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Verify Artifact

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pedrohcgs
verify-artifact

Prove that the file you are about to send, publish, or hand off IS the thing you mean — before it leaves your hands. Rebuild from source, check integrity, diff the derived artifact against its source, and require the recipient to echo what they received. Use before sending a paper/PDF for review, shipping a release or replication package, handing files to a coauthor or collaborator, uploading anything to an external reviewer or model, or publishing. Prevents an entire review/QA cycle from being spent on defects that exist only in the artifact, not in the work.

Overview

Publisherpedrohcgs
Repositoryclaude-code-my-workflow
Skill nameverify-artifact
Stars
1.6K
Forks
3.1K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by pedrohcgs on GitHub. Read the source before you install it.

Installation

Install the Verify Artifact 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/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/verify-artifact
  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/pedrohcgs/claude-code-my-workflow.git /tmp/claude-code-my-workflow
mkdir -p .claude/skills
cp -r /tmp/claude-code-my-workflow/.claude/skills/verify-artifact .claude/skills/verify-artifact
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify Artifact 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 Verify Artifact 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 Verify Artifact 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.

Verify the artifact before it leaves

A review is only as good as the thing reviewed. If the artifact is corrupt, truncated, stale, or silently renumbered, a competent reviewer will return confident findings about defects that do not exist in your work — and you will spend a cycle chasing them. This is cheap to prevent and expensive to miss.

Rule: never send a derived artifact you have not diffed against its source.

1. Rebuild from source, in one shell invocation

Never send yesterday's build. Rebuild, then copy to the send location in the same command, so nothing can change underneath you.

Cloud-synced folders (Dropbox/iCloud/OneDrive) dehydrate files: a PDF can become 0 bytes or a partial copy between building and reading it. Symptoms: Syntax Error: Couldn't find trailer dictionary, a 70 KB file that should be 700 KB. Build-tool "up to date" messages are not evidence the file is intact — the tool checks timestamps, not content. Always stage to a local (non-synced) directory and verify there.

2. Integrity checks (mechanical, fast, non-negotiable)

  • Size and structure: byte size in the expected range; page/record/row count as expected.
  • It parses: open it with a real reader (pdfinfo, pdftotext, a JSON/CSV parser, unzip -t). A file that exists is not a file that works.
  • Sample the content: first and last page/record actually contain what you expect — truncation shows up at the end.
  • Unresolved markers: for documents, count ??, [cite], TODO, XXX, \ref{ leftovers, "Chapter ??"; for code/data, NaNs, empty cells, placeholder values.

3. Diff the derived artifact against its source

This is the step people skip and the one that pays. If you produced the artifact by transforming, excerpting, compressing, or subsetting, then enumerate what could have been lost and check it:

  • Labels/anchors/IDs: set of identifiers in source vs artifact. Anything defined in source and referenced but missing in the artifact is a break.
  • Numbering: removing a numbered object silently renumbers everything after it, so a citation that was "Lemma 10" becomes "Lemma 6" — every downstream reference now points somewhere plausible and wrong. Check numbering stability, not just presence.
  • Counts: sections, tables, figures, rows, functions, endpoints — before vs after.
  • Nested content: excerpting a block can remove statements nested inside it, not just the prose you meant to cut.

If you cut anything, leave a visible in-artifact note saying so, so a reviewer does not read an omission as a gap.

4. Make the recipient prove what they got

Ask the reviewer (human or model) to state, at the top of their response, the exact filenames, page/record counts, and version they are reviewing. This catches stale caches, wrong attachments, and silent fallbacks to an older upload — failures that are otherwise invisible until the findings make no sense.

Use unique filenames per round (report_r6.pdf, not report.pdf). Repeated identical names invite the recipient's system to serve a cached earlier copy.

5. If findings look strange, suspect the artifact first

Before acting on a review, ask: could this finding be an artifact of what I sent? Signals: complaints about missing/undefined references, "sections appear truncated", "the proof ends mid-argument", numbering that does not match your copy, or objections to text you know is present. Re-verify the artifact before you re-verify the work. Applying "fixes" for artifact-induced findings actively damages correct material.

Minimum checklist

  1. Rebuild from source; stage to a local dir in the same shell command.
  2. Verify it parses; check size, counts, first/last content.
  3. Count unresolved markers — expect zero.
  4. Diff identifiers/numbering/counts against source; note any deliberate omissions in the artifact itself.
  5. Unique filename for this round.
  6. Require the recipient to echo filename + counts.

Cross-references

Frequently asked questions

What does the Verify Artifact AI skill do?

Prove that the file you are about to send, publish, or hand off IS the thing you mean — before it leaves your hands. Rebuild from source, check integrity, diff the derived artifact against its source, and require the recipient to echo what they received. Use before sending a paper/PDF for review, shipping a release or replication package, handing files to a coauthor or collaborator, uploading anything to an external reviewer or model, or publishing. Prevents an entire review/QA cycle from being spent on defects that exist only in the artifact, not in the work.

Why use Verify Artifact on TypingMind?

Because you install it once and use it with any model. Verify Artifact 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 Verify Artifact in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/pedrohcgs/claude-code-my-workflow/tree/main/.claude/skills/verify-artifact. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verify Artifact?

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 Verify Artifact?

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

Is the Verify Artifact AI skill free?

Yes. It is published on GitHub by pedrohcgs 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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