gupsammy/claudest/extract-frames

Claude Code and agent skill by gupsammy/claudest

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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.

Install

npx vskill@latest install gupsammy/claudest/extract-frames

SKILL.md

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.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
255
Repository forks
16
7-day trend score
28

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