Vibecode SharpAPI

You are building a lean indie version of SharpAPI.

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 =====
# SharpAPI indie build

## Goal

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

## Scope

Wrap OpenAI or Anthropic structured outputs in a small local API: one prompt file and one JSON schema per task, a SQLite job table for async processing, and a worker that retries failures.

## 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:
- tuned prompts per use case, tested against messy real-world inputs
- response contracts that stay stable when the underlying models change
- the managed async queue with polling, throttling, and rate limits
- ready SDKs for PHP/Laravel, Node, and Python
- 80+ language coverage tested per endpoint

If those capabilities are essential, use OpenAI Node SDK 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 me a personal AI text-workflow API to replace SharpAPI. Requirements:

- Node 22 + Express bound to localhost, better-sqlite3 for storage, no frontend.
  This is an API my other projects call with curl or fetch.
- Three endpoints to start: POST /summarize, POST /categorize against my own
  category list in categories.json, and POST /parse-resume (PDF or text in,
  structured JSON out).
- Every endpoint calls OpenAI structured outputs with a JSON schema kept in
  schemas/<task>.json and a prompt kept in prompts/<task>.md, so I can tune
  prompts without touching code. Adding a task means adding one schema + prompt pair.
- Async by default: POST returns a job id, GET /jobs/:id returns status and result.
  Jobs live in a SQLite table with created/started/finished timestamps.
- A worker loop processes jobs with concurrency 2, retries twice on 429 and 5xx
  with backoff, and stores the error text on the job instead of dropping it.
- Parse PDFs with pdf-parse before sending text to the model.
- API key from .env. No accounts, no telemetry, binds to localhost only.
- Out of scope: billing, rate-limit tiers, packaged SDKs, and the other 27
  SharpAPI endpoints. Do not build an admin UI.
- README: setup, .env keys, one example curl per endpoint, and a rough per-call
  cost note.

## Required capabilities

- OpenAI or Anthropic API key
- a JSON schema per task for structured outputs
- a job queue (BullMQ + Redis, or a SQLite job table)
- real sample inputs to calibrate each prompt against

## 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 SharpAPI.

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 =====
# SharpAPI indie build

## Goal

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

## Scope

Wrap OpenAI or Anthropic structured outputs in a small local API: one prompt file and one JSON schema per task, a SQLite job table for async processing, and a worker that retries failures.

## 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:
- tuned prompts per use case, tested against messy real-world inputs
- response contracts that stay stable when the underlying models change
- the managed async queue with polling, throttling, and rate limits
- ready SDKs for PHP/Laravel, Node, and Python
- 80+ language coverage tested per endpoint

If those capabilities are essential, use OpenAI Node SDK 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 me a personal AI text-workflow API to replace SharpAPI. Requirements:

- Node 22 + Express bound to localhost, better-sqlite3 for storage, no frontend.
  This is an API my other projects call with curl or fetch.
- Three endpoints to start: POST /summarize, POST /categorize against my own
  category list in categories.json, and POST /parse-resume (PDF or text in,
  structured JSON out).
- Every endpoint calls OpenAI structured outputs with a JSON schema kept in
  schemas/<task>.json and a prompt kept in prompts/<task>.md, so I can tune
  prompts without touching code. Adding a task means adding one schema + prompt pair.
- Async by default: POST returns a job id, GET /jobs/:id returns status and result.
  Jobs live in a SQLite table with created/started/finished timestamps.
- A worker loop processes jobs with concurrency 2, retries twice on 429 and 5xx
  with backoff, and stores the error text on the job instead of dropping it.
- Parse PDFs with pdf-parse before sending text to the model.
- API key from .env. No accounts, no telemetry, binds to localhost only.
- Out of scope: billing, rate-limit tiers, packaged SDKs, and the other 27
  SharpAPI endpoints. Do not build an admin UI.
- README: setup, .env keys, one example curl per endpoint, and a rough per-call
  cost note.

## Required capabilities

- OpenAI or Anthropic API key
- a JSON schema per task for structured outputs
- a job queue (BullMQ + Redis, or a SQLite job table)
- real sample inputs to calibrate each prompt against

## 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 SharpAPI.

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 =====
# SharpAPI product brief

## Problem

Any single endpoint here, summarize a text, categorize a product, parse a resume, is one LLM call with a decent prompt and a JSON schema, so the piece you actually need is often an evening of work. What you will not rebuild in a weekend is the catalog: 30+ endpoints with tuned prompts, response contracts that survive model upgrades, an async job queue with polling, and maintained SDKs. Rebuild the two or three endpoints you use, pay when you need the shelf.

## Product outcome

Wrap OpenAI or Anthropic structured outputs in a small local API: one prompt file and one JSON schema per task, a SQLite job table for async processing, and a worker that retries failures.

## Target user

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

## Required capabilities

- OpenAI or Anthropic API key
- a JSON schema per task for structured outputs
- a job queue (BullMQ + Redis, or a SQLite job table)
- real sample inputs to calibrate each prompt against

## Explicit non-goals for v1

- tuned prompts per use case, tested against messy real-world inputs
- response contracts that stay stable when the underlying models change
- the managed async queue with polling, throttling, and rate limits
- ready SDKs for PHP/Laravel, Node, and Python
- 80+ language coverage tested per endpoint

## 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 me a personal AI text-workflow API to replace SharpAPI. Requirements:

- Node 22 + Express bound to localhost, better-sqlite3 for storage, no frontend.
  This is an API my other projects call with curl or fetch.
- Three endpoints to start: POST /summarize, POST /categorize against my own
  category list in categories.json, and POST /parse-resume (PDF or text in,
  structured JSON out).
- Every endpoint calls OpenAI structured outputs with a JSON schema kept in
  schemas/<task>.json and a prompt kept in prompts/<task>.md, so I can tune
  prompts without touching code. Adding a task means adding one schema + prompt pair.
- Async by default: POST returns a job id, GET /jobs/:id returns status and result.
  Jobs live in a SQLite table with created/started/finished timestamps.
- A worker loop processes jobs with concurrency 2, retries twice on 429 and 5xx
  with backoff, and stores the error text on the job instead of dropping it.
- Parse PDFs with pdf-parse before sending text to the model.
- API key from .env. No accounts, no telemetry, binds to localhost only.
- Out of scope: billing, rate-limit tiers, packaged SDKs, and the other 27
  SharpAPI endpoints. Do not build an admin UI.
- README: setup, .env keys, one example curl per endpoint, and a rough per-call
  cost note.

## 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 SharpAPI capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.

# SharpAPI indie build

## Goal

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

## Scope

Wrap OpenAI or Anthropic structured outputs in a small local API: one prompt file and one JSON schema per task, a SQLite job table for async processing, and a worker that retries failures.

## 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:
- tuned prompts per use case, tested against messy real-world inputs
- response contracts that stay stable when the underlying models change
- the managed async queue with polling, throttling, and rate limits
- ready SDKs for PHP/Laravel, Node, and Python
- 80+ language coverage tested per endpoint

If those capabilities are essential, use OpenAI Node SDK 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 me a personal AI text-workflow API to replace SharpAPI. Requirements:

- Node 22 + Express bound to localhost, better-sqlite3 for storage, no frontend.
  This is an API my other projects call with curl or fetch.
- Three endpoints to start: POST /summarize, POST /categorize against my own
  category list in categories.json, and POST /parse-resume (PDF or text in,
  structured JSON out).
- Every endpoint calls OpenAI structured outputs with a JSON schema kept in
  schemas/<task>.json and a prompt kept in prompts/<task>.md, so I can tune
  prompts without touching code. Adding a task means adding one schema + prompt pair.
- Async by default: POST returns a job id, GET /jobs/:id returns status and result.
  Jobs live in a SQLite table with created/started/finished timestamps.
- A worker loop processes jobs with concurrency 2, retries twice on 429 and 5xx
  with backoff, and stores the error text on the job instead of dropping it.
- Parse PDFs with pdf-parse before sending text to the model.
- API key from .env. No accounts, no telemetry, binds to localhost only.
- Out of scope: billing, rate-limit tiers, packaged SDKs, and the other 27
  SharpAPI endpoints. Do not build an admin UI.
- README: setup, .env keys, one example curl per endpoint, and a rough per-call
  cost note.

## Required capabilities

- OpenAI or Anthropic API key
- a JSON schema per task for structured outputs
- a job queue (BullMQ + Redis, or a SQLite job table)
- real sample inputs to calibrate each prompt against

## 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.

# SharpAPI product brief

## Problem

Any single endpoint here, summarize a text, categorize a product, parse a resume, is one LLM call with a decent prompt and a JSON schema, so the piece you actually need is often an evening of work. What you will not rebuild in a weekend is the catalog: 30+ endpoints with tuned prompts, response contracts that survive model upgrades, an async job queue with polling, and maintained SDKs. Rebuild the two or three endpoints you use, pay when you need the shelf.

## Product outcome

Wrap OpenAI or Anthropic structured outputs in a small local API: one prompt file and one JSON schema per task, a SQLite job table for async processing, and a worker that retries failures.

## Target user

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

## Required capabilities

- OpenAI or Anthropic API key
- a JSON schema per task for structured outputs
- a job queue (BullMQ + Redis, or a SQLite job table)
- real sample inputs to calibrate each prompt against

## Explicit non-goals for v1

- tuned prompts per use case, tested against messy real-world inputs
- response contracts that stay stable when the underlying models change
- the managed async queue with polling, throttling, and rate limits
- ready SDKs for PHP/Laravel, Node, and Python
- 80+ language coverage tested per endpoint

## 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 me a personal AI text-workflow API to replace SharpAPI. Requirements:

- Node 22 + Express bound to localhost, better-sqlite3 for storage, no frontend.
  This is an API my other projects call with curl or fetch.
- Three endpoints to start: POST /summarize, POST /categorize against my own
  category list in categories.json, and POST /parse-resume (PDF or text in,
  structured JSON out).
- Every endpoint calls OpenAI structured outputs with a JSON schema kept in
  schemas/<task>.json and a prompt kept in prompts/<task>.md, so I can tune
  prompts without touching code. Adding a task means adding one schema + prompt pair.
- Async by default: POST returns a job id, GET /jobs/:id returns status and result.
  Jobs live in a SQLite table with created/started/finished timestamps.
- A worker loop processes jobs with concurrency 2, retries twice on 429 and 5xx
  with backoff, and stores the error text on the job instead of dropping it.
- Parse PDFs with pdf-parse before sending text to the model.
- API key from .env. No accounts, no telemetry, binds to localhost only.
- Out of scope: billing, rate-limit tiers, packaged SDKs, and the other 27
  SharpAPI endpoints. Do not build an admin UI.
- README: setup, .env keys, one example curl per endpoint, and a rough per-call
  cost note.

## 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 SharpAPI 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