Vibecode SalesTouch

You are building a lean indie version of SalesTouch.

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 =====
# SalesTouch indie build

## Goal

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

## Scope

Import or paste prospects, score them from user-supplied context, draft personalized outreach with an LLM, and track manual follow-ups in a local pipeline.

## 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:
- live LinkedIn and Sales Navigator searches and audience extraction
- authenticated messages, invitations, engagement, and publishing
- residential IP routing and resilient LinkedIn sessions
- human-paced queues, limits, cooldowns, and account safety controls
- multi-account orchestration, activity logs, and analytics

If those capabilities are essential, use SalesTouch 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 local LinkedIn outreach cockpit inspired by SalesTouch. Requirements:

- Node 22 + Express + better-sqlite3; one localhost web app with no accounts.
- Import prospects from CSV with name, company, role, LinkedIn URL, source,
  notes, and last-contact date; validate and deduplicate by LinkedIn URL.
- Let me paste profile, company, post, and conversation text into each record.
  Never crawl LinkedIn or read browser cookies.
- Store my offer and ICP rules in config.json. Use the OpenAI API to return a
  fit score, evidence, a personalized angle, a connection note under 300
  characters, a first DM, and one follow-up. Put OPENAI_API_KEY in .env.
- A kanban pipeline: new, researched, ready, contacted, replied, won, lost.
- A Today queue showing due follow-ups and a configurable manual daily limit.
- Each action opens the LinkedIn profile in a new tab and has copy buttons for
  the approved text. It must never click, send, invite, comment, or publish.
- Let me paste a new reply, show the complete conversation history, and draft
  a suggested response for approval.
- Log every status change and copied draft in SQLite; export prospects and
  activity as CSV. Never pretend a copied message was sent.
- Keep all data local. No cloud database, telemetry, background workers, or
  multi-user features.
- Explicitly out of scope: LinkedIn login or cookies, scraping, Sales Navigator
  automation, residential proxies, auto-sending, engagement, publishing,
  multi-account orchestration, and claims that this is account-safe automation.
- README: setup, CSV format, backup path, and an honest explanation that the
  tool replaces planning and drafting only, not SalesTouch's LinkedIn execution.

## Required capabilities

- OpenAI API key
- LinkedIn account
- manual profile and conversation input

## 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 SalesTouch.

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 =====
# SalesTouch indie build

## Goal

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

## Scope

Import or paste prospects, score them from user-supplied context, draft personalized outreach with an LLM, and track manual follow-ups in a local pipeline.

## 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:
- live LinkedIn and Sales Navigator searches and audience extraction
- authenticated messages, invitations, engagement, and publishing
- residential IP routing and resilient LinkedIn sessions
- human-paced queues, limits, cooldowns, and account safety controls
- multi-account orchestration, activity logs, and analytics

If those capabilities are essential, use SalesTouch 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 local LinkedIn outreach cockpit inspired by SalesTouch. Requirements:

- Node 22 + Express + better-sqlite3; one localhost web app with no accounts.
- Import prospects from CSV with name, company, role, LinkedIn URL, source,
  notes, and last-contact date; validate and deduplicate by LinkedIn URL.
- Let me paste profile, company, post, and conversation text into each record.
  Never crawl LinkedIn or read browser cookies.
- Store my offer and ICP rules in config.json. Use the OpenAI API to return a
  fit score, evidence, a personalized angle, a connection note under 300
  characters, a first DM, and one follow-up. Put OPENAI_API_KEY in .env.
- A kanban pipeline: new, researched, ready, contacted, replied, won, lost.
- A Today queue showing due follow-ups and a configurable manual daily limit.
- Each action opens the LinkedIn profile in a new tab and has copy buttons for
  the approved text. It must never click, send, invite, comment, or publish.
- Let me paste a new reply, show the complete conversation history, and draft
  a suggested response for approval.
- Log every status change and copied draft in SQLite; export prospects and
  activity as CSV. Never pretend a copied message was sent.
- Keep all data local. No cloud database, telemetry, background workers, or
  multi-user features.
- Explicitly out of scope: LinkedIn login or cookies, scraping, Sales Navigator
  automation, residential proxies, auto-sending, engagement, publishing,
  multi-account orchestration, and claims that this is account-safe automation.
- README: setup, CSV format, backup path, and an honest explanation that the
  tool replaces planning and drafting only, not SalesTouch's LinkedIn execution.

## Required capabilities

- OpenAI API key
- LinkedIn account
- manual profile and conversation input

## 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 SalesTouch.

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 =====
# SalesTouch product brief

## Problem

The local CRM and AI drafting layer are straightforward, but SalesTouch's core value is reliable access to LinkedIn's private network: authenticated sessions, live data extraction, residential IP routing, human-paced queues, limits, cooldowns, reconnection, and ongoing adaptation to platform changes. A one-shot clone either stops at manual copy and paste or becomes brittle automation that can put the LinkedIn account at risk.

## Product outcome

Import or paste prospects, score them from user-supplied context, draft personalized outreach with an LLM, and track manual follow-ups in a local pipeline.

## Target user

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

## Required capabilities

- OpenAI API key
- LinkedIn account
- manual profile and conversation input

## Explicit non-goals for v1

- live LinkedIn and Sales Navigator searches and audience extraction
- authenticated messages, invitations, engagement, and publishing
- residential IP routing and resilient LinkedIn sessions
- human-paced queues, limits, cooldowns, and account safety controls
- multi-account orchestration, activity logs, and analytics

## 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 local LinkedIn outreach cockpit inspired by SalesTouch. Requirements:

- Node 22 + Express + better-sqlite3; one localhost web app with no accounts.
- Import prospects from CSV with name, company, role, LinkedIn URL, source,
  notes, and last-contact date; validate and deduplicate by LinkedIn URL.
- Let me paste profile, company, post, and conversation text into each record.
  Never crawl LinkedIn or read browser cookies.
- Store my offer and ICP rules in config.json. Use the OpenAI API to return a
  fit score, evidence, a personalized angle, a connection note under 300
  characters, a first DM, and one follow-up. Put OPENAI_API_KEY in .env.
- A kanban pipeline: new, researched, ready, contacted, replied, won, lost.
- A Today queue showing due follow-ups and a configurable manual daily limit.
- Each action opens the LinkedIn profile in a new tab and has copy buttons for
  the approved text. It must never click, send, invite, comment, or publish.
- Let me paste a new reply, show the complete conversation history, and draft
  a suggested response for approval.
- Log every status change and copied draft in SQLite; export prospects and
  activity as CSV. Never pretend a copied message was sent.
- Keep all data local. No cloud database, telemetry, background workers, or
  multi-user features.
- Explicitly out of scope: LinkedIn login or cookies, scraping, Sales Navigator
  automation, residential proxies, auto-sending, engagement, publishing,
  multi-account orchestration, and claims that this is account-safe automation.
- README: setup, CSV format, backup path, and an honest explanation that the
  tool replaces planning and drafting only, not SalesTouch's LinkedIn execution.

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

# SalesTouch indie build

## Goal

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

## Scope

Import or paste prospects, score them from user-supplied context, draft personalized outreach with an LLM, and track manual follow-ups in a local pipeline.

## 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:
- live LinkedIn and Sales Navigator searches and audience extraction
- authenticated messages, invitations, engagement, and publishing
- residential IP routing and resilient LinkedIn sessions
- human-paced queues, limits, cooldowns, and account safety controls
- multi-account orchestration, activity logs, and analytics

If those capabilities are essential, use SalesTouch 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 local LinkedIn outreach cockpit inspired by SalesTouch. Requirements:

- Node 22 + Express + better-sqlite3; one localhost web app with no accounts.
- Import prospects from CSV with name, company, role, LinkedIn URL, source,
  notes, and last-contact date; validate and deduplicate by LinkedIn URL.
- Let me paste profile, company, post, and conversation text into each record.
  Never crawl LinkedIn or read browser cookies.
- Store my offer and ICP rules in config.json. Use the OpenAI API to return a
  fit score, evidence, a personalized angle, a connection note under 300
  characters, a first DM, and one follow-up. Put OPENAI_API_KEY in .env.
- A kanban pipeline: new, researched, ready, contacted, replied, won, lost.
- A Today queue showing due follow-ups and a configurable manual daily limit.
- Each action opens the LinkedIn profile in a new tab and has copy buttons for
  the approved text. It must never click, send, invite, comment, or publish.
- Let me paste a new reply, show the complete conversation history, and draft
  a suggested response for approval.
- Log every status change and copied draft in SQLite; export prospects and
  activity as CSV. Never pretend a copied message was sent.
- Keep all data local. No cloud database, telemetry, background workers, or
  multi-user features.
- Explicitly out of scope: LinkedIn login or cookies, scraping, Sales Navigator
  automation, residential proxies, auto-sending, engagement, publishing,
  multi-account orchestration, and claims that this is account-safe automation.
- README: setup, CSV format, backup path, and an honest explanation that the
  tool replaces planning and drafting only, not SalesTouch's LinkedIn execution.

## Required capabilities

- OpenAI API key
- LinkedIn account
- manual profile and conversation input

## 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.

# SalesTouch product brief

## Problem

The local CRM and AI drafting layer are straightforward, but SalesTouch's core value is reliable access to LinkedIn's private network: authenticated sessions, live data extraction, residential IP routing, human-paced queues, limits, cooldowns, reconnection, and ongoing adaptation to platform changes. A one-shot clone either stops at manual copy and paste or becomes brittle automation that can put the LinkedIn account at risk.

## Product outcome

Import or paste prospects, score them from user-supplied context, draft personalized outreach with an LLM, and track manual follow-ups in a local pipeline.

## Target user

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

## Required capabilities

- OpenAI API key
- LinkedIn account
- manual profile and conversation input

## Explicit non-goals for v1

- live LinkedIn and Sales Navigator searches and audience extraction
- authenticated messages, invitations, engagement, and publishing
- residential IP routing and resilient LinkedIn sessions
- human-paced queues, limits, cooldowns, and account safety controls
- multi-account orchestration, activity logs, and analytics

## 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 local LinkedIn outreach cockpit inspired by SalesTouch. Requirements:

- Node 22 + Express + better-sqlite3; one localhost web app with no accounts.
- Import prospects from CSV with name, company, role, LinkedIn URL, source,
  notes, and last-contact date; validate and deduplicate by LinkedIn URL.
- Let me paste profile, company, post, and conversation text into each record.
  Never crawl LinkedIn or read browser cookies.
- Store my offer and ICP rules in config.json. Use the OpenAI API to return a
  fit score, evidence, a personalized angle, a connection note under 300
  characters, a first DM, and one follow-up. Put OPENAI_API_KEY in .env.
- A kanban pipeline: new, researched, ready, contacted, replied, won, lost.
- A Today queue showing due follow-ups and a configurable manual daily limit.
- Each action opens the LinkedIn profile in a new tab and has copy buttons for
  the approved text. It must never click, send, invite, comment, or publish.
- Let me paste a new reply, show the complete conversation history, and draft
  a suggested response for approval.
- Log every status change and copied draft in SQLite; export prospects and
  activity as CSV. Never pretend a copied message was sent.
- Keep all data local. No cloud database, telemetry, background workers, or
  multi-user features.
- Explicitly out of scope: LinkedIn login or cookies, scraping, Sales Navigator
  automation, residential proxies, auto-sending, engagement, publishing,
  multi-account orchestration, and claims that this is account-safe automation.
- README: setup, CSV format, backup path, and an honest explanation that the
  tool replaces planning and drafting only, not SalesTouch's LinkedIn execution.

## 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 SalesTouch 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