installation token flow, webhook secret, and private key stored outside the repository.
Handle pull_request opened, synchronize, and reopened events, and verify every webhook signature before processing.
Analyze the base commit and head commit separately so the comment shows previous cost, proposed cost, and monthly delta.
Parse Terraform HCL locally and support a deliberately bounded first set: EC2 Linux on-demand instances, EBS gp2/gp3 volumes, and RDS on-demand instances plus allocated storage.
Use the public AWS Price List Bulk files as the pricing source, cache downloaded price data, and record the source publication timestamp.
Assume 730 hours per month for hourly resources and show every assumption in the result.
Never price an unsupported or ambiguous resource as zero; list it as unsupported and exclude it from totals.
Support count when it resolves to a literal integer; mark for_each, unknown expressions, modules, and unresolved variables as unsupported in v1.
Add configurable required-tag checks and a configurable monthly cost-increase warning threshold.
Post a Markdown PR comment with total before, total after, delta, the five largest increases, policy warnings, unsupported resources, and pricing timestamp.
Use a hidden HTML marker so repeated webhook deliveries update the existing comment instead of creating duplicates.
Store repository settings and analysis history in SQLite, with a small admin page for recent PR analyses and configuration.
Add retry and timeout handling for GitHub and AWS pricing downloads, structured logs, and a health endpoint.
Ship .env.example, a Dockerfile, docker-compose.yml, database migrations, and one-command local startup.
Include unit tests for pricing normalization and Terraform resource extraction plus one end-to-end webhook happy path using fixtures.
Document the minimum GitHub App permissions and events in the README, including pull requests read, contents read, and issues write.
Do not send Terraform source, secrets, or repository contents to any AI or third-party analysis API.
Deliberately leave out Azure, GCP, Terragrunt, CloudFormation, CDK, negotiated discounts, Savings Plans, Reserved Instances, and usage-based cost forecasting.
Deliberately leave out SSO, billing, organization dashboards, campaigns, AutoFix, enterprise RBAC, and GitLab or Azure DevOps integrations.
Finish by running the tests, starting the stack locally, processing the included sample webhook, and listing the exact commands used.

## Boundaries

Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors.

## Production baseline

- Configuration: validated at startup with safe local defaults where possible.
- Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives.
- Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions.
- Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts.
- Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists.
- Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test.

## Decision records

For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.

# Agent instructions

- Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code.
- Implement milestone by milestone; keep each change reviewable and leave the application runnable.
- Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present.
- Never invent cryptography or silently weaken a requirement to make a test pass.
- Use provider interfaces for external services and deterministic fakes in tests.
- Add migrations and rollback or recovery notes for persistent data changes.
- Log useful operational context without credentials, tokens, passwords, or personal data.
- Update documentation and run all checks before completing a milestone.

# Delivery milestones

## M0 — Decisions and scaffold

- Confirm the runtime, persistence model, threat boundaries, and deployment target.
- Create a reproducible local environment and continuous checks.

## M1 — Core workflow

- Implement the smallest end-to-end product path with validation and tests.
- Keep integrations behind interfaces.

## M2 — Trust layer

- Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards.
- Test abuse cases and destructive operations.

## M3 — Operability

- Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration.

## M4 — Release gate

- Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise.
- Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.

# Operations

## Before release

- Validate configuration and secrets at startup.
- Define backup, restore, and rollback procedures and test them.
- Document logs, error tracking, health signals, and alert ownership.
- Set dependency update and vulnerability review expectations.

## Incident checklist

1. Contain the issue without destroying evidence or user data.
2. Record the timeline and affected scope.
3. Rotate exposed secrets and revoke compromised sessions or credentials.
4. Restore from a verified source when needed.
5. Document the root cause, remediation, and regression test.

## Launch constraint

Do not market omitted Infracost Cloud capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

$ choose a build depth, inspect the files, then open the complete pack in your agent