Output Quality Check logo

Output Quality Check

Community
mia-ppp
output-quality-check

When the user wants an independent quality check of an artifact produced by a PMM skill, such as a brief, strategy, plan, or asset. Checks the output against the originating skill's own explicit requirements and routes failures back for correction. For company-message alignment use messaging-consistency-audit; for SKILL.md structure use repository validation or skill specification review.

Overview

Publishermia-ppp
RepositoryPMM-Skills
Skill nameoutput-quality-check
Stars
76
Forks
2
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by mia-ppp on GitHub. Read the source before you install it.

Installation

Install the Output Quality Check AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/mia-ppp/PMM-Skills/tree/main/skills/output-quality-check
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mia-ppp/PMM-Skills.git /tmp/PMM-Skills
mkdir -p .claude/skills
cp -r /tmp/PMM-Skills/skills/output-quality-check .claude/skills/output-quality-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Output Quality Check in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Output Quality Check on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Output Quality Check is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Output Quality Check

Independently answer: did the originating skill produce an output that meets the quality standard declared by that skill? The originating skill owns the domain standard. This skill reads and applies it without adding a universal PMM rubric.

Scope boundary

  • Verify output quality against the originating skill's explicit requirements.
  • Do not decide whether an asset matches company messaging. That belongs to messaging-consistency-audit and the canonical SSOT.
  • Do not audit whether a SKILL.md is well structured. Use repository validation or the applicable skill-spec review process.
  • Do not silently edit the artifact. Report corrections and route them back to the originating skill.

An artifact can pass both reviews, fail either one, or fail both. Keep their findings and statuses separate. For a market-facing asset, the useful flow is originating skill → artifact → output-quality-check → messaging-consistency-audit → approval or remediation. The messaging audit is not required for internal artifacts where it is irrelevant.

Inputs and standard discovery

Accept the artifact and its claimed originating skill or infer the skill from artifact metadata, format, or user context. If origin remains ambiguous, ask which skill produced it. If the artifact is absent, request it. Never verify a summary in place of the complete artifact when the full version is available.

Read the originating skill's current SKILL.md before evaluating the artifact. Extract all explicit, applicable criteria from its declared:

  • required output and sections;
  • process or workflow requirements that leave observable evidence in the output;
  • output rules and format constraints;
  • quality gates, verification criteria, and checklists;
  • evidence, traceability, safety, and approval requirements.

Use only requirements actually stated by that skill or by a shared reference it explicitly requires. Follow referenced shared standards, such as ../_shared/evidence-gaps.md, when applicable. Do not import criteria from a neighboring skill, this verifier's preferences, or generic notions of good PMM work. If the skill declares no explicit verifiable criteria, report that limitation and mark the affected check UNVERIFIABLE. Do not invent a rubric.

Read the entire artifact. Evaluate each requirement independently and cite the artifact location or quote that supports the finding. A claim that a step was completed is not evidence when the required source or artifact is unavailable.

For audience-dependent work, apply the persona-consumption requirements in ../_shared/ssot-consumption.md when required by the originating skill. Inspect whether relevant intelligence supports the material choices in the output and preserves source access, evidence status and uncertainty. Existing rationale or annotations suffice; no separate audience table or exhaustive attributes are required. Shared treatment may be valid when evidence supports shared needs. Non-audience tasks are exempt. Missing nonessential metadata is not a failure; unavailable essential evidence is UNVERIFIABLE. Do not invent persona differences or certify unsupported application. Route failures to the originating owner; message-to-canon alignment remains a separate messaging review.

Finding statuses

Assign each applicable requirement exactly one result:

ResultUse
PASSObservable artifact evidence meets the originating skill's requirement.
FAILThe artifact contradicts or omits a requirement that can be checked.
NOT_APPLICABLEThe originating skill permits a conditional requirement and the artifact provides a valid reason it does not apply.
UNVERIFIABLERequired evidence/input is unavailable, or a claimed requirement cannot be independently confirmed. Do not count this as PASS.

Overall status is PASS only when every applicable criterion is PASS or NOT_APPLICABLE and none is UNVERIFIABLE. Otherwise use NEEDS_CORRECTION. Report UNVERIFIABLE separately from actual FAIL findings. Do not imply that absence of evidence proves the underlying claim false.

Verification workflow

  1. Identify the artifact, claimed/inferred originating skill, and artifact version. Record the verification date when available.
  2. Load that skill's current SKILL.md and any shared references it explicitly requires for the artifact. Record the standard's source and version/status.
  3. Extract the complete set of explicit applicable requirements. Preserve conditional branches. Do not add requirements.
  4. Read the artifact in full and check each requirement. Record evidence with locations. Mark unsupported claims UNVERIFIABLE when verification depends on unavailable inputs or sources.
  5. Assign stable finding IDs for FAIL or UNVERIFIABLE results, such as MV-[skill]-001. Preserve IDs on re-verification when the same requirement remains unresolved. Close only after inspecting the corrected artifact.
  6. Route every failure to the originating skill for correction. If the originating skill cannot satisfy its own requirement because canonical context appears outdated or contradictory, route the evidence through ssot-context-loop and its human approval gates. Never change canon.
  7. For market-facing messaging alignment, separately route the artifact to messaging-consistency-audit where relevant. Do not combine its outcome with this verification status.

Required output

Output quality verification: [artifact]

  • Originating skill: [skill]
  • Overall status: PASS or NEEDS_CORRECTION
  • Standard inspected: [SKILL.md path/version or available revision]
RequirementSource in SKILL.mdResultEvidence in artifactFinding / correction

Include all applicable requirements, including passing checks. Cite section headings or line/page/slide/email locations when available. Use NOT_APPLICABLE only when allowed and explained. Use UNVERIFIABLE when the artifact claims compliance but evidence/input cannot be inspected.

Required corrections

List only actual FAIL findings, prioritized by impact. For each include finding ID, violated requirement, artifact location, why it fails, required correction, and route to the originating skill. List UNVERIFIABLE items separately with the missing source or input needed. Do not frame them as proven errors.

If no corrections are needed, say so. Re-verification inspects the revised artifact and updates each existing ID to PASS, NOT_APPLICABLE, FAIL, or UNVERIFIABLE. Do not close an item based only on a correction description.

Safety and boundaries

  • Verification is read-only. Do not rewrite, patch, or approve the artifact.
  • Do not inspect or compare canon to decide messaging consistency. If the originating skill requires canon use as part of its own deliverable, verify only that the artifact records the required inputs and follows its declared process; route actual message-to-canon claims to the messaging auditor.
  • Do not treat output-quality-check as a check of skill specification structure.
  • Repeated failures that expose a gap in a skill's own requirements can be reported as a proposed skill improvement, routed to that skill's owner. Do not amend skill standards during artifact verification.

Related skills

  • messaging-consistency-audit: vertical SSOT and horizontal messaging checks.
  • ssot-context-loop: governed canonical context and approval workflow.
  • events: event strategy and downstream asset orchestration.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Output Quality Check AI skill do?

When the user wants an independent quality check of an artifact produced by a PMM skill, such as a brief, strategy, plan, or asset. Checks the output against the originating skill's own explicit requirements and routes failures back for correction. For company-message alignment use messaging-consistency-audit; for SKILL.md structure use repository validation or skill specification review.

Why use Output Quality Check on TypingMind?

Because you install it once and use it with any model. Output Quality Check is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Output Quality Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mia-ppp/PMM-Skills/tree/main/skills/output-quality-check. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Output Quality Check?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Output Quality Check?

As many as you like. As long as a model supports skills, you can use Output Quality Check with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Output Quality Check AI skill free?

Yes. It is published on GitHub by mia-ppp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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