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Video Review Canvas

Community
mrpaidsocial
video-review-canvas

Publish a finished (or draft) video cut to a frame.io-style review canvas on here.now — a player with a frame-accurate scrubber, persistent timeline notes, version comparison, replies, resolution and evidence, and a clickable beat map — then read those notes back before the next cut. Use this to DELIVER any video edit for review: reel recuts, brand-deal edits, motion-graphics ads, carousel videos, YouTube cuts, sizzle reels. Trigger whenever a cut is ready to show, or on "send me the video", "put this on a canvas", "let me leave notes on the timeline", "frame.io style", "get me a link to review this", "publish the new version", or "what notes did I leave?" — even if the canvas is never named. Delivering a cut as a bare file attachment or a local preview link instead of a canvas link is the failure this skill exists to prevent. NOT for image or slide galleries (plain here-now publish), and not for making the video itself (branded-ad-edit / hyperframes).

Overview

Publishermrpaidsocial
Repositorymrpaidsocial-video-editor-agent
Skill namevideo-review-canvas
Stars
114
Forks
34
Bundled files
8
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.

  • 8 bundled files

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

  • Open source

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

Installation

Install the Video Review Canvas 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/mrpaidsocial/mrpaidsocial-video-editor-agent/tree/main/.claude/skills/video-review-canvas
  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/mrpaidsocial/mrpaidsocial-video-editor-agent.git /tmp/mrpaidsocial-video-editor-agent
mkdir -p .claude/skills
cp -r /tmp/mrpaidsocial-video-editor-agent/.claude/skills/video-review-canvas .claude/skills/video-review-canvas
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Video Review Canvas 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 Video Review Canvas 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 Video Review Canvas 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.

Video Review Canvas

Every cut the reviewer sees goes on a here.now canvas: the video, a scrubber that reports the exact frame, notes they pin by clicking the timeline, and a beat map naming what each moment is supposed to be doing. The notes persist in here.now Site Data, so the next session reads them back and works the list.

The reply delivering a cut must LEAD with the canvas URL, pasted in full, every single round — even when it's the same link as last time. This has failed in practice: the reviewer had to ask twice for the link on a real delivery. A file card without the link reads as "not delivered", and "it's on the canvas" without the URL doesn't count. Send files as an extra after the link, never instead of it.

Prerequisites

  • node >= 20 (the scripts use fetch and top-level await).
  • ffprobe on PATH, or point the FFPROBE env var at the binary (part of ffmpeg).
  • The here-now skill for publishing, with credentials at ~/.herenow/credentials. If you don't have it, agent docs live at https://here.now/docs (fetch with User-Agent: claude).

Why a canvas and not a file

A file attachment gets you "the middle bit feels slow." A canvas gets you "at 0:14.3 the chip lands before he says the word." The timestamp is the difference between a vague note and an actionable one, so everything here optimises for making it trivial to pin a precise moment: frame stepping with the arrow keys, a frame counter next to the clock, and a beat map that tells the reviewer what each section was trying to do so they can say it missed.

Build and publish

1. Write the config

One JSON file. The beat map is the part that earns its keep — name every card, cut, or section, and say in one line what it's for. That framing is what turns "I dunno, something's off" into a specific note.

jsonc
{
  "video":   "videos/product-launch/output-v2.mp4",
  "outDir":  "videos/product-launch/review",
  "title":   "Product launch ad",
  "version": "v1",
  "eyebrow": "STUDIO · EDIT REVIEW",
  "blurb":   "Motion graphic on every line, in the brand's palette, cut like the reference ad.",
  "author":  "Reviewer",
  "accents": ["#0082fb", "#a033ff", "#ff5c87"],
  "facts":   ["30.03s · 1080×1920 · 30fps", "8 designed cards", "39 SFX hits + music bed"],
  "beats": [
    { "t": 0.00,  "n": "Hook — full-bleed", "s": "Hard open on the subject's face, hard cut on the key word.", "tone": "mute" },
    { "t": 0.62,  "n": "Card 1 · The unlock", "s": "Real screenshot + green headline stamp." },
    { "t": 17.40, "n": "Card 5 · BEFORE", "s": "The old painful workflow, red stamp.", "tone": "red" },
    { "t": 19.24, "n": "Card 6 · NOW", "s": "Feature toggle flips ON on the word \"flip\".", "tone": "green" },
    { "t": 23.00, "n": "Card 7 · One catch", "s": "The honest caveat.", "tone": "amber" }
  ]
}

tone ("" | red | green | amber | mute) tints the beat's left rule, so the emotional arc of the edit is readable at a glance in the sidebar. Match it to the caption colour ranges if the edit has them.

Editor's notes (optional notes array)

The beat map says what each moment is. notes says what you did to the cut and what still needs a human decision — the things with no single timestamp. It renders as a third sidebar card above the beat map, same tones, and b may contain <b>.

jsonc
"notesHint": "What I changed, what I left alone, and what needs your call.",
"notes": [
  { "n": "35 cuts, 17.71s out, zero copy cut", "tone": "green",
    "b": "33 pauses plus two flubs. <b>Not one word of the script was removed.</b>" },
  { "n": "Runtime is 1:41, the plan said 55 to 70s", "tone": "amber",
    "b": "Trim menu with timecodes below. <b>I did not cut copy to fix this</b> — that is your call." }
]

On a paid brand deliverable this card is the point of the canvas: it is where a proposed copy trim gets timecodes and a runtime, so the reviewer can approve or reject it without opening an NLE. Put anything that could get the video rejected by the brand at the top, toned red.

2. Build

bash
node .claude/skills/video-review-canvas/scripts/build-canvas.mjs <config.json>

It ffprobes the video for real duration/fps (so the frame counter is honest), copies the media in under a version-stamped filename, drops the Site Data manifest, and prints the publish command.

3. Publish

Publish the output directory with the here-now skill's publish.sh (set HERENOW_PUBLISH to its path if it isn't at the default install location):

bash
bash "${HERENOW_PUBLISH:-$HOME/.agents/skills/here-now/scripts/publish.sh}" <outDir> \
  --title "<Title> — V1 review" --client claude-code

Add --slug <existing-slug> to update in place and keep the URL stable across versions — one durable link per project is easier for the reviewer than a new link every round.

4. Smoke-test before you send the link

Never hand over an untested canvas. Two curls and you know it works:

bash
U=https://<slug>.here.now
curl -s -o /dev/null -w "%{http_code}\n" "$U/"                       # 200
curl -s -r 0-1000 -o /dev/null -w "%{http_code}\n" "$U/<video>.mp4"  # 206 (range = it streams)
curl -s "$U/.herenow/data/comments?limit=5"                          # {"records":[...]}

If you want to prove the write path too, POST with an Origin header (see gotcha 2) and a throwaway version like "probe" — the page filters by version, so the probe stays invisible.

Reading notes back

bash
node .claude/skills/video-review-canvas/scripts/read-notes.mjs <slug> [version]

Prints each note with its stable record ID, version, status, replies and evidence, sorted by version and timestamp. Work them in timeline order against the actual frames — seek to each one and look before deciding what it means. A round where you publish but never GET the store is a round of feedback silently dropped.

Version comparison and revision tracking

Existing single-video configs still work, including download, downloadName, downloadLabel and additional downloads. Add versions to keep old media available at the same URL:

json
{
  "title": "Product launch ad",
  "outDir": "review",
  "version": "v2",
  "author": "Reviewer",
  "versions": [
    {"version": "v1", "label": "First cut", "video": "output-v1.mp4",
     "beats": [{"t": 0, "n": "Hook", "s": "Opening statement"}]},
    {"version": "v2", "label": "Revised cut", "video": "output-v2.mp4"}
  ],
  "download": "output-v2.mp4",
  "downloadName": "master-v2.mp4"
}

Paths resolve relative to the config. Version IDs must be unique, filename-safe strings up to 20 characters. Each version is probed independently; use its own beat map. version selects the initial cut; it defaults to the last listed one. Each version's SHA-256 is saved in review-config.json. Before writing anything, the builder checks every existing version receipt and media filename. Rebuilding with the same bytes is allowed; changed media needs a new version ID and file. Receipts remain in the config for versions hidden from the picker. The guard also applies when a project title changes, the input is already inside the review directory, or a download alias would overwrite old version media, so existing comments/evidence retain their meaning. A selected comparison plays beside the main video, muted and synchronized to elapsed time plus the editable comparison offset. This is not automatic alignment of differently edited timelines. Compare this change uses a note's before/after evidence points to set the correct offset for that specific change.

Every note has a stable record ID. Reviewers can reply, mark it resolved/reopen it, and attach before/after version + frame pairs with a description of the change. Evidence opens both actual rendered videos at those points. Retain those video versions in the config; a missing version is reported instead of seeking in the wrong cut. Notes from all versions are shown initially, with status and version filters. Frame pins store an integer frame, its unrounded t = frame / fps, and FPS. Use CFR review encodes for exact frame indexing; VFR sources should first be normalized for review. The model preserves fractional rates such as 30000/1001.

The existing comments collection remains compatible. Replies, status changes and evidence append records to reviewEvents, keyed by commentId, without rewriting original feedback. Like the original comments, these collections allow public read/insert; no owner key is embedded in the browser. A shared review URL is a collaborative workspace, not authenticated sign-off. Republish the generated .herenow/data.json with the page to enable the new collection. Old sites without that collection can still be read. Load/save errors are visible and a failed load does not replace the currently displayed notes.

Agent readback and export

The Export review JSON button downloads raw comments/events and a replayed notes list containing status, replies, evidence and history (schemaVersion: 2). The command-line reader uses the same replay model and follows paginated responses:

bash
node .claude/skills/video-review-canvas/scripts/read-notes.mjs <slug> --json
node .claude/skills/video-review-canvas/scripts/read-notes.mjs <slug> v1 --out revisions-v1.json
node .claude/skills/video-review-canvas/scripts/read-notes.mjs --file review-revisions.json --json

Pass the record's stable id to any future event writer; never use its visible list number, timestamp alone or the new cut's absolute seconds as identity.

Local demo without publishing

Set "storage": {"mode": "local", "key": "my-review-demo"} and serve the generated directory with a local HTTP server. This explicitly uses browser localStorage, with a visible local-only notice. Comments, replies, status and evidence survive refresh. The key isolates projects; keep it stable to resume a demo. Clearing site data clears this local review, so export JSON for backup or agent readback. The default is {"mode":"remote"} for here.now Site Data.

Optional reviewData seeds a local demo only on its first use:

json
{
  "reviewData": {
    "comments": [{"id":"note-1", "createdAt":"2026-09-18T12:00:00Z",
      "data":{"version":"v1","t":1,"frame":30,"fps":30,
        "text":"Tighten this pause.","author":"Reviewer"}}],
    "events": [{"id":"event-1", "createdAt":"2026-09-18T12:01:00Z",
      "data":{"commentId":"note-1","kind":"status",
        "status":"resolved","author":"Editor"}}]
  }
}

assets/review-model.mjs is DOM-free and exposes framePin, replayReviews, revisionExport, createLocalStore and createRemoteStore for integrations. Run local coverage with:

bash
node --test .claude/skills/video-review-canvas/tests/*.test.mjs

Shipping a new version

  1. Render the new cut as a new file — never overwrite an approved one.
  2. Bump version in the config (v2) and rebuild. The video filename gets the new suffix automatically and the version picker identifies the round. Include previous media in versions for comparison. Notes from all versions remain visible by default; reviewers can filter them.
  3. Update blurb to say what changed in this cut — it's the changelog the reviewer reads first.
  4. Publish with --slug to the same URL.
  5. Reply, leading with the link, and say which of the reviewer's notes you addressed.

Cap the review encode

A CRF-16 master peaks at 16-21 Mbps on motion-heavy beats and can stutter in a phone browser even when the file is perfect — which reads to the reviewer as "it skips around". Publish a capped copy to the canvas (-crf 22 -maxrate 6M -bufsize 12M, faststart) and keep the master for posting. Measure per-second bitrate on both before deciding a "glitch" is in the edit: ffprobe -select_streams v:0 -show_entries packet=pts_time,size summed per second.

Gotchas

Each of these cost real time.

#SymptomCauseFix
1"Why isn't the new cut on here.now?" — the reviewer sees the old videosame video filename, so the browser serves its cacheversion-stamp the filename every round (build-canvas.mjs does this); also bump the on-page version label so the round is visible
2POST /.herenow/data/comments → 403 forbidden from curl, but fine in the browserpublic Site Data writes require a matching Origin header; browsers send it, curl doesn't-H "Origin: https://<slug>.here.now" when testing by hand
3Can't delete a test record even with the owner bearer keythe delete op is refused regardless of access.deletedon't try — give throwaway records a distinct version and let the page's version filter hide them
4Notes from v1 appear on v2the store intentionally keeps all rounds visibleuse the Versions filter for the current cut; resolve addressed notes instead of hiding them
5Notes land a beat latethe reviewer pins while playing, seeing a frame that's already gonethe composer pauses and records the exact integer frame plus its unrounded timestamp; keep the frame counter visible so the reviewer can verify
6Beat map text runs together into one line.bn/.bs are inline spansthey're display:block in the shipped template — keep it that way if you retint
7Script dies with ENOENT on a path containing %20new URL(import.meta.url).pathname percent-encodes; project paths often contain spacesfileURLToPath(import.meta.url)
8Canvas published but the reviewer never opened itthe reply buried the link or omitted itfirst line of the reply is the bare URL
9build-canvas.mjs exits 0, prints nothing, creates no review/the skill was invoked through a symlink and the old entry guard compared the link path with the real pathfixed (real-path compare); if you see it on an old copy, run the script by its readlink -f path
10publish.sh → finalize failed: Invalid Site Data manifestthe account only accepts ONE Site Data collection; the default manifest declares comments + reviewEventscopy assets/data-single.json over <outDir>/.herenow/data.json before publishing (notes still work; the reply/resolve events have no store on that account)

Files

FileWhat it is
scripts/build-canvas.mjsconfig + video → publishable canvas dir (probes fps/duration, version-stamps the media)
scripts/read-notes.mjspull the pinned notes back off a published canvas
assets/canvas-template.htmlthe page itself — retint the three --a1/2/3 accent tokens per project, leave the rest
assets/data.jsonthe here.now comments + append-only review events manifest
assets/review-model.mjsshared frame, event replay, persistence and export model
assets/review-app.mjsversion/compare playback, notes, replies, status and evidence UI
tests/review.test.mjslocal model, persistence, builder compatibility and escaping checks

Requires ffprobe (or FFPROBE env var), node >= 20, and here.now credentials at ~/.herenow/credentials (the here-now skill's publish.sh). Related: branded-ad-edit, talking-head-recut, hyperframes-cli.

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 Video Review Canvas AI skill do?

Publish a finished (or draft) video cut to a frame.io-style review canvas on here.now — a player with a frame-accurate scrubber, persistent timeline notes, version comparison, replies, resolution and evidence, and a clickable beat map — then read those notes back before the next cut. Use this to DELIVER any video edit for review: reel recuts, brand-deal edits, motion-graphics ads, carousel videos, YouTube cuts, sizzle reels. Trigger whenever a cut is ready to show, or on "send me the video", "put this on a canvas", "let me leave notes on the timeline", "frame.io style", "get me a link to re...

Why use Video Review Canvas on TypingMind?

Because you install it once and use it with any model. Video Review Canvas 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 Video Review Canvas in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mrpaidsocial/mrpaidsocial-video-editor-agent/tree/main/.claude/skills/video-review-canvas. 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 Video Review Canvas?

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 Video Review Canvas?

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

Is the Video Review Canvas AI skill free?

It is published on GitHub by mrpaidsocial. 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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