Get watched: 10 AI skills for short-form video
retention-doctor
find the second people leave and fix it
How the two work together
Claude thinks it through. Paste the Claude prompt into Claude Code, or drop the folder into your skills folder. Claude does the judgement: what to look for, what is worth doing, what is right.
Codex gets it done. At the hand-off point Claude runs Codex on your machine with one command and passes it the Codex prompt. Codex does the mechanical part and hands the result back. Claude checks it before you see it.
No API key to set up: Claude calls the Codex you already have installed. If Codex is not installed, Claude does that half itself and tells you.
Prompt for Claude
---
name: retention-doctor
description: Reads a short video's retention curve and names the exact second people leave, why, and the one edit that fixes it. Use when a video underperformed and you want a diagnosis, not a guess.
---
# Find the second people leave, and fix it
A named drop-off second, a cause, and a rewritten line for it.
## Claude does
1. Ask for four things, refusing to start without the first two: **retention data** (screenshot or per-second percentages), **length in seconds**, **script with rough timings**, **platform**.
2. Classify the curve: **cliff** (steep loss inside 3 seconds), **slide** (steady bleed), **dip-and-recover** (one bad passage), **flat-with-loop** (healthy, above 100% from replays).
3. Score against published benchmarks and name the misses: **~75% through the stop-phase, ~70% through the hold-phase, above 100% for replays**; Hoyos targets **90%+ counting loops**.
4. Name the **single second with the largest percentage-point fall** and quote the script line running at it. Never estimate a second the data does not cover.
5. Diagnose that second on the five-dimension rubric: **hook strength 0.30, standalone coherence 0.25, emotional intensity 0.20, value density 0.15, payoff quality 0.10**, minimum 60. Say which dimension collapsed.
6. Apply the per-segment responsibility model: the opener must **prove the promise the first frame made**, every later stretch justifies its own existence, and a large opening drop is normal, not a cliff.
7. Rewrite that exact second: new spoken line, new on-screen text, and a new opening visual if the drop is inside the first 3 seconds.
8. Refuse to guess: no invented percentages, no assumed platform, no thumbnail-only diagnosis.
## Then Codex does
Codex takes the bulk arithmetic: a long export parsed into per-second deltas, every 3-second segment scored on the five weighted dimensions, the weak ones ranked. Repetitive computation over many rows, not judgement, so Claude keeps the diagnosis and the rewrite.
Claude fills `{RETENTION_DATA}`, `{TRANSCRIPT_TIMED}`, `{VIDEO_LENGTH}` and `{PLATFORM}` into `CODEX.md`, then runs:
```
codex exec --sandbox danger-full-access --skip-git-repo-check -C "<working folder>" - < CODEX.md
```
## Claude checks
1. The named worst second sits inside the supplied data range, or reject and rerun.
2. Every percentage traces to a supplied row. A figure appearing nowhere in the input is invented: reject.
3. Recompute one weighted total by hand. Wrong arithmetic, reject and rerun.
4. Check what a low score is **for**. A quiet beat before a payoff scores low on emotional intensity by design: a working device, not a fault.
5. If every segment clears 60 while the video underperformed, suspect the instrument rather than invent a fault. Two failed runs and Claude scores it itself.
## Rules
- A number off a screenshot is a lead; a number in an export is a finding. Say which.
- Diagnose from the curve, never the script alone.
- "The algorithm" is not a cause. Name the second.
- Public information only.
- Never invent a fact, a number or a quote.
- Anything sent in someone's name says whose name it is.
## If Codex is not installed
Claude parses and scores it directly, and says so.
## Built from
- AgriciDaniel/claude-youtube, https://github.com/AgriciDaniel/claude-youtube - 350 stars (api.github.com, 5 Sep 2026); curve-shape classification.
- AgriciDaniel/claude-shorts, https://github.com/AgriciDaniel/claude-shorts - 204 stars (same source and date); the weighted rubric.
- social-media-skills/skills, https://github.com/social-media-skills/skills - 66 stars (same); phase benchmarks.
- Jenny Hoyos and Todd Sherman, https://blog.youtube/creator-and-artist-stories/youtube-shorts-deep-dive/ - published by YouTube; the 90%-with-loops target.
- MrBeast production handbook, https://simonwillison.net/2024/Sep/15/how-to-succeed-in-mrbeast-production/ - per-segment responsibility model.
Prompt for Codex
# Find the second people leave - Codex task
## You are given
- `{RETENTION_DATA}` - retention figures for one short video. Either a list of `second, percentage` pairs, or a pasted analytics export. It may be messy, out of order, or contain duplicate seconds.
- `{TRANSCRIPT_TIMED}` - the script or transcript, one line per entry, each with a start second.
- `{VIDEO_LENGTH}` - total video length in seconds.
- `{PLATFORM}` - one of YouTube Shorts, TikTok, Instagram Reels.
## Produce
1. **A per-second delta table.** Columns: second, retention percentage, change from the previous second in percentage points. One row per second present in the data, sorted ascending. Do not interpolate seconds that are missing; mark them `no data`.
2. **The three largest single-second falls**, ranked, each with the second, the fall in percentage points, and the transcript line running at that second (or `no line` if the transcript has no entry covering it).
3. **A segment score table.** Split the video into 3-second segments from 0 to `{VIDEO_LENGTH}`. Score each segment 0-100 on five dimensions and produce a weighted total:
- hook strength, weight 0.30
- standalone coherence, weight 0.25
- emotional intensity, weight 0.20
- value density, weight 0.15
- payoff quality, weight 0.10
Show the five raw scores, the weighted total to one decimal place, and `KEEP` if the total is 60 or above, `WEAK` if below.
4. **Threshold check.** State the retention at the final second, whether it exceeds 100% (replays), and whether the curve stays above 70% through the first third and above 60% overall. Print each as `pass`, `fail` or `no data`.
Output plain Markdown tables. No prose commentary, no recommendations, no rewrite suggestions.
## Rules
- Every percentage you print must appear in `{RETENTION_DATA}` or be a difference computed from two figures that do. Never estimate a missing value.
- If `{RETENTION_DATA}` cannot be parsed into second-and-percentage pairs, print `UNPARSEABLE` and the first five lines you could not read, then stop.
- If a second in your output does not exist in the input, that is a defect: drop the row.
- Show your arithmetic for the weighted total on the lowest-scoring segment so it can be rechecked by hand.
- Do not fetch anything from the internet. Work only from the text given.
- Do not write files outside the working folder.
## Return
Print, in this order:
1. `ROWS PARSED: <n>` and `RANGE: <first second> to <last second>`.
2. The per-second delta table.
3. The three largest falls table.
4. The segment score table.
5. The threshold check block.
6. `WORST SECOND: <n> (<fall> pp)` on its own final line.
Built from the best public work on this
Sources
- **AgriciDaniel/claude-youtube** - https://github.com/AgriciDaniel/claude-youtube - 350 stargazers_count read from api.github.com on 5 Sep 2026 (created 5 Mar 2026, pushed 10 Apr 2026). A real three-layer skill: orchestrator, 14 sub-skills and 9 reference guides, including analytics-guide.md and retention-scripting-guide.md (8,171 bytes). Source of the retention-graph shape classification and funnel diagnosis. Its own benchmark figures are stated in-repo without citation, so they are treated as house numbers and are not used here.
- **AgriciDaniel/claude-shorts** - https://github.com/AgriciDaniel/claude-shorts - 204 stargazers_count from api.github.com on 5 Sep 2026. Publishes a weighted segment rubric with explicit numbers and renders actual video rather than advice. Source of the five-dimension scoring and the minimum score of 60.
- **social-media-skills/skills** - https://github.com/social-media-skills/skills - 66 stargazers_count from api.github.com on 5 Sep 2026, 106 skill directories confirmed via the contents API, last pushed 3 Sep 2026. Source of the stop-phase, hold-phase and replay retention benchmarks.
- **Jenny Hoyos with YouTube Shorts product lead Todd Sherman** - https://blog.youtube/creator-and-artist-stories/youtube-shorts-deep-dive/ - published by YouTube itself; roughly 10 million views per Short, 3 million subscribers. Source of the 90%-or-higher retention target counting loops, and the rule that the viewer judges the whole video by the intensity of the final emotion.
- **MrBeast, "How to Succeed in MrBeast Production" internal handbook** - https://simonwillison.net/2024/Sep/15/how-to-succeed-in-mrbeast-production/ - 36-page internal document leaked Aug 2024, covered by Tubefilter, Fortune and Cybernews. Source of the per-segment responsibility model and the point that a large opening drop can be normal rather than a defect.
Best public prompt we found for this job
**AgriciDaniel/claude-shorts** - https://github.com/AgriciDaniel/claude-shorts
The most directly reusable scoring prompt in public for this job, because it publishes the weights instead of describing them:
hook strength 0.30, standalone coherence 0.25, emotional intensity 0.20, value density 0.15, payoff quality 0.10, minimum score 60 to include
Worth trusting over the advice repositories because it renders real video, so the rubric is load-bearing in a working pipeline rather than decoration in a README.
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