Careerflow

Build the following focused alternative to Careerflow. Implement the focused workflow below first; the verdict is not evidence of a completed or production-certified build.

WORKING SLICE
Track job applications and maintain an evidence bank that feeds truthful resume and LinkedIn profile improvement checklists.

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 Application, JobSnapshot, ExperienceEvidence, SuggestedEdit and ReviewDecision; every new achievement or metric must cite user evidence or remain a question.

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
Keep job text snapshots and application deadlines editable. Do not scrape authenticated profiles or auto-apply; suggestions cannot promise ATS success or employment outcomes.

ACCEPTANCE SCENARIO
Compare a job asking for Kubernetes against a resume with no such experience; suggest a skills gap instead of adding Kubernetes to the resume or profile. 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 Careerflow

## Project scope
Track job applications and maintain an evidence bank that feeds truthful resume and LinkedIn profile improvement checklists.

Catalogue verdict: yes. The core loop is small enough for a capable coding agent to produce a useful local version in one sitting. For Careerflow, organize applications and create evidence-grounded resume and LinkedIn improvement checklists. The hard boundary is browser tooling, templates, coaching content, ai workflows, and hosted sync, plus data, distribution, and coaching.
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 Application, JobSnapshot, ExperienceEvidence, SuggestedEdit and ReviewDecision; every new achievement or metric must cite user evidence or remain a question.
- 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.
- Keep job text snapshots and application deadlines editable. Do not scrape authenticated profiles or auto-apply; suggestions cannot promise ATS success or employment outcomes.
- 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 Application, JobSnapshot, ExperienceEvidence, SuggestedEdit and ReviewDecision; every new achievement or metric must cite user evidence or remain a question.
- Project rule — scope and recovery: Keep job text snapshots and application deadlines editable. Do not scrape authenticated profiles or auto-apply; suggestions cannot promise ATS success or employment outcomes.
- Project rule — acceptance: Compare a job asking for Kubernetes against a resume with no such experience; suggest a skills gap instead of adding Kubernetes to the resume or profile.
- 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: Track job applications and maintain an evidence bank that feeds truthful resume and LinkedIn profile improvement checklists.
- Share a synthetic example export and the acceptance walkthrough; keep real customer, health, financial and source data private: Compare a job asking for Kubernetes against a resume with no such experience; suggest a skills gap instead of adding Kubernetes to the resume or profile.
- State the limits before asking someone to replace their existing tool: Keep job text snapshots and application deadlines editable. Do not scrape authenticated profiles or auto-apply; suggestions cannot promise ATS success or employment outcomes.

## Engineering roadmap
1. Phase 1 — Pin the working slice and create its example input: Track job applications and maintain an evidence bank that feeds truthful resume and LinkedIn profile improvement checklists. 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 Application, JobSnapshot, ExperienceEvidence, SuggestedEdit and ReviewDecision; every new achievement or metric must cite user evidence or remain a question.
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: Keep job text snapshots and application deadlines editable. Do not scrape authenticated profiles or auto-apply; suggestions cannot promise ATS success or employment outcomes.
5. Phase 5 — Walk through this concrete acceptance case and preserve its exported evidence: Compare a job asking for Kubernetes against a resume with no such experience; suggest a skills gap instead of adding Kubernetes to the resume or profile. Finish the README and backup/restore instructions; report unfinished capabilities explicitly.

## Paid-product capabilities outside this build
- browser tooling, templates, coaching content, AI workflows, and hosted sync
- proprietary recruiter data
- job-board distribution
- human coaching
- automated application networks

## Implementation prompt
Build the following focused alternative to Careerflow. Implement the focused workflow below first; the verdict is not evidence of a completed or production-certified build.

WORKING SLICE
Track job applications and maintain an evidence bank that feeds truthful resume and LinkedIn profile improvement checklists.

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 Application, JobSnapshot, ExperienceEvidence, SuggestedEdit and ReviewDecision; every new achievement or metric must cite user evidence or remain a question.

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
Keep job text snapshots and application deadlines editable. Do not scrape authenticated profiles or auto-apply; suggestions cannot promise ATS success or employment outcomes.

ACCEPTANCE SCENARIO
Compare a job asking for Kubernetes against a resume with no such experience; suggest a skills gap instead of adding Kubernetes to the resume or profile. 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.