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Creative Qa

  • 687 installs
  • 19 repo stars
  • Updated July 27, 2026
  • postplusai/postplus-skills

creative-qa is an agent skill that converts human pass/fail reviews of generated images, voice, and video into structured QA records with reasons, blame stages, and rerun guidance for developers running media production

About

creative-qa is an agent skill from postplusai/postplus-skills in the Media and Creative Production family that records human-in-the-loop quality judgments for generated images, voice takes, video renders, and final exports. After a person reviews an asset, the skill captures structured verdicts, reasons, issue categories, blame stage attribution, and rerun guidance instead of leaving subjective approval as untracked chat history. Developers reach for creative-qa in short-form and generative media workflows where QA must be auditable—knowing which pipeline stage failed, why an image or voice take was rejected, and what to regenerate next. The skill fits post-generation review gates before publishing or downstream edit assembly. It complements PostPlus generation skills by focusing on judgment capture and remediation notes rather than model selection or rendering commands themselves.

  • Human-in-the-loop only—no invented approval or rejection
  • Structured verdicts, issue categories, blame stage, and proposed action
  • Targets images, voice takes, video renders, and final exports with version ids
  • AI may normalize categories and suggest blame stages; humans confirm the record
  • Part of PostPlus Media and Creative Production family (media-production)

Creative Qa by the numbers

  • 687 all-time installs (skills.sh)
  • +43 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #359 of 2,715 Automation & Workflows skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/postplusai/postplus-skills --skill creative-qa

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Listed on Skillselion
Installs687
repo stars19
Security audit2 / 3 scanners passed
Last updatedJuly 27, 2026
Repositorypostplusai/postplus-skills

How do you track human QA on generated media assets?

Turn a human reviewer’s pass/fail on generated images, voice, or video into a structured QA record with reasons, blame stage, and rerun guidance.

Who is it for?

Developers building generative media pipelines who need auditable human review records for images, voice takes, and video renders in short-form production.

Skip if: Teams still generating assets who should run transcription, subtitle, or edit-prep skills first via media-router before any human QA pass.

When should I use this skill?

A human has reviewed a generated image, voice take, or video and the user needs structured verdicts, issue categories, blame stage, or rerun guidance captured formally.

What you get

Structured QA record with verdict, categorized issues, blamed pipeline stage, and actionable rerun guidance for the rejected asset.

  • structured QA verdict record
  • issue category tags
  • rerun guidance notes

By the numbers

  • Covers 3 generative asset types: images, voice takes, and video renders

Files

SKILL.mdMarkdownGitHub ↗

Creative QA

Use When

  • Record human-in-the-loop quality judgments for generated images, voice takes, video renders, and final exports.
  • Use after a person has reviewed an asset and you need structured verdicts, reasons, issue categories, blame stage, proposed action, and rerun guidance.

Do Not Use When

  • No human verdict exists yet.
  • The user wants autonomous AI approval.
  • The task is prompt critique before generation; use preflight or prompt QA instead.

Required Input

  • Human reviewer verdict and reasons.
  • Target object type, id, and version.
  • Reviewer and review timestamp.
  • Proposed action.

Human Rule

Humans decide quality. AI may summarize notes, normalize categories, and suggest likely blame stages, but only human-confirmed feedback becomes the durable QA record.

Do not invent approval, rejection, or reviewer intent.

Default Workflow

1. Confirm the reviewed asset: type, id, version, and optional campaign/persona/concept linkage. 2. Confirm the human reviewer, timestamp, verdict, and reasons. 3. Normalize reasons into goodReasons, badReasons, issue categories, and blame stage. 4. Decide proposed action: approve, rerun a specific stage, revise source input, or reject. 5. If rerun guidance exists, build a feedback handoff with a concrete rerunTarget. 6. Store or return the structured QA record next to the reviewed object or in the work folder's QA area.

Supported Objects

  • image
  • voice_take
  • video_render
  • final_export

Verdicts

Valid verdicts are approved, revise, and reject.

For revise or reject, require non-empty badReasons and issueCategories. For approved, these may be empty arrays.

Categories

Common issue categories:

  • lip_sync, persona_drift, audio_style, audio_pacing, hook_weak, ad_like, ugc_native_feel, visual_realism, subtitle_accuracy, mixed

Common blame stages:

  • image, script, voice, render, subtitle, mixed

Use blame stage as diagnostic guidance, not as a substitute for human verdict.

Stop Conditions

  • Stop when required user intent, source evidence, or owned input artifacts are

missing and guessing would change the result.

  • If an owned CLI or script command fails, report the exact error and stop. Do

not bypass the failure with metadata-only answers, readiness probing, local payload rewrites, fallback providers, or unpublished tools.

Output Shape

qaReport:

  • qaReportId, targetObjectType, targetObjectId, targetVersion, campaignId, personaId, conceptId, reviewer, reviewedAt, verdict, goodReasons, badReasons, issueCategories, blameStage, scores, proposedAction, status

Optional feedbackHandoff:

  • feedbackId, qaReportId, targetObjectType, targetObjectId, feedbackCategory, feedbackText, dependencyImpact, rerunTarget

Anti-Patterns

  • silently approving an asset
  • replacing a human verdict with an AI guess
  • using vague stage labels as rerun targets
  • losing linkage to the reviewed object version
  • preserving subjective chat notes without structured reasons

Handoff

  • Return the structured QA record structured output or an explicit blocker. If feedback is present, hand off to the named rerun target with the QA record attached.

Public Command Boundary

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

it directly.

  • If an owned CLI or script command fails, report the exact error and stop. Do

not bypass the failure with metadata-only answers, readiness probing, local payload rewrites, fallback providers, or unpublished tools.

Related skills

FAQ

What assets does creative-qa cover?

creative-qa records human judgments for generated images, voice takes, video renders, and final exports in short-form production. Each review yields a structured verdict with reasons and rerun guidance.

When should creative-qa run in a pipeline?

creative-qa runs after a person reviews an asset and before publishing or downstream edits. It replaces subjective chat approval with categorized issues, blame stage, and explicit rerun instructions.

Does creative-qa generate new media?

creative-qa does not render or transcribe media. It captures human QA outcomes and remediation notes so PostPlus generation or routing skills can rerun the correct pipeline stage.

Is Creative Qa safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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