A video-editing workflow that finds notable moments in a long English video and turns them into separate short clips. It can change widescreen video to vertical format and add word-by-word captions.

Use it to make short social-media clips from spoken videos, especially when you need face-focused vertical edits and burned-in captions.

It reduces the work of watching a full recording, locating highlights, reframing the picture, and timing subtitles by hand.

clipify — Skill for Claude Code and Codex by ZJU-REAL

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add ZJU-REAL/Easel --skill clipify

Clone the repo

git clone --depth 1 https://github.com/ZJU-REAL/Easel

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/zju-real/easel/clipify.svg)](https://agentmods.dev/skills/zju-real/easel/clipify)

Your own site

<a href="https://agentmods.dev/skills/zju-real/easel/clipify"><img src="https://agentmods.dev/badge/skills/zju-real/easel/clipify.svg" alt="Measured on agentmods" height="20"></a>

Per session 131 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,670 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00131	$0.02670
Opus 5	$0.00066	$0.01335
Sonnet 5	$0.00026	$0.00534
Haiku 4.5	$0.00013	$0.00267

Measured 7d ago against content hash 690ee973cac9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

clipify scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/analyze.py, scripts/audio_align.py, scripts/build_ass.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/openclaw/clipify/SKILL.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Clipify

Find the funniest moments in a video, cut them as standalone clips, optionally reformat 16:9 → 9:16 (face-pan or split-screen), and burn opus-style word-by-word captions.

Inputs

A video file path (the user will provide it; otherwise ask)
Optional: requested format (9:16, 16:9, 1:1) — if not given, ask after candidates are picked
Optional: subtitle style preference — if not given, ask before captioning

Tooling (use only the fastest path)

Whisper: whisper --model tiny.en --word_timestamps True --output_format json (≈10× faster than small.en; quality fine for English). For non-English: --model base (drop --language).
ffmpeg: hardware decode is optional and platform-specific — use -hwaccel auto, or omit it (macOS: videotoolbox; Linux: vaapi/cuda/none). Add -preset ultrafast for renders. Use -c:v libx264 -crf 20 for the final master.
Numpy for audio alignment (FFT cross-correlation). No scipy/cv2 needed.
Scripts: <skill-dir>/scripts/ (where <skill-dir> is the directory containing this SKILL.md — typically ~/.claude/skills/clipify/)
- analyze.py — speaker timeline from two ROI motion files
- build_pan.py — ffmpeg crop x-expression with hard cuts
- build_ass.py — opus-style ASS captions from whisper JSON
- audio_align.py — find offset of a sub-clip in a longer source

Working dir: /tmp/clipify/ (mkdir at start, leave artifacts for debugging).

Workflow

Step 1 — Find the funniest parts

mkdir -p /tmp/clipify
ffmpeg -y -i "$VIDEO" -vn -ac 1 -ar 16000 /tmp/clipify/audio.wav
whisper /tmp/clipify/audio.wav --model tiny.en --word_timestamps True --output_format json --output_dir /tmp/clipify --language en

Read the resulting JSON (or .txt) and pick 3–5 candidate clips. Funny signals to scan for:

Punchlines and reactions: words like "what", "wait", "no way", laughter, "haha", swearing
Reversal moments: setup question → unexpected answer
Awkward pauses: Whisper segment with long gap, or filler ("uh", "um")
Self-roast / quotable one-liners: short declarative sentences that stand alone
Audio peaks: detect via ffmpeg -af volumedetect or look for rapid back-and-forth (alternating short Whisper segments)

Read the full file on GitHub · 172 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 172 lines · 131 tokens per session scan A 690ee973cac9

Subscribe to this mod's changes

clipify is a skill published in the GitHub repository ZJU-REAL/Easel (360 stars, last pushed yesterday), licensed Apache-2.0. It adds 131 tokens to every session and 2,670 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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