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Conz-Wilts/ai-editor

Reverse-engineers a short-form creator's video format from their real numbers. Pulls a TikTok or YouTube Shorts profile (or pasted links, Instagram reels too) with view, like and save counts, transcribes the top videos word for word, measures pace, pauses and fillers, maps each video into timed beats, and writes a teardown.md someone can film from, a teardown.html page of their graphics, motion and sound, and the style.json the style-edit skill edits from (on its own it can write an edit plan as a table and .srt). Use when the user wants to analyse, break down or study a creator ("analyse @handle", "tear down @handle", "teardown of @handle", "break down this creator", "what makes this creator's videos work"), pastes TikTok, YouTube or Instagram links and asks what the pattern is, or asks to set up creator-teardown. Not for editing the user's own video like a creator (that is start), not for researching a topic, and not for cutting raw footage.

What is ai-editor?

ai-editor is a Claude Code agent skill that reverse-engineers a short-form creator's video format from their real numbers. Pulls a TikTok or YouTube Shorts profile (or pasted links, Instagram reels too) with view, like and save counts, transcribes the top videos word for word, measures pace, pauses and fillers, maps each video into timed beats, and writes a teardown.md someone can film from, a teardown.html page of their graphics, motion and sound, and the style.json the style-edit skill edits from (on its own it can write an edit plan as a table and .srt). Use when the user wants to analyse, break down or study a creator ("analyse @handle", "tear down @handle", "teardown of @handle", "break down this creator", "what makes this creator's videos work"), pastes TikTok, YouTube or Instagram links and asks what the pattern is, or asks to set up creator-teardown. Not for editing the user's own video like a creator (that is start), not for researching a topic, and not for cutting raw footage.

Works with✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/Conz-Wilts/ai-editor/tree/HEAD/.claude/skills/creator-teardown
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Documentation

Creator Teardown

Paths: ${CLAUDE_SKILL_DIR} means the folder containing this SKILL.md. On Windows, use py wherever these commands say python3.

Every question to the user goes in the question box: call the AskUserQuestion tool (2-4 options, the recommended one first). Only in an agent without that tool, ask numbered questions in text.

Output

creator-teardowns/<handle>/: teardown.md (the analysis), teardown.html (the page the user looks at), look.md (what Claude reads), style.json (every measured number, for editing in this style), plus the per-video files listed in ${CLAUDE_SKILL_DIR}/references/outputs.md. An edit plan goes to edit-plans/<name>/. Whether an edit in this style matches: scripts/match.py (references/outputs.md "Match check").

First: doctor

python3 "${CLAUDE_SKILL_DIR}/scripts/fetch.py" doctor

Run it before the first teardown in a session. Ready. means go. If it prints Not ready, or the user asks to install or set up the skill, follow ${CLAUDE_SKILL_DIR}/references/setup.md. The ElevenLabs key is optional. Never ask for it in the chat: the user saves it with setkey in their own terminal.

VPY="${CLAUDE_SKILL_DIR}/scripts/run.py"; python3 "$VPY" below (py on Windows) runs a script with the tool venv's Python wherever AI_EDITOR_HOME puts it.

Pipeline

Quick mode (called by ai-editor's start, setup or style-edit)

When another skill needs only the style.json, run exactly: Step 1 list, Step 2's commands with --top 5 (5 plus 2 control and 2 spares; pasted links: all), Step 3b's four, then Step 3c's four, then stop. The Step 0 parts question only if the caller has not asked it. Skip metrics.py, the Step 3 written passes and Steps 4-6. About 3 minutes per creator on a 14-core Mac (2-3 min measured; the spares add about 1 min at worst), twice that on an 8-core laptop. Say so first.

Step 0: Ask, once

Skip what the message already answers. One question box call:

  1. Which creators? The handle or links they named (Recommended) / I'll paste more. Several is normal: people like one creator's captions and another's pace. Each creator's style is one format.
  2. With more than one: which part from whom? Not sure yet (Recommended): ask again after Step 3b, with each creator's look.md to compare; or captions, pace (cuts, zooms, motion), visuals (graphics, face framing, cards), sound.

Step 1: Get the videos

python3 "${CLAUDE_SKILL_DIR}/scripts/fetch.py" list <handle> --platform tiktok --limit 40   # or youtube
python3 "${CLAUDE_SKILL_DIR}/scripts/fetch.py" list <name> --urls picks.txt   # pasted links, one a line

Prints a ranked table (views, x median) and writes videos.json; * marks 3x the median.

Instagram: list <handle> --platform instagram reads the newest reels with views through the user's instagram.com login in their browser (sent only to instagram.com). No login: it exits 1 with one sentence; say it, or put 6+ reel links in picks.txt for --urls (ranked by likes; say so).

Step 2: Download and transcribe the winners and a control group

python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/visual.py" download <handle>
python3 "${CLAUDE_SKILL_DIR}/scripts/fetch.py" transcribe <handle>

--top N downloads and transcribes N plus 2 control videos (the 2 closest to the median; --control changes the 2) plus 2 spares for the format check (--spare), all of the creator's usual length when enough are. Pasted links: every one. The default --top 8 is 12 videos; --top 5, the fast run, is 9; pass it to both commands. Both print the count first. --ids <id>,<id> picks exact videos. The median ones are the control: a move that appears in the 500K video and the 3K video is that creator's habit, not the reason the 500K one worked. Keep at least 2.

Every measuring script works on several videos at once (CT_JOBS=1 turns that off on a small laptop).

Say the time first (download plus transcription, about 10 s a video on a 14-core Mac). Whisper (free, local) is the default; with an ElevenLabs key transcribe uses Scribe, which keeps every filler ($0.22 an hour of audio; give the estimate before 10+ videos). If filler counts lean on Whisper transcripts, say so in the teardown.

Step 3: Measure, then analyse

python3 "${CLAUDE_SKILL_DIR}/scripts/metrics.py" <handle>

Writes metrics.json (WPM per third, pauses, fillers, sentence length, you/I/we, repeated phrases), all off the word timestamps.

Run Step 3b first, so the passes skip the videos format.json left out. Then load ${CLAUDE_SKILL_DIR}/references/analysis-framework.md and run its passes. For pass 1, launch one video-analyst agent per video, all in one message (they run at once): give each the absolute creator-teardowns/<handle>/ path and one id. Each returns a JSON beat map; merge them into the tables. Without an Agent tool, read transcripts/<id>.timed.txt yourself, one video at a time. Never read transcripts/<id>.json or metrics.json whole:

  1. Beat map + timings. Every video split into labelled beats with second-marks and word counts.
  2. Voice/vocab profile. Sentence length, person, commands, recurring phrases, and the moves the user would not say.
  3. Visual pass. Always: Step 3b. It is what lets the user edit their own video in this style.
  4. Card events, only when the ask covers the creator's cards and graphics, or an edit plan is coming. Writes events.json in the shape in ${CLAUDE_SKILL_DIR}/references/edit-plan.md. scripts/strip.py needs ffmpeg, numpy and pillow.

Do not skip the timings: "hook lands by 0:03 and runs 14 words, items are 8-11 s each" is a format someone can film; "hook, then list, then close" is not.

Step 3b: The visual pass (measured, no images for you to read)

python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/visual.py" measure <handle>
python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/look.py" measure <handle>
python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/format_check.py" check <handle>

Then the sheet pass (4 below), then python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/lookpass.py" merge <handle>.

Run them after transcribe (look.py matches OCR text to the spoken words).

  1. visual.py: cuts and their kinds, shot lengths, zooms, pans and shake.
  2. look.py: OCR 3 frames a second and YuNet faces: the captions (place, size, words, case, colours, weight, entrance), face framing, graphics share, palette, motion personality. About 40 s a video.
  3. format_check.py: a style is ONE format, not a blend of all they post. It groups the videos by frame, face, captions, shot length and length; a majority group of 3+ is kept, the rest move to video/left-out/ (format.json says why; references/look-pass.md). Tell the user its first line when any differ ("Using 4 of 6: 2 look like a different format."); if mixed, ask for more videos of one kind.
  4. The sheet pass: one Agent call with model: "haiku" reads sheets/<id>.jpg for each kept video (one 3 x 3 sheet each, made by visual.py) and writes video/<id>.look-ai.json per lookpass.py prompt: font, graphics style, transitions, what is not generic. Never a number. Exact brief in references/look-pass.md. lookpass.py merge folds them into style.json.

Then read look.md (under 48 lines). Do not open frames or sheets. Every field is in ${CLAUDE_SKILL_DIR}/references/look-pass.md. Numbers in style.json come from code; never edit them by hand. Where look.md and the model's words disagree (say motion: punchy measured, smooth named), the measured one wins and the teardown says both.

No OCR engine (doctor says optional): captions stays unmeasured and look.md says so. Install it (doctor prints the line) rather than reading sheets yourself.

Step 3c: The deep pass and the page

python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/graphics.py" measure <handle>
python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/sound.py" measure <handle>
python3 "$VPY" "${CLAUDE_SKILL_DIR}/scripts/report.py" build <handle>

Each measure prints one summary line and the file it wrote (--json prints the full block; not needed: look.md has the numbers). Run them after Step 3b (graphics.py masks the speaker with look.py's faces). They add every graphic (cropped, its kind, where it sits, how it enters and exits as a fitted GSAP ease), cut kinds and camera moves (visual.py), sound effects and music, the first 3 seconds of each video, winners against the control group, and an AI-tell scan of the creator's own look. Fields, methods, ceilings and cost: ${CLAUDE_SKILL_DIR}/references/teardown-page.md. After graphics.py measure, run the haiku kinds pass in that reference (or the kinds stay the code's guess), then sound.py and report.py.

Then open creator-teardowns/<handle>/teardown.html for the user (open on a Mac, start on Windows, xdg-open on Linux) and offer to publish it as an artifact. Do not read the page or its images yourself: look.md has the numbers. Tell the user in two or three lines what the winners do differently (the Winners: lines in look.md, with n) and any BAN-level AI tell in their look.

Then ask in the question box (the page only shows the evidence; it has nothing to tick), one multi-select question: "Which parts of @ should your edit copy?" Options: captions, pace, graphic kinds, entrance motion, layout, sound (at most 4 options a question: split into two questions if needed), each option's description one measured line from look.md (e.g. "slides in 0.25 s with overshoot"). If any BAN tell sits in a part they pick, say which before they confirm. Save the answer:

python3 "${CLAUDE_SKILL_DIR}/scripts/editplan.py" blend <handle>-mine --from <handle> \
    --take captions,pace,graphics,entrances,layout,sound    # only the parts picked

style-edit reads creator-teardowns/<handle>-mine/style.json; a part left out uses its default. report.py build and editplan.py blend print the style's skill (skill edit-like-<handle> -> <path>, with ai-editor installed): tell the user its name. Feedback on that style edits that skill.

Step 3d: Several creators (when the user named more than one)

Follow ${CLAUDE_SKILL_DIR}/references/blend.md: Steps 1 to 3b per creator, look.md side by side, then editplan.py blend.

Step 4: Write the teardown

Fill ${CLAUDE_SKILL_DIR}/references/teardown-template.md into creator-teardowns/<handle>/teardown.md. Every video gets a stats line, verbatim transcript, beat table with timings, patterns to keep and patterns to reject.

Step 5 (optional): Turn it into a skill

Ask whether the format is worth keeping. If yes, follow ${CLAUDE_SKILL_DIR}/references/skill-handoff.md (the format skill, beside the style's own skill).

Step 6 (optional): Plan the user's video in the creator's style

When the user hands over their own video, follow ${CLAUDE_SKILL_DIR}/references/edit-plan.md. It needs the creator's events.json from the visual pass; it does not render.

If a script stops

messagedo
doctor Not ready: ...follow references/setup.md for each line it lists
yt-dlp returned nothing / parsed 0 videosread the hint it prints: update yt-dlp (doctor shows how), or ask for single video links and use --urls
Instagram profile failssay its sentence: log in to instagram.com in Chrome, or 6+ reel links (Step 1 "Instagram")
... Fix the key, then rerun (transcribe)the ElevenLabs key is wrong: the user re-saves it with setkey in their own terminal, or drop it and use Whisper
no videos in ... Run download firstrun visual.py download <handle>
download failed: <ids> (exit 1)rerun with the --ids it prints; if they fail again, say the sample is smaller and go on
yt-dlp not foundrun fetch.py doctor and fix what it lists
some videos fail to transcriberetry with --ids, then say which failed; never fill the gap

Hard rules

  • Real numbers or nothing. Every claim about what works cites the view count next to it. Never say a video "went viral" without the number.
  • Verbatim transcripts only. Never summarise a video that was not transcribed.
  • Structure is copied, voice is not. Every teardown states what to reject.
  • Median control group. At least two mid-performers transcribed, or the analysis cannot separate the format from the creator's habits.
  • Never invent a transcript line, or a stat or quote for a card. If transcription failed, retry with --ids, then say it failed.
  • Never ask for the ElevenLabs key in the chat.
  • Measured numbers only in style.json. pace and zoom come from visual.py; captions, face, graphics and motion from look.py; only words (look, font_match) from the model.
  • Read look.md, not frames. Open a frame only to settle a question look.md cannot answer, and say which one. The user gets teardown.html; Claude gets look.md.
  • Winners against control: say n. Never call a difference proven; "every winner" only means no overlap in this sample.

Individual skills in this repo

This repo contains 4 individual skills — each has its own dedicated page.

Conz-Wilts/ai-editor

Turns one long video or podcast (a local file or a YouTube link) into the best 3-5 short vertical clips, each cut and styled in the user's profile style. Transcribes once, splits the talk into candidate clips at sentence boundaries inside the chosen length, scores every candidate in code (speech rate, loudness, laughter, numbers and names said), shows the shortlist on one page with a preview of each, then trims, cuts and style-edits each pick as its own edit. Use when the user says "clip this video", "make shorts from my podcast", "find the best clips", "cut this into TikToks", "turn this YouTube video into reels", "repurpose this long video", or drops a long video (over about 3 minutes) or a YouTube link and wants short clips. Not for editing one short take (that is start or cut), and not for measuring someone else's style (that is creator-teardown).

Conz-Wilts/ai-editor

Cuts raw talking-head footage into a clean jump cut. Takes one take or several joined (mp4 or mov, any aspect, 4K phone footage is fine), transcribes it word by word, removes retakes, false starts, fillers and dead pauses, and renders cut.mp4 at the source resolution plus words.json timed to the cut. The user approves the cut on a page with the whole transcript and every removed word struck through. Use when the user says "cut my video", "just cut it", "remove my mistakes", "cut out the retakes", "remove the pauses", "clean up this take", "jump cut this", "tighten this clip", or asks only for the cut of a raw take; start calls it for a full edit. Runs before style-edit. Not for a general request to edit a video or to make it look like a creator (that is start), not for adding captions, zooms or cards (style-edit), and not for analysing someone else's videos (creator-teardown).

Conz-Wilts/ai-editor

Generates new footage with Higgsfield and puts it on the edit's timeline. Prices every generation first, asks the user with the dollar amount (and, for their own footage, whether it may be uploaded to Higgsfield), then submits, waits, downloads into the edit's generated/ folder and adds the result to the timeline's Generated track. Covers motion transfer on a clip of the user's video (their moves onto a character or styled look), b-roll from a sentence (text to video), a still brought to life (image to video) and stills for cards (text to image). Use when the user says "generate b-roll", "make a shot of", "AI b-roll", "generate a clip", "use Higgsfield", "motion transfer", "put my moves on", "restyle this part with AI", or asks for footage they did not film. Not for animated diagrams or motion graphics (motion), not for real screenshots and logos (overlays, logo-pack), and not for the cut (cut).

Conz-Wilts/ai-editor

For the editor's maintainers. Turns what users corrected into lasting fixes in this repo, for every user, enforced by code and tests rather than notes. Reads the suggestions users marked "would help everyone" (~/.ai-video-editor/suggestions.jsonl), taste rules said twice (taste.py report), preview corrections (edits/*/corrections.jsonl), review-page notes (*/review.json) and open GitHub issues labelled feedback; groups repeats; for each item the maintainer accepts, writes the general rule into PRINCIPLES.md and the skill that owns it, adds or tightens the check that enforces it, adds a test that fails before and passes after, and runs the full test set. Use when the maintainer says "improve the editor", "turn feedback into fixes", "process suggestions", "what are users complaining about", "go through the feedback issues". Not for one user's own preferences (that is taste), and not for editing a video.

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