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Extract Frames

Community
gupsammy
extract-frames

Extracts first and/or last frames of every shot from a video using adaptive scene detection. Use this skill when the user says "extract frames", "get shot frames", "pull frames", "shot breakdown", "scene detect", "first frame of each shot", "last frame of each shot", "extract shots from video", or wants to extract key frames at shot cut points from a video file.

Overview

Publishergupsammy
RepositoryClaudest
Skill nameextract-frames
Stars
275
Forks
16
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Extract Frames 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/gupsammy/Claudest/tree/main/plugins/claude-content/skills/extract-frames
  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/gupsammy/Claudest.git /tmp/Claudest
mkdir -p .claude/skills
cp -r /tmp/Claudest/plugins/claude-content/skills/extract-frames .claude/skills/extract-frames
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Extract Frames 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 Extract Frames 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 Extract Frames 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.

Extract Frames

Arguments

Parse the user's request for:

  • video path (required): path to the video file
  • --last: extract last frame of each shot instead of first
  • --first --last or --all: extract both first and last frames
  • --shots N,N,N: only process specific shot numbers (1-indexed)
  • --threshold X: override auto-detected threshold (skip user confirmation)

Default: extract first frame of every shot.

Workflow

Phase 0: Setup

  1. Validate the video file exists.
  2. Get video metadata via ffprobe:
bash
ffprobe -v error -show_entries format=duration -show_entries stream=r_frame_rate,width,height,codec_name -of default "$INPUT"
  1. Parse fps (needed for last-frame calculation) and duration.
  2. Create output directory next to the video: {video_basename}_frames/. If it already exists, check for scores.txt — if present, skip Phase 1.

Phase 1: Score Dump

Dump per-frame scene scores for the entire video:

bash
ffmpeg -i "$INPUT" -vf "select='gte(scene,0)',metadata=print:file=$OUTPUT_DIR/scores.txt" -fps_mode vfr -f null - 2>&1

This is the expensive step. The output file scores.txt contains blocks like:

frame:0    pts:0       pts_time:0.000000
lavfi.scene_score=0.000000

Phase 2: Cut Detection

Parse scores.txt with this awk one-liner to get distribution + all candidate frames:

bash
awk '
/pts_time/ { split($0, a, "pts_time:"); ts=a[2]+0 }
/scene_score/ { split($0, a, "="); score=a[2]+0;
  if (score < 0.01) b1++;
  else if (score < 0.05) b2++;
  else if (score < 0.10) b3++;
  else if (score < 0.20) b4++;
  else b5++;
  total++;
  if (score > max) max=score;
  if (score > 0.05) printf "  ts=%.3fs  score=%.6f\n", ts, score;
}
END {
  print "\n--- Distribution ---";
  printf "< 0.01:      %d (%.1f%%)\n", b1, b1/total*100;
  printf "0.01 - 0.05: %d (%.1f%%)\n", b2, b2/total*100;
  printf "0.05 - 0.10: %d (%.1f%%)\n", b3, b3/total*100;
  printf "0.10 - 0.20: %d (%.1f%%)\n", b4, b4/total*100;
  printf "0.20+:       %d (%.1f%%)\n", b5, b5/total*100;
  printf "Max score:   %.6f\n", max;
}' "$OUTPUT_DIR/scores.txt"

Step 2a — Startup artifact filter: Discard any frames in the first 0.5s where score > 0.05 (a fixed preliminary value — the final user-confirmed threshold is not known yet). Fade-ins from black or codec initialization commonly produce score=1.0 spikes at t=0.03-0.08s that are not real cuts. After discarding these, recompute max score from the remaining frames.

Step 2b — Branch on max score. If max score (after startup filter) < 0.05, the video has no cuts:

  • Report: "No shot boundaries detected — single continuous shot."
  • Extract only frame at t=0 (or t=1.0s if t=0 is a black frame — check file size, <50KB indicates black).
  • Skip threshold confirmation.

Step 2c — Gap analysis and threshold. If max score >= 0.05, use the raw candidates (all frames with score > 0.05, after startup filter) to find the noise ceiling and the lowest-scoring candidate. Place the proposed threshold at the midpoint of the gap. Present to user via AskUserQuestion:

  • Score distribution summary
  • Gap analysis (noise ceiling → lowest cut, gap width)
  • Proposed threshold and resulting shot count
  • Options: Accept proposed (Recommended), Lower threshold, Higher threshold, Custom value

If --threshold was provided, skip confirmation and set $THRESHOLD_VALUE directly. Otherwise set $THRESHOLD_VALUE to the user-confirmed value before proceeding.

Step 2d — Run-based dedup. Using $THRESHOLD_VALUE from Step 2c, identify cut points. A "run" is a sequence of consecutive frames where every frame scores above threshold. The principle: an aftershock immediately follows its parent cut (consecutive frames both above threshold), while a real cut always rises from the noise floor (preceded by at least one below-threshold frame).

bash
awk -v THRESHOLD="$THRESHOLD_VALUE" '
/pts_time/ { split($0, a, "pts_time:"); ts=a[2]+0 }
/scene_score/ { split($0, a, "="); score=a[2]+0;
  if (score > THRESHOLD) {
    if (!in_run) { run_best_ts = ts; run_best_score = score; in_run = 1; }
    else if (score > run_best_score) { run_best_ts = ts; run_best_score = score; }
  } else {
    if (in_run) { printf "%.3f %.6f\n", run_best_ts, run_best_score; in_run = 0; }
  }
}
END { if (in_run) printf "%.3f %.6f\n", run_best_ts, run_best_score }
' "$OUTPUT_DIR/scores.txt"

This keeps the peak frame of each run and discards aftershocks within the same run. It correctly handles:

  • Standard cuts: isolated spikes → each kept (run length 1)
  • Cuts with aftershocks: 2-3 consecutive high frames → peak kept, echoes discarded
  • Rapid montages: each cut separated by noise frames → all kept, even at 0.12s intervals

Phase 3: Frame Extraction

For each detected cut point (plus t=0 for shot 1):

First frame (default):

bash
ffmpeg -y -ss $TIMESTAMP -i "$INPUT" -frames:v 1 -update 1 "$OUTPUT_DIR/shot_${NUM}_${TIME}s.png"

Last frame (--last):

  • For shots 1 through N-1: last_time = next_shot_timestamp - (1/fps)
  • For final shot: last_time = duration - (2/fps) (use 2 frames back, not 1 — seeking to duration - 1/fps can produce empty files near the end of some videos)
bash
ffmpeg -y -ss $LAST_TIME -i "$INPUT" -frames:v 1 -update 1 "$OUTPUT_DIR/shot_${NUM}_last_${TIME}s.png"

If a last-frame extraction produces an empty file (0 bytes), back off by another frame and retry.

Both (--first --last or --all): extract both per shot.

Filtered (--shots 3,5,7): only extract for the specified shot numbers.

Output

  • Directory: {video_basename}_frames/ next to the input video
  • First frames: shot_01_0.00s.png, shot_02_1.63s.png, ...
  • Last frames: shot_01_last_1.60s.png, shot_02_last_3.77s.png, ...
  • scores.txt always retained for re-runs at different thresholds

Report to user: total shots detected, shot list with timestamps, output directory path.

Re-run Behavior

If scores.txt already exists in the output directory, skip Phase 1 entirely and go straight to Phase 2 analysis. This makes threshold iteration instant — the user can re-run with --threshold 0.15 without waiting for the score dump again.

Shell Portability

Use pipe-based loops for frame extraction instead of array syntax (zsh handles for over arrays differently than bash):

bash
echo "0.000 2.480 4.280" | tr ' ' '\n' | awk '{printf "%02d %s\n", NR, $1}' | while read LABEL TS; do
  TS_FMT=$(printf "%.2f" "$TS")
  ffmpeg -y -ss "$TS" -i "$INPUT" -frames:v 1 -update 1 "$OUTPUT_DIR/shot_${LABEL}_${TS_FMT}s.png" 2>/dev/null
done

Edge Cases

  • Single continuous shots: Handled by step 2b. Common in fashion videos with rack-focus reveals, slow wardrobe progression, or single-take lifestyle shots.
  • Startup artifacts: Fade-ins from black produce score=1.0 at t=0.03-0.08s. Step 2a discards frames with score > 0.05 in the first 0.5s. If t=0 itself is a black frame (PNG < 50KB), extract at t=1.0s instead.
  • Aftershock spikes: Consecutive above-threshold frames (a cut + its echo). Run-based dedup in step 2d keeps only the peak of each run — no temporal window needed.
  • Rapid montages: Videos where shots are 2-4 frames long (0.08-0.16s). Each cut rises from the noise floor with 1-2 noise frames between spikes. Run-based dedup correctly preserves every cut because no two spikes are frame-adjacent. Report montage segments to the user: "Detected rapid montage from Xs-Ys with N shots."
  • Dissolves/fades: Score gradually ramps over multiple frames — forms a single run. Run-based dedup takes the peak frame as the cut point.
  • Empty file on seek: If ffmpeg produces a 0-byte PNG (common near video end), back off by one frame interval and retry.

Frequently asked questions

What does the Extract Frames AI skill do?

Extracts first and/or last frames of every shot from a video using adaptive scene detection. Use this skill when the user says "extract frames", "get shot frames", "pull frames", "shot breakdown", "scene detect", "first frame of each shot", "last frame of each shot", "extract shots from video", or wants to extract key frames at shot cut points from a video file.

Why use Extract Frames on TypingMind?

Because you install it once and use it with any model. Extract Frames 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 Extract Frames in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gupsammy/Claudest/tree/main/plugins/claude-content/skills/extract-frames. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Extract Frames?

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 Extract Frames?

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

Is the Extract Frames AI skill free?

It is published on GitHub by gupsammy. 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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