Verify logo

Verify

Organization
Factory-AI
verify

Background knowledge for droid-control workflows -- not invoked directly. Deliverable verification against commitments.

Overview

PublisherFactory-AI
Repositoryfactory-plugins
Skill nameverify
Stars
111
Forks
15
Bundled files
Instructions only
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 Factory-AI on GitHub. Read the source before you install it.

Installation

Install the Verify 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.
  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/Factory-AI/factory-plugins.git /tmp/factory-plugins
mkdir -p .claude/skills
cp -r /tmp/factory-plugins/plugins/droid-control/skills/verify .claude/skills/verify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verify 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 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 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 orchestrator routed you here. This atom checks the final deliverable against the commitments made at the start of the workflow.

Inputs

You receive:

  1. Commitments from the command's parse step -- the promises made about what the deliverable would contain
  2. Compose outputs -- the finished artifact(s) and their metadata

Composition is optional: raw desktop screenshots/state and the driver's evidence handoff can be the deliverable. Verify the task's postcondition, not merely the action's exit status or effect field.

Status vocabulary

Every step, check, and postcondition gets exactly one of these; commands and driver atoms use the same words.

StatusMeaningCounts as
PASSThe postcondition was observedMet
FAILThe postcondition was observed to be falseNot met — this is a finding
BLOCKEDThe postcondition could not be observed: capture unavailable, permission wait unresolved, missing binary or connection, environment prevented the stepNeither. Name what blocked it and what unblocks it

BLOCKED is never rolled into PASS or FAIL. A deliverable with BLOCKED steps is incomplete unless the user accepts the partial evidence. /verify's claim verdict (CONFIRMED / REFUTED / INCONCLUSIVE) is about evidence sufficiency, not step status: any BLOCKED step that the claim depends on makes the verdict INCONCLUSIVE, but INCONCLUSIVE also covers steps that all ran and still did not decide the claim.

Video deliverables

Technical checks

Run ffprobe on the final .mp4, then a full decode:

bash
ffprobe -v quiet -print_format json -show_format -show_streams <video>
ffmpeg -v error -xerror -i <video> -f null -   # -xerror: exit non-zero on the first decode error
CheckPass condition
Exists and decodesffprobe exits 0 with duration > 0, and the ffmpeg -xerror decode exits 0 with empty stderr
ResolutionMatches the fidelity compose resolved: 1920x1080 for compact/standard, 2560x1440 for inspect (the default for side-by-side), or the explicit width/height in the props
Pixel formatpix_fmt=yuv420p with color_space=bt709 (a yuvj420p stream means the render bypassed render-showcase.sh)
File sizeUnder 5 MB for GitHub embeds (25 MB hard limit)
DurationEquals 4s + longest_clip / speed + 3.5s (compose's duration checkpoint) and falls within the compose pacing table's target range for this demo type: 30-45s (single feature), 45-75s (side-by-side comparison), 60-120s (multi-phase). Below the minimum is a failure — re-compose with a lower speed factor or re-capture with more steps.
FilenameIncludes PR number or meaningful identifier

Commitment checks

Walk through each commitment from the parse step:

CommitmentHow to verify
Title cardVideo starts with a static frame showing PR info (check first 5s)
Side-by-side layoutVideo shows two panels with a divider
Showcase polishResolution matches the resolved fidelity, window chrome and rounded corners visible
Keystroke overlayPill overlays appear at interaction points
EffectsEffects matching the committed tier are present (utilitarian: zoom/keystroke; full: spotlight, zoom, callout, keystroke)
Speed noteTitle card mentions playback speed

Content checks

Metadata cannot show what is on screen. For every proof point, extract the frame and look at it:

bash
ffmpeg -v error -y -ss <t> -i <video> -frames:v 1 "${RUN_DIR}/proof-<t>.png"   # then Read the PNG
  • Every claim from the "what to prove" analysis has visible evidence in the video, confirmed in an extracted frame at the timestamp you report
  • Both states (before/after, input/result) appear on screen
  • The final state: clips play from t = 4.0s to t = 4.0s + longest_clip / speed, fully visible; after that the last frame is held under the outro crossfade. Extract the final proof frame just before the clips end (e.g. t = 4.0 + longest_clip / speed - 0.1), not from the crossfade
  • In a side-by-side, a shorter clip holds its final frame; confirm the held panel shows the intended final state, not an unfinished step
  • No dead time longer than 3 seconds without visible activity

Screenshot/snapshot deliverables

For proofs

CheckPass condition
Evidence existsScreenshots/snapshots at every claimed proof point
Environment statedDriver, terminal/browser, OS identified
Conclusion presentEvidence explicitly supports or refutes the claim
Before/after pairedIf comparison, both branches shown at same capture points

For QA reports

CheckPass condition
Step coverageEvery defined test step has a status (PASS / FAIL / BLOCKED)
Evidence attachedScreenshots/snapshots at every step
Failures documentedFAIL steps have evidence and description; BLOCKED steps name the blocker and what unblocks it
Report structuredMarkdown report follows the QA template

Failure handling

If any check fails:

  1. Identify which stage produced the problem (capture or compose)
  2. Report the specific failure: "Side-by-side layout was committed but the output is a single panel"
  3. Go back to the failed stage and fix it
  4. Re-verify after the fix

Do not report a deliverable as complete until every commitment is met.

Output

## Verification

### Technical
- Decode: ffmpeg -xerror exit 0 ✓
- Resolution: 2560x1440 (inspect, side-by-side) ✓
- Duration: 52.5s = 4 + 45 + 3.5 ✓
- Size: 3.2 MB ✓
- Format: yuv420p / bt709 ✓

### Commitments
- [x] Title card with PR info
- [x] Side-by-side comparison layout
- [x] Showcase hero preset applied
- [x] Keystroke overlay visible

### Content
- [x] Fork creates independent session (frame at 0:18 read: new session id in header)
- [x] History diverges after fork (frame at 0:32 read: differing last messages)

All commitments met. Deliverable ready.

Frequently asked questions

What does the Verify AI skill do?

Background knowledge for droid-control workflows -- not invoked directly. Deliverable verification against commitments.

Why use Verify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Factory-AI/factory-plugins/tree/master/plugins/droid-control/skills/verify. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Verify?

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?

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

Is the Verify AI skill free?

It is published on GitHub by Factory-AI. Check the repository for licensing terms. 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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