Omentir

Build the following focused alternative to Omentir. This is a deliberately limited personal or small-team substitute, not parity with the paid service.

WORKING SLICE
Import known LinkedIn prospect URLs, review fit against an explicit ICP, draft a connection note and maintain a manual reply-aware follow-up queue.

SETUP AND ARCHITECTURE
Use TypeScript, React and a Node server with SQLite for a single small workspace. Prerequisites: A local Node runtime, writable data directory, sample records and a documented backup/restore path. Before integrating anything, record actual versions and permissions, plus model files or provider limits only where used, in the README; make unavailable dependencies visible rather than simulating success.

DOMAIN MODEL AND INVARIANTS
Store Prospect, EvidenceField, ICPVersion, Draft and Outcome; fit scores explain which user-supplied fields matched and missing profile facts remain unknown.

IMPLEMENTATION CONTRACT
Use migrations and server-side validation; expose saved, pending and failed states. Keep each write atomic and reject stale edits using a revision number. Provide an input/setup view, the main work view, and a review/export view appropriate to this workflow. Preserve the last saved state if a job or save fails. Include empty, loading, permission-denied, partial and retryable-error states. Log identifiers and error categories without secret values or unnecessary private content.

APP-SPECIFIC BOUNDARY AND RECOVERY
Use manual actions or an explicitly authorized, documented provider integration only. Do not scrape hidden fields, evade platform limits or assume a third-party API removes account-policy risk.

ACCEPTANCE SCENARIO
Mark a prospect replied after a follow-up draft is queued; cancel its send suggestion and retain the conversation outcome without opening an automated outreach loop. Also reopen the app after an interrupted operation, confirm the saved record/export remains inspectable, and document the recovery action. These are implementation acceptance requirements, not a claim that this guide has been tested.

DELIVERY
Deliver a runnable repository with migrations or project-format versioning, a non-sensitive example, environment/permission setup, the exact manual acceptance steps, and a backup/export-and-restore walkthrough. Implement the working slice before optional integrations; list any deferred paid-product capabilities honestly. Do not add capabilities outside the working slice just to resemble the original product.

PROJECT RULES FOR AGENTS.md
Keep the domain invariants above executable at the write boundary. Propose scope changes before adding providers or permissions. Never fabricate source evidence, publish results, identity matches or successful delivery. Preserve user originals and require an explicit confirmation for destructive changes or external publication.

# AGENTS.md — Build guide for Omentir

## Project scope
Import known LinkedIn prospect URLs, review fit against an explicit ICP, draft a connection note and maintain a manual reply-aware follow-up queue.

Catalogue verdict: kinda. The personal loop is buildable in a sitting: score a list you already have, draft connection notes, queue sends, stop on replies. Live LinkedIn send is Unipile, a paid API, not a trivial key, and LinkedIn will still restrict a real account if you ignore the caps. The hosted product's own repo is already MIT with Docker Compose, so the interesting move is clone-and-run, not a from-scratch rebuild. That fork still needs Firebase, Unipile, and Gemini, which is ops. Keep paying if you want them to run those three, plus the three-bookings-a-week refund.
Use the implementation prompt below to define the deliverable. Complete each phase's acceptance checks before extending the scope.

## Working agreement
- Inspect the repository and its existing instructions before choosing paths, dependencies or commands. Keep one coherent stack and explain changes to the proposed architecture.
- Plan a vertical slice that accepts a real input and produces the useful output described below. Persist only the state the prompt calls for; respect memory-only and upstream-managed workflows. Use fixtures only when they are clearly labelled.
- After scaffolding, document the actual install, development, check and build commands in README and keep them synchronized with the package or project manifest. Do not report commands as successful unless they ran.
- Work in small steps. At handoff, list implemented flows, checks actually performed, remaining blockers, and any credentials or provider setup the owner must supply.
- Do not publish, spend money, contact customers, delete source data or run irreversible migrations without the project owner's authorization.

## Prerequisites
- Runtime and tools: TypeScript, React and a Node server with SQLite for a single small workspace.
- Before starting: A local Node runtime, writable data directory, sample records and a documented backup/restore path.

## Stack and architecture
- TypeScript, React and a Node server with SQLite for a single small workspace
- Data design: Store Prospect, EvidenceField, ICPVersion, Draft and Outcome; fit scores explain which user-supplied fields matched and missing profile facts remain unknown.
- Setup: A local Node runtime, writable data directory, sample records and a documented backup/restore path

## Security and data integrity
- Use migrations and server-side validation; expose saved, pending and failed states. Keep each write atomic and reject stale edits using a revision number.
- Use manual actions or an explicitly authorized, documented provider integration only. Do not scrape hidden fields, evade platform limits or assume a third-party API removes account-policy risk.
- Keep secrets outside client bundles and exported projects; document what leaves the device and make retention/deletion controls visible.

## Agent implementation rules
- Project rule — domain: Store Prospect, EvidenceField, ICPVersion, Draft and Outcome; fit scores explain which user-supplied fields matched and missing profile facts remain unknown.
- Project rule — scope and recovery: Use manual actions or an explicitly authorized, documented provider integration only. Do not scrape hidden fields, evade platform limits or assume a third-party API removes account-policy risk.
- Project rule — acceptance: Mark a prospect replied after a follow-up draft is queued; cancel its send suggestion and retain the conversation outcome without opening an automated outreach loop.
- Project rule — delivery: document real setup commands and permissions; do not claim a build, accuracy level, performance result or security certification that has not been demonstrated.

## Optional agent skills and references
- Recommended skill: [vercel-react-best-practices](https://github.com/vercel-labs/agent-skills/blob/main/skills/react-best-practices/SKILL.md) — keep the proposed React work/review views responsive and avoid unnecessary rendering or data-fetch waterfalls. Follow the maintainer's installation instructions and match its requirements to the chosen runtime.
- Recommended skill: [web-design-guidelines](https://github.com/vercel-labs/agent-skills/blob/main/skills/web-design-guidelines/SKILL.md) — review keyboard access, focus, validation, error recovery and the readable work/review interface or HTML report. Follow the maintainer's installation instructions and match its requirements to the chosen runtime.

Read the linked SKILL.md and its dependencies before adding a skill. Select only the skills matching this project's runtime and task; their documentation does not supply API access, credentials or approval to perform external actions. Pin the reviewed revision where the tool supports it. Follow the chosen agent's documented project-level installation mechanism.

## Distribution ideas
These are optional planning notes. Obtain the owner's approval before publishing or contacting anyone.
- Demonstrate this working slice using synthetic or explicitly authorized non-sensitive examples: Import known LinkedIn prospect URLs, review fit against an explicit ICP, draft a connection note and maintain a manual reply-aware follow-up queue.
- Share a synthetic example export and the acceptance walkthrough; keep real customer, health, financial and source data private: Mark a prospect replied after a follow-up draft is queued; cancel its send suggestion and retain the conversation outcome without opening an automated outreach loop.
- State the limits before asking someone to replace their existing tool: Use manual actions or an explicitly authorized, documented provider integration only. Do not scrape hidden fields, evade platform limits or assume a third-party API removes account-policy risk.

## Engineering roadmap
1. Phase 1 — Pin the working slice and create its example input: Import known LinkedIn prospect URLs, review fit against an explicit ICP, draft a connection note and maintain a manual reply-aware follow-up queue. Confirm setup: A local Node runtime, writable data directory, sample records and a documented backup/restore path.
2. Phase 2 — Implement persistence and write-time invariants before decorating the UI: Store Prospect, EvidenceField, ICPVersion, Draft and Outcome; fit scores explain which user-supplied fields matched and missing profile facts remain unknown.
3. Phase 3 — Connect the working view to real saved state. Use migrations and server-side validation; expose saved, pending and failed states. Keep each write atomic and reject stale edits using a revision number.
4. Phase 4 — Expose the app-specific limits and recovery path in context: Use manual actions or an explicitly authorized, documented provider integration only. Do not scrape hidden fields, evade platform limits or assume a third-party API removes account-policy risk.
5. Phase 5 — Walk through this concrete acceptance case and preserve its exported evidence: Mark a prospect replied after a follow-up draft is queued; cancel its send suggestion and retain the conversation outcome without opening an automated outreach loop. Finish the README and backup/restore instructions; report unfinished capabilities explicitly.

## Paid-product capabilities outside this build
- the three-bookings-a-week refund on the hosted plan
- someone else paying for and babysitting Unipile, Firebase, and Gemini
- daily invite caps already wired, so you do not have to invent account-safety defaults
- MCP and the Agent API already pointed at a running workspace
- a support line when a sending account gets restricted

## Implementation prompt
Build the following focused alternative to Omentir. This is a deliberately limited personal or small-team substitute, not parity with the paid service.

WORKING SLICE
Import known LinkedIn prospect URLs, review fit against an explicit ICP, draft a connection note and maintain a manual reply-aware follow-up queue.

SETUP AND ARCHITECTURE
Use TypeScript, React and a Node server with SQLite for a single small workspace. Prerequisites: A local Node runtime, writable data directory, sample records and a documented backup/restore path. Before integrating anything, record actual versions and permissions, plus model files or provider limits only where used, in the README; make unavailable dependencies visible rather than simulating success.

DOMAIN MODEL AND INVARIANTS
Store Prospect, EvidenceField, ICPVersion, Draft and Outcome; fit scores explain which user-supplied fields matched and missing profile facts remain unknown.

IMPLEMENTATION CONTRACT
Use migrations and server-side validation; expose saved, pending and failed states. Keep each write atomic and reject stale edits using a revision number. Provide an input/setup view, the main work view, and a review/export view appropriate to this workflow. Preserve the last saved state if a job or save fails. Include empty, loading, permission-denied, partial and retryable-error states. Log identifiers and error categories without secret values or unnecessary private content.

APP-SPECIFIC BOUNDARY AND RECOVERY
Use manual actions or an explicitly authorized, documented provider integration only. Do not scrape hidden fields, evade platform limits or assume a third-party API removes account-policy risk.

ACCEPTANCE SCENARIO
Mark a prospect replied after a follow-up draft is queued; cancel its send suggestion and retain the conversation outcome without opening an automated outreach loop. Also reopen the app after an interrupted operation, confirm the saved record/export remains inspectable, and document the recovery action. These are implementation acceptance requirements, not a claim that this guide has been tested.

DELIVERY
Deliver a runnable repository with migrations or project-format versioning, a non-sensitive example, environment/permission setup, the exact manual acceptance steps, and a backup/export-and-restore walkthrough. Implement the working slice before optional integrations; list any deferred paid-product capabilities honestly. Do not add capabilities outside the working slice just to resemble the original product.

PROJECT RULES FOR AGENTS.md
Keep the domain invariants above executable at the write boundary. Propose scope changes before adding providers or permissions. Never fabricate source evidence, publish results, identity matches or successful delivery. Preserve user originals and require an explicit confirmation for destructive changes or external publication.

## Completion evidence
Demonstrate the prompt's acceptance scenarios against the scoped workflow. Include setup from a clean checkout and failure recovery. Check persistence across restart and export/restore only for the state the prompt says to store; for memory-only tools, confirm that temporary content is discarded as specified. Record actual results and remaining limitations. A detailed plan alone does not establish a working replacement.