Agent skill

Review Video With Pingfusi

by alex-durango in alex-durango/pingfusi

Have any video reviewed by a real human, through iterative pingfusi review rounds.

MITAuto-check passedMedia & Creative

Install Review Video With Pingfusi

skills CLI
$ npx skills add alex-durango/pingfusi --skill review-video-with-pingfusi -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alex-durango/pingfusi review-video-with-pingfusi --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alex-durango/pingfusi.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skill/review-video-with-pingfusi .claude/skills/review-video-with-pingfusi && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-video-with-pingfusi
GitHub stars
125
Token cost
~1.8k tokens
SKILL.md length
811 words
Files
2
Skills in repo
5
Repo updated
First seen
Licence
MIT

At a glance

Have any video reviewed by a real human, through iterative pingfusi review rounds.

  • Works in 6 steps: Run pingfusi doctor. If the review login… → Assemble the review context before… → Render the MP4 and publish it through… → …
  • Asked to review this video
  • SKILL.md covers Non-negotiables and Workflow
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Video With Pingfusi is an agent skill from alex-durango/pingfusi. Have any video reviewed by a real human, through iterative pingfusi review rounds. Use when asked to "review this video", "check the rendered video", "does this video match the prompt/brief", "what do people think of this ad/trailer/demo", or after rendering a Remotion composition or AI-generated clip that no test can judge. Works with or without a brief behind the video, and you author the questions and the verdict wording. Do not use for web pages (use fix-with-pingfusi or beautify-with-pingfusi) or for…

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Media & Creative, covering Design to code and Video production. It works with Remotion and Model Context Protocol. The repository describes itself as: MCP server + CLI that puts a real human in your coding agent's loop. It publishes work mid-task, a reviewer pins what's wrong and returns a verdict, and the agent iterates until… The licence is MIT.

When your agent uses it

  • Asked to review this video
  • Check the rendered video
  • Does this video match the prompt/brief
  • What do people think of this ad/trailer/demo

Example prompts

  • “review this video”
  • “check the rendered video”
  • “does this video match the prompt/brief”
  • “/review-video-with-pingfusi”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Run pingfusi doctor. If the review login is missing, stop and have the user
  2. Assemble the review context before rendering anything final: every prompt in
  3. Render the MP4 and publish it through Pingfusi hosting by default
  4. File the round against a caller-owned state file
  5. The filing command automatically chains client-safe wait legs until feedback. If a
  6. Repeat until core.review.verify(stateFile) returns ok === true on your

What it can do on your machine

Read from SKILL.md and the folder at commit cec753b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Video With Pingfusi loads about 1.8k tokens when it runs. Until then it costs about 147 tokens; SKILL.md has 811 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~147
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alex-durango/pingfusi at commit cec753b, republished under its MIT licence (© alex-durango). 811 words, ~1,821 tokens.

Download SKILL.mdSave it as .claude/skills/review-video-with-pingfusi/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
review-video-with-pingfusi
description
Have any video reviewed by a real human, through iterative pingfusi review rounds. Use when asked to "review this video", "check the rendered video", "does this video match the prompt/brief", "what do people think of this ad/trailer/demo", or after rendering a Remotion composition or AI-generated clip that no test can judge. Works with or without a brief behind the video, and you author the questions and the verdict wording. Do not use for web pages (use fix-with-pingfusi or beautify-with-pingfusi) or for pixel-matching a site (use pixel-perfect-clone).

Review a video with pingfusi

A machine can render a video; it cannot tell you whether the result lands. This skill puts the video in front of a real human reviewer who scrubs it, pins comments to exact timestamps, draws on frames, answers your questions, and returns a verdict — then you fix the source, re-render, and refile until it passes.

Two shapes, one tool. Matching your own render against a brief you wrote is the iteration loop it was built for. But the brief is optional: point it at any video and ask your own questions when there is no prompt behind it to match.

Non-negotiables

  • Publish before review. The reviewer is remote: video_url must be a public, long-lived MP4 whose host answers Range requests with 206 + Content-Range (the service probes it at file time and refuses the round otherwise). A new render is a new URL — never mutate the bytes behind a URL a round already cites.
  • The brief must be honest, when there is one. current_brief is what the video must match NOW. Superseded prompts go into prompt_history marked replaced — never silently dropped; the reviewer resolves conflicts by state, not guesswork. requirements are concrete, checkable claims, each naming the prompt_ids it came from. All three are OPTIONAL: a video you did not generate from prompts you control — a competitor's ad, a tutorial, a clip someone sent you — has no brief, and you say what to judge with video_intro and steps instead.
  • Everything except title is private until claim. steps, verdict_options, video_headline, video_intro, the brief, the history and the requirements all travel in a payload delivered to exactly one reviewer when they take the job, so a question may quote the brief it belongs to. title is the opposite: it is the PUBLIC headline on the row every reviewer sees while browsing, before anyone claims. Put nothing in it you would not publish.
  • Ask what you actually want to know. Omit steps and verdict_options and you get the generic prompt-match questionnaire with Matches the prompt / Needs another pass, which is right for a render-against-brief loop and wrong for almost everything else. A round asking "would you keep watching past five seconds?" tells you something the generic pair cannot.
  • Act on feedback in the SOURCE. A timestamped comment means a fix in the composition code, the prompt, or the asset that produced that moment — never a hand-patched frame or a trimmed clip to dodge the note.
  • Never approve your own render, and never infer approval from prose. Done is a fresh core.review.verify(stateFile) returning ok === true on the declared verdict.
Show full SKILL.md (509 more words)Show less

Workflow

  1. Run pingfusi doctor. If the review login is missing, stop and have the user run pingfusi setup; there is no offline substitute for a human verdict.

  2. Assemble the review context before rendering anything final: every prompt in authored order (active / replaced / context), the distilled current_brief, and requirements with prompt provenance. The complete context caps at 250 KB.

  3. Render the MP4 and publish it through Pingfusi hosting by default:

    sh
    pingfusi publish <render.mp4> --name <name>-round-1 \
      --record .pingfusi/video/<name>/round-1.json --json

    The command creates the player wrapper, uploads immutable bytes, and returns a direct asset_url; use that value as video_url. Pingfusi serves it with 206 and Content-Range, so the native player can scrub. The current hosted-video cap is 25 MB per render. If a render cannot fit after reasonable encoding, use another long-lived public host that serves Range requests; do not introduce a live-site tunnel for a file.

  4. File the round against a caller-owned state file:

    Matching a render against a brief you wrote — the iteration loop:

    js
    const core = require("pingfusi/packages/core");
    const { ping_id } = await core.review.file(stateFile, {
      media_type: "video",
      video_url,
      current_brief,
      prompt_history,  // [{ id, text, state: "active"|"replaced"|"context", replaced_by? }]
      requirements,    // [{ id, text, prompt_ids }]
      n_target: 1,
      approve_verdicts: ["Matches the prompt"], // local bookkeeping, stripped before the wire
    });

    Judging any other video — no brief, your own questions:

    js
    const { ping_id } = await core.review.file(stateFile, {
      media_type: "video",
      video_url,
      title: "Launch video, third cut",          // PUBLIC — on the browsing row
      video_headline: "Does this ad land?",      // private, top of the reviewer's panel
      video_intro:
        "Watch it once at full speed, then scrub back to anything that made you hesitate.",
      steps: [                                    // private; your questions
        { text: "Would you keep watching past the first five seconds?", options: ["Yes", "No"] },
        { text: "What is this selling, in your words?" },
      ],
      verdict_options: ["Ready to run", "Needs another cut"],   // private
      n_target: 3,
      approve_verdicts: ["Ready to run"],
    });

    Mix them freely: keep requirements without a prompt_history when you want timestamped notes linked to specific claims, or send a current_brief with your own steps. The one rule is that the reviewer must have something to answer — a brief, requirements, steps, or an intro. url and draft_url must be absent; video mode refuses them.

  5. The filing command automatically chains client-safe wait legs until feedback. If a raw MCP leg returns pending, immediately call pingfusi_wait again; never return pending to the user or file a duplicate. Each leg renews the short idle lease; passive result/verify reads do not (a lapse only pulls the round from the feed for new claims; a reviewer mid-review can still finish). When results land, read the envelope: comments arrive sorted by video_anchor.time_ms, drawn annotations in normalized frame coordinates (0 = left/top, 1 = right/bottom), questionnaire answers attached to their questions. Fix every noted moment in the source, re-render, publish the NEW file under a new receipt/URL, and refile with the same context — update current_brief/requirements only if the user's ask actually changed.

  6. Repeat until core.review.verify(stateFile) returns ok === true on your declared approving verdict — Matches the prompt by default, or whichever of your own verdict_options you named in approve_verdicts. Record the receipt; stop only on approval or when the user says stop.

© alex-durango, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skill/review-video-with-pingfusi of alex-durango/pingfusi.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit cec753b

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Questions about Review Video With Pingfusi

What does Review Video With Pingfusi do?

Have any video reviewed by a real human, through iterative pingfusi review rounds. Review Video With Pingfusi is an agent skill from alex-durango/pingfusi. Have any video reviewed by a real human, through iterative pingfusi review rounds.

When should I use Review Video With Pingfusi?

Review Video With Pingfusi fits situations like: asked to review this video; check the rendered video; does this video match the prompt/brief; what do people think of this ad/trailer/demo.

How do I install Review Video With Pingfusi in Claude Code?

Run `npx skills add alex-durango/pingfusi --skill review-video-with-pingfusi -a claude-code`. Or copy the skill folder (skill/review-video-with-pingfusi in alex-durango/pingfusi) into .claude/skills/review-video-with-pingfusi in your project. Claude Code loads it when a task matches its description.

How do I install Review Video With Pingfusi in Codex?

Run `npx skills add alex-durango/pingfusi --skill review-video-with-pingfusi -a codex`. Or copy the skill folder (skill/review-video-with-pingfusi in alex-durango/pingfusi) into .agents/skills/review-video-with-pingfusi in your project. Codex loads it when a task matches its description.

Can I use Review Video With Pingfusi in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alex-durango/pingfusi --skill review-video-with-pingfusi -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-video-with-pingfusi, .gemini/skills/review-video-with-pingfusi, .github/skills/review-video-with-pingfusi and .opencode/skills/review-video-with-pingfusi in your project.

What does Review Video With Pingfusi need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Video With Pingfusi is instructions for the agent only.

Does Review Video With Pingfusi access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Video With Pingfusi safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Video With Pingfusi use?

Review Video With Pingfusi is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Video With Pingfusi use?

About 1.8k tokens (SKILL.md is roughly 7.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Video With Pingfusi?

Skills that share tags, products or a category with Review Video With Pingfusi: Making Demo Videos (nukeop/nuclear, 19k stars), Avatar Video (calesthio/OpenMontage, 66k stars), Remotion Production (DojoCodingLabs/remotion-superpowers, 132 stars) and Remotion Video Builder (hashgraph-online/awesome-codex-plugins, 1.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Video With Pingfusi?

alex-durango (a GitHub user) maintains it in alex-durango/pingfusi, which has 125 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on September 9, 2026.

Source: alex-durango/pingfusi on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.