MCPInstall

image-batch-runner

Execute ready image briefs and edits, including product or persona images and batch variants. Use resolved references and return image assets with saved run results.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/postplusai/postplus-skills --skill image-batch-runner
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads image-batch-runner automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Image Batch Runner

Use When

  • Persona, concept, or shot inputs already exist and the next step is a hosted image generation or reference-based edit run.
  • The output must include local image files plus durable request, response, and manifest records for later QA or video rendering.

Do Not Use When

  • The task belongs to ideation, QA, or another released skill listed in the handoff section.
  • Required inputs are missing and guessing would change the result.
  • Creative classification, model/reference policy, or storyboard logic is still unresolved. Use image-generation first.

Execution Boundary

  • Hosted image generation and edits run through the public postplus media create verb and are async. A submit records the run handle, current status, and completed artifact metadata (bytes are not auto-downloaded; see the download command below).
  • This runner validates and executes resolved requests. It must not make creative strategy, task-classification, or reference-policy decisions.
  • A higher-quality default and faster or cheaper model families are available; prefer the default unless the user or upstream brief asks for a specific family, ratio, quality, or resolution. The generated example below shows the default endpoint key.
  • Only edit endpoints accept --reference-image (default edit endpoint: image-gpt-image-2-edit). Text endpoints such as image-gpt-image-2-text reject the flag with Unknown option, so any reference-bound generation must target an edit endpoint, not a text endpoint.
  • Reference-based edits pass each source image via a repeated --reference-image flag. Each value may be a local path, HTTPS URL, existing PostPlus media reference, or data URI. The CLI validates and prepares local media before the single hosted submit; do not pre-upload it or construct a manual request object.
  • Save a finished image output to disk with postplus media-file download --reference <output.data.artifacts[0].mediaReference> --output-file <path>. Use the completed result's artifact reference as the download source.
  • Identifiers and run-local state (assetId, runId, localAssetDir, manifest paths) are minted or derived by the runner — do not supply them. Read them back from the result for the next handoff.

Source And Path

  • Ground every request in a benchmark-backed persona lock, concept or shot need, visual constraints, assetPurpose, and sourceBasis.
  • Use source files from the active project/client folder first. Do not treat one client directory as the default for all image work.
  • Keep internal requests, responses, and manifests under .postplus; keep final user-facing images and manifests in the active asset folder. If no asset folder exists, choose one explicit workspace path.

Review And Handoff

  • Before submission, verify persona grounding, asset purpose, source basis, and what must stay fixed versus vary.
  • After generation, check realism, benchmark fit, repeatability across videos, copied-creator risk, and ad-like drift.
  • If processing is still pending, return the manifest/request paths and the poll command postplus media poll --handle <output.data.id> --output path/to/generation-result.json. Reuse the exact --output path from the initial submit. A completed poll atomically replaces the processing JSON at that path with the completed result; prefer the CLI-returned action or resume command for that same operation, honor its wait/recovery boundary, and never submit a replacement job.

Stop Conditions

  • Stop when required user intent, source evidence, or owned input artifacts are missing and guessing would change the result.

  • Batch canary: before fanning out a batch of independent items, submit item 1 alone and poll it to a terminal state. If the canary is content-policy blocked (the per-item typed code below), record and skip it per batch isolation, then canary the next item; fan out only after a non-blocked canary completes successfully. Every other canary failure is systemic: stop. Some failures are only visible on poll (async terminal states), so a submit-accepted batch can still be 100% doomed — a canary caps the blast radius of any systemic defect (bad reference form, service outage, auth) at one item instead of the whole batch.

  • Batch isolation: when producing a batch of independent items, a per-item content/safety rejection is isolated to that item. It is identified only by the typed code postplus_cli_hosted_media_content_policy_blocked, never by matching error prose, and it surfaces at either boundary: a failed postplus media create whose typed error code is that code, or a submitted run whose poll result carries output.data.status: failed and output.data.error.code set to that code. On either, record which item was blocked and its exact reason, skip it, and continue submitting and polling the remaining items, then report the incomplete set at the end. Do not retry, soften, or re-submit the blocked item — that is a forbidden payload rewrite. Every other failure (a failed owned CLI/script command whose typed code is not that content-policy code, or a run whose error.code is not that content-policy code — auth, transport, quota, malformed request, service outage) is systemic: stop per the rule above.

Public Command Boundary

  • Choose the smallest matching command or workflow from the user input and run it directly.

  • Readiness diagnostics: postplus doctor --skill image-batch-runner.

  • Poll a pending image job: postplus media poll --handle <output.data.id> --output path/to/generation-result.json. Reuse the initial submit's result path so the completed poll atomically replaces its processing JSON. Prefer the returned resume action and stop when the CLI wait/recovery boundary is reached.

  • Use postplus media schema --json only when you need the full endpoint, flag, and enum contract or are repairing an unknown request shape.

  • Run the hosted image job with the generated command below; do not use another execution interface.

<!-- BEGIN GENERATED EXECUTION EXAMPLE -->
postplus media create image-gpt-image-2-text \
  --prompt "Describe the result you need" \
  --wait \
  --output ./result.json

Follow the CLI's structured result and reported next action; do not infer recovery from free-text messages. Wait for explicit user approval when requested; an action does not authorize spending, publishing, or overwriting. Resume the same operation through its returned checkpoint or action; never resubmit uncertain work, repeat exhausted recovery, or switch providers to bypass failure.

<!-- END GENERATED EXECUTION EXAMPLE -->
  • Requested tasks use existing PostPlus credits after the current price and balance are checked. Estimate-only requests do not execute. Stop on insufficient credits; do not recharge, buy a subscription, or repeat an uncertain paid operation.

FAQ

What does the image-batch-runner skill do?

Execute ready image briefs and edits, including product or persona images and batch variants. Use resolved references and return image assets with saved run results.

How do I install image-batch-runner?

Run npx skills add https://github.com/postplusai/postplus-skills --skill image-batch-runner in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/image-batch-runner/ (personal) or .claude/skills/image-batch-runner/ (project).

Which AI agents can use image-batch-runner?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is image-batch-runner safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains image-batch-runner?

It is published by postplusai in postplusai/postplus-skills. It has 1,029 recorded installs.

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