Video Clipper

What it does

When your agent uses it

• Tasks that involve Transcription
• Tasks that involve Podcasting
• Tasks that involve Video production

Example prompts

• “Use the video-clipper skill to repurpose long-form video (podcasts, interviews, talks) into short-form vertical clips for Instagram Reels, TikTok…”
• “/video-clipper”

Requirements

• Python 3
• A credential in KLAP_API_KEY
• A credential in CAPTIONS_AI_API_KEY
• Pre-approved tools (allowed-tools): Bash, Read, Write, Edit, Grep, Glob, WebSearch, WebFetch

Workflow steps

8 steps, taken from the step headings in SKILL.md.

1. 1Get the Video
2. 2Transcribe with Whisper
3. 3Identify Best Moments (Viral Scoring)
4. 4Extract Raw Clips
5. 5Reframe with Klap
6. 6Add Animated Captions with Captions.ai
7. 7Generate Platform Captions
8. 8Output

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Bash
  • Read
  • Write
  • Edit
  • Grep
  • Glob
  • WebSearch
  • WebFetch

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • brew
  • ffmpeg
  • claude
  • pip
  • ffprobe
  • yt-dlp
  • curl
  • whisper
  • apt

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

• Network

  Hosts in commands or code, which the agent is likely to contact:

  • api.klap.app
  • api.mirage.app
  • youtube.com

  Also links to:

  • klap.app
  • captions.ai
  • platform.mirage.app

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

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • KLAP_API_KEY
  • CAPTIONS_AI_API_KEY

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

Context cost

Video Clipper loads about 3.1k tokens when it runs. Until then it costs about 68 tokens; SKILL.md has 1,070 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~68

When it runs · the whole SKILL.md, loaded when a task matches

~3.1k

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

• NoteMentions a .env fileSKILL.md:20

  - **API Keys** in `.env` file (project root or any parent directory):

• NoteMentions a .env fileSKILL.md:363

  Add these to your `.env` file:

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Bash, Read, Write, Edit, Grep, Glob, WebSearch, WebFetch

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 gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,070 words, ~3,106 tokens.

name

video-clipper

description

Repurposes long-form video (podcasts, interviews, talks) into short-form vertical clips for Instagram Reels, TikTok, and YouTube Shorts. Handles transcription, moment selection, clip extraction, speaker-tracked reframing (16:9 to 9:16), and animated captions.

allowed-tools

Bash, Read, Write, Edit, Grep, Glob, WebSearch, WebFetch

user-invocable

true

argument-hint

video-file-path-or-url

# Verify it exists and get duration
ffprobe -v quiet -print_format json -show_format "video.mp4"

yt-dlp -f "bestvideo[height<=720]+bestaudio/best[height<=720]" --merge-output-format mp4 -o "<workdir>/source.mp4" "<URL>"

curl -L -o "<workdir>/source.mp4" "<URL>"

import whisper

model = whisper.load_model("base")
result = model.transcribe("source.mp4", language="en", word_timestamps=True)

ffmpeg -y -ss <start> -to <end> -i source.mp4 -c copy clip<N>-raw.mp4

import requests

headers = {
    "Authorization": f"Bearer {klap_key}",
}

# Direct file upload
with open("clip-raw.mp4", "rb") as f:
    r = requests.post(
        "https://api.klap.app/v2/tasks/video-to-video",
        headers=headers,
        files={"video": f},
        data={
            "language": "en",
            "editing_options": '{"captions":false,"reframe":true,"emojis":false,"intro_title":false}',
            "dimensions": '{"width":1080,"height":1920}'
        }
    )
task_id = r.json()["id"]
output_id = r.json().get("output_id")

# Poll every 30 seconds
r = requests.get(f"https://api.klap.app/v2/tasks/{task_id}", headers=headers)
status = r.json()["status"]  # "processing" or "ready"
output_id = r.json()["output_id"]  # project ID when ready

# Request export
r = requests.post(
    f"https://api.klap.app/v2/projects/{output_id}/exports",
    headers=headers,
    json={}
)
export_id = r.json()["id"]

# Poll export every 15 seconds
r = requests.get(
    f"https://api.klap.app/v2/projects/{output_id}/exports/{export_id}",
    headers=headers
)
# When status != "processing", download from src_url
download_url = r.json()["src_url"]

headers = {"x-api-key": captions_key}

with open("clip-reframed.mp4", "rb") as f:
    r = requests.post(
        "https://api.mirage.app/v1/videos/captions",
        headers=headers,
        files={"video": f},
        data={"caption_template_id": "ctpl_DxflLOnuKkb198FNdI9E"}
    )
video_id = r.json()["video_id"]

# Poll every 10 seconds
r = requests.get(f"https://api.mirage.app/v1/videos/{video_id}", headers=headers)
status = r.json()["status"]  # QUEUED → PROCESSING → COMPLETE or FAILED

r = requests.get(
    f"https://api.mirage.app/v1/videos/{video_id}/content",
    headers=headers,
    allow_redirects=True
)
with open("clip-FINAL.mp4", "wb") as f:
    f.write(r.content)

<output-dir>/
  clip1-FINAL.mp4          # Ready-to-post clip
  clip2-FINAL.mp4
  clip3-FINAL.mp4
  captions.md              # All platform captions for each clip
  summary.md               # Overview: source video, clips made, scores, costs

User provides video
      ↓
[ASK] "Do you want me to pick the best moments, or do you have specific timestamps?"
      ↓
Whisper transcribes locally (free)
      ↓
Claude scores moments on viral rubric (hook, quotability, emotion, self-contained, surprise)
      ↓
[ASK] "Here are the top N moments with scores. Approve, adjust, or add your own?"
      ↓
FFmpeg extracts raw clips (free)
      ↓
Klap reframes to 9:16 with speaker tracking (~$2/clip)
      ↓
Captions.ai adds animated captions (~$0.15/clip)
      ↓
Claude writes platform-specific captions
      ↓
Output: final clips + captions, ready to post

KLAP_API_KEY=kak_xxxxx
CAPTIONS_AI_API_KEY=sk-xxxxx

© gooseworks-ai, 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 skills/design/packs/video-production/video-clipper of gooseworks-ai/goose-skills.

• SKILL.md
• skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

• criptogus/agent-evolve-network.claude/skills/video-clipper