joblit-codex-batch

name	joblit-codex-batch
description	Execute Joblit NEW-job batch tailoring in a deterministic Codex loop and persist CV/Cover PDFs with task-level status updates.

Joblit Codex Batch Skill

Purpose

Run a deterministic, resumable batch loop that generates resume and cover outputs for filtered NEW jobs.

Required Inputs

• batchId (UUID)
• maxSteps (default 1, recommended 3-5 per loop)
• External model output JSON for each target (resume and cover)

API Sequence

1. Claim tasks

• POST /api/application-batches/:id/codex-run
• Read tasks[] and context.promptMetaByTarget

2. Build target prompt per task

• POST /api/applications/prompt with { jobId, target }
• Use returned prompt and expectedJsonSchema

3. Import generated output

• POST /api/applications/manual-generate with:
  • jobId
  • target
  • modelOutput (strict JSON string)
  • promptMeta (echo from prompt response)

4. Mark task state

• PATCH /api/application-batches/:id/tasks/:taskId
• Status:
  • SUCCEEDED when both targets are imported
  • FAILED with concise error when unrecoverable

5. Check summary

• GET /api/application-batches/:id/summary

Hard Rules

• Never skip promptMeta on manual import.
• Keep JSON strict: no markdown wrappers, no prose around JSON.
• Do not fabricate resume facts or unsupported claims.
• Fail fast with concise reason if parsing/validation fails.

Completion Criteria

• No pending tasks in batch summary.
• Every processed task is SUCCEEDED, FAILED, or SKIPPED.
• Generated PDFs are downloadable from Joblit UI.