Mark generated images as approved and save them for reuse.
npx skills add https://github.com/redbananastudios/ai-library --skill approve-output
Please help me install this Agent Skill. Skill: approve-output Source: https://github.com/redbananastudios/ai-library/tree/main/generated/claude/skills/approve-output Command: npx skills add https://github.com/redbananastudios/ai-library --skill approve-output
This Skill streamlines the process of reviewing generated image results and turning user approvals into durable project knowledge.
Tell the AI which images to approve for a given job, for example: "Approve all images for job_id 12345 and note which ones look best for the packshot style."
High-intent search queries and answers about installing and using this skill.
To mark generated image outputs as approved, you update the approval_status field in the job metadata.json. This process also copies the approved images into a selected/ folder for reuse and appends winning patterns to winning-patterns.md.
The best way to save approved images for reuse is by using a bulk approval process that copies them from the job's all/ output directory into a selected/ folder. This curates your preferred assets while updating the job metadata.
Yes, you can promote a candidate LoRA after approving generated images by updating its status and approval fields in lora-registry.json. This ensures the successful model is selected automatically for future generation tasks.
Human-in-the-loop feedback improves future workflows by persisting learning signals from user approvals. It appends successful details to winning-patterns.md and updates workflow-registry.json with success metrics to guide better workflow selection.
Yes, you need a specific job ID or output path to review and approve generated image candidates. This identifier allows the system to locate the correct metadata.json and associate the approval status with the exact generation job.
When you approve specific images, workflow-registry.json is updated with success metrics based on your selections. This records the effective configurations to help optimize and improve the selection of future image generation workflows.
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