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Moai Workflow Docs Claim Check

OrganizationPopular
modu-ai
moai-workflow-docs-claim-check

Read-only check of whether the claims a public-facing document makes (README, release notes, install and usage guides) are supported by user-supplied evidence. Runs Preflight, Claim Triage, and Validation, splits composite claims into atomic ones, and labels each. Runs no commands and writes no fixes.

Overview

Publishermodu-ai
Repositorymoai-adk
Skill namemoai-workflow-docs-claim-check
Stars
1.2K
Forks
226
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

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

  • Open source

    Published by modu-ai on GitHub. Read the source before you install it.

Installation

Install the Moai Workflow Docs Claim 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/modu-ai/moai-adk/tree/main/.claude/skills/moai-workflow-docs-claim-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/modu-ai/moai-adk.git /tmp/moai-adk
mkdir -p .claude/skills
cp -r /tmp/moai-adk/.claude/skills/moai-workflow-docs-claim-check .claude/skills/moai-workflow-docs-claim-check
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Moai Workflow Docs Claim 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 Moai Workflow Docs Claim 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 Moai Workflow Docs Claim 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.

Documentation Claim Check

Assess whether the statements a public-facing document makes are actually supported by the evidence supplied alongside it. The assessment reads, judges, and reports. It changes nothing and runs nothing.

Typical subjects: a README, a release note, an install guide, a quickstart, a migration note, a feature or compatibility table.

Hard boundaries

Three boundaries are absolute. They hold even when the request asks for more; in that case perform the assessment and decline the rest in Boundary Notes.

  1. No command execution. Do not run builds, tests, package managers, linters, network requests, or any shell command as part of the assessment. When a claim can only be settled by running something, name the exact command and the file it should run against and label the claim needs-human. Naming the command is the deliverable; running it is not.
  2. No fixes. Do not produce patches, diffs, rewritten passages, or file edits. Describe what a maintainer would change and where, then stop.
  3. No code review and no security review. Do not assess code quality, architecture, performance, or vulnerabilities. If asked, state the boundary in Boundary Notes and continue with the claim assessment only.

Opening the files the user pointed at, to locate the evidence they supplied, is in scope — that is reading, not executing.

Phase 1 — Preflight

Complete all three steps before triaging a single claim. If a step cannot be completed, report that and stop rather than guessing.

  1. Confirm the document is public-facing. This skill judges documents written for users of the software: README, release notes, install and usage guides, published site pages. Internal design notes, task trackers, and private runbooks are out of scope — say so and stop.
  2. Inventory the supplied evidence. For every item record what it is, where it came from, its version identifier, and its timestamp. An item with neither is still usable, but record it as undated: it cannot later support a freshness judgment.
  3. Flag secrets for redaction before proceeding. Scan the supplied evidence for credentials, tokens, private keys, connection strings, and personal data. On a hit, flag the location for redaction, never reproduce the secret in any output, and continue only once a redacted copy is available.

Phase 2 — Claim Triage

Turn the document into an inventory of atomic claims.

Extract. Walk the document and pull out every statement that asserts something checkable about the software: supported platforms and versions, install and usage steps, defaults, limits, guarantees, availability, counts.

Decompose composite claims. A composite claim bundles several independently-checkable assertions into one sentence. Split it so that each atomic claim carries exactly one assertion and therefore receives exactly one label. A sentence that would otherwise need two labels is not yet atomic.

"Installs with a single command on macOS and Linux" splits into three atomic claims: single-command install, macOS support, Linux support. Each is evidenced — and can fail — independently.

Set aside the subjective. Statements of taste or ambition ("fast", "developer-friendly", "production-grade") cannot be checked against evidence. Exclude them from labeling and list them under Input Scope Reviewed with a one-line reason. When a subjective adjective wraps a checkable core, split it: label the core, exclude the adjective.

Bind evidence. For each atomic claim, note which supplied evidence items bear on it — or record that none does.

Phase 3 — Validation

Assign exactly one label to every atomic claim by walking the ordered decision tree. Stop at the first gate that fires; do not re-open an earlier gate.

needs-human  ->  stale-suspected  ->  verified  ->  unsupported
LabelGate condition
needs-humanSettling the claim requires something outside this skill: running a command, reaching a private system, exercising a UI, or a call only a maintainer can make.
stale-suspectedEvidence indicates the claim was true earlier, but current evidence disagrees on a version, date, count, or name. A temporal mismatch, not a contradiction of substance.
verifiedSupplied evidence directly supports the claim and the supporting item can be named.
unsupportedNone of the above fired: the evidence does not carry the claim.

unsupported always carries exactly one reason:

ReasonMeaning
missing-evidenceNo supplied evidence speaks to the claim at all.
contradictedSupplied evidence asserts the opposite.
insufficient-coverageEvidence is on-topic but narrower than the claim — one platform of three, one version of a declared range, one path of several.

Anchoring rules: every verified names its evidence anchor; every stale-suspected names the mismatched field and both values; every needs-human names the command or file that would settle it; every unsupported carries its reason.

Gate criteria in full, tie-breaks between adjacent gates, and a claim-type table live in references/label-decision-tree.md. Worked end-to-end assessments live in references/worked-examples.md.

Output contract

Emit exactly these three sections, in this order, every time — including when the claim inventory is empty.

1. Input Scope Reviewed

  • Documents read, with version or date when known.
  • Evidence items inventoried, each with version and timestamp (or undated).
  • Statements excluded as subjective, each with a one-line reason.
  • Evidence that was needed but not supplied, named specifically.

2. Claim Assessments

One row per atomic claim:

#Atomic claimLabelReasonEvidence anchor or what is missing

Reason is filled only for unsupported rows.

3. Boundary Notes

  • The certification, stated literally: no commands executed during this assessment.
  • Anything declined, with the boundary that forbade it (fix requests, code or security review requests).
  • Residual risk: what this assessment still does not prove even where every row is verified.
  • For each needs-human row, the specific command or file a maintainer needs.

Relationship to the claim-integrity policy

This skill is the document-facing procedure for the project rule at .claude/rules/moai/core/verification-claim-integrity.md. That rule owns the norm and its reporting format; this skill only applies the norm to a published document. Read the rule directly when the norm itself is in question — it is not restated here.

Bundled references

FileContents
references/label-decision-tree.mdFull gate criteria, adjacent-gate tie-breaks, claim-type table, composite-splitting guidance
references/worked-examples.mdEnd-to-end assessments across several language ecosystems

Common Rationalizations

RationalizationReality
"One quick command settles this claim, so I will just run it"The no-execution boundary has no size exemption. Name the command and label the claim needs-human.
"The claim is obviously true, I know this ecosystem"Background knowledge is not supplied evidence. Absent an evidence anchor the label is unsupported, reason missing-evidence.
"No evidence contradicts it, so it passes"Nothing arriving to contradict a claim is not support for it. Silence maps to missing-evidence, never to verified.
"The doc is wrong, I will just fix the sentence"Findings only. Describe the change and where it belongs; the edit is a maintainer action.
"This sentence has two parts but one obvious verdict"Two assertions need two labels. Split it before labeling.
"The Linux log is missing but the macOS log is close enough"Narrower evidence than the claim is insufficient-coverage, not verified.

Red Flags

  • A claim row carries two labels, or a label plus a hedge.
  • A verified row names no evidence anchor.
  • An unsupported row carries no reason, or more than one.
  • A subjective adjective was labeled instead of excluded.
  • Boundary Notes are missing, or omit the literal no-commands-executed line.
  • A command was run "just to confirm" during the assessment.
  • Zero evidence was supplied and some claim still came back verified.
  • Evidence was inventoried without a version or timestamp and then used to judge freshness.

Verification

  • All three Preflight steps completed and recorded before any labeling.
  • Every composite claim split until each atomic claim holds one assertion.
  • Every atomic claim carries exactly one label from the four-value set.
  • The decision tree was walked in order for each claim.
  • Every unsupported row carries exactly one reason.
  • All three output sections are present, in order.
  • Boundary Notes state the literal no-commands-executed certification.
  • No patch, diff, or edited passage appears anywhere in the output.

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 Moai Workflow Docs Claim Check AI skill do?

Read-only check of whether the claims a public-facing document makes (README, release notes, install and usage guides) are supported by user-supplied evidence. Runs Preflight, Claim Triage, and Validation, splits composite claims into atomic ones, and labels each. Runs no commands and writes no fixes.

Why use Moai Workflow Docs Claim Check on TypingMind?

Because you install it once and use it with any model. Moai Workflow Docs Claim 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 Moai Workflow Docs Claim Check in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/modu-ai/moai-adk/tree/main/.claude/skills/moai-workflow-docs-claim-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 Moai Workflow Docs Claim 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 Moai Workflow Docs Claim Check?

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

Is the Moai Workflow Docs Claim Check AI skill free?

Yes. It is published on GitHub by modu-ai under the Apache-2.0 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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