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SKILL verified MIT Self-run

Loom Analyze

skill-ao92265-claude-code-playbook-loom-analyze · by ao92265

Download a Loom video share URL and produce an audio transcript (whisper) plus optional keyframes for visual analysis. Triggers on any loom.com share URL the user provides, or on phrases "transcribe this loom", "analyze this loom", "watch this loom", or "/loom-analyze" followed by a URL.

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Install

$ agentstack add skill-ao92265-claude-code-playbook-loom-analyze

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Loom Analyze

Local pipeline (no API key, no third-party MCP) that turns a Loom share URL into:

  1. A plain-text transcript via whisper.
  2. Optional keyframe PNGs for visual analysis when the user asks about UI / screen content.

When to invoke

  • User pastes a https://www.loom.com/share/... URL.
  • User says "transcribe", "analyze", or "watch this loom".
  • /loom-analyze is typed.

How to run

The shell wrapper lives next to this file: loom-analyze.sh. Always call it through Bash.

${CLAUDE_PLUGIN_ROOT:-$HOME/.claude}/skills/loom-analyze/loom-analyze.sh  [--model M] [--frames N] [--keep]

Flags:

  • --model base|small|medium|large — whisper accuracy/speed tradeoff. Default base (~140 MB download first time).
  • --frames N — also extract one PNG every N seconds into frames/. Skip this for transcript-only (faster).
  • --keep — keep MP4/MP3 after transcribing. Default deletes them.

Output goes to ~/Downloads/loom-transcripts//. The transcript is printed to stdout, so the agent gets it in the tool result.

Decision rules for the agent

  • Transcript-only is the fast default. Don't request frames unless the user asks about visuals, UI, screen content, or a demo walkthrough.
  • Long videos (>10 min): stick to --model base. --model small or larger only when the user specifically complains about transcript accuracy.
  • When frames are extracted: read the PNGs from ~/Downloads/loom-transcripts//frames/ via the Read tool when the user asks what's on screen at a given time. Don't bulk-read every frame; pick the ones that match the question.
  • First-time setup: if the script exits with "Missing dep", run ${CLAUDE_PLUGIN_ROOT:-$HOME/.claude}/skills/loom-analyze/setup.sh once, then retry.

Setup (per-machine, one-time)

bash ${CLAUDE_PLUGIN_ROOT:-$HOME/.claude}/skills/loom-analyze/setup.sh

Installs yt-dlp, ffmpeg, openai-whisper via Homebrew + pip. Idempotent.

For machine requirements, model disk sizes, troubleshooting, and limitations, see references/usage.md.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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