hook-retention-teardown

name	hook-retention-teardown
description	Tear down what a creator's best hooks do that their flops don't. Use when a SocialGPT user asks "why do my best videos pop and others flop", "what makes a good hook for me", "compare my top vs bottom posts", "what should my openings do", or wants a teardown of their own hooks and pacing. Pulls the user's top and bottom performers via the SocialGPT MCP, reads each one's transcript and hooks, then computes deterministic text/pacing metrics — words-per-second, time-to-hook, first-person vs instructional language, specificity, curiosity words, CTA usage, hook style — and writes a visual report showing the dimensions where winners and losers differ most. Requires the SocialGPT MCP server to be connected.
license	MIT

Hook & Retention Teardown

Find the language and pacing patterns that separate a creator's winners from their flops — so they learn what their own best hooks and openings do that their weak ones don't. This is the deterministic, transcript-level complement to qualitative hook advice: instead of vibes, you measure words-per-second, opener style, first-person language, and CTA placement, then show exactly where the top cohort diverges from the bottom.

This skill pairs with the SocialGPT MCP server (https://mcp.gpt.social/mcp). The MCP provides the data; this skill provides the deterministic analysis and a shareable report. If the SocialGPT tools below aren't available, the user needs to connect the MCP first: https://gpt.social/integrations/mcp

When to use

Trigger on requests like: "why do my best videos pop and others flop?", "what makes a good hook for me?", "compare my top and bottom posts", "what should my openings do?", "do a teardown of my hooks", "what do my winners do differently?"

Workflow

1. Confirm access. Make sure the SocialGPT MCP tools are connected. If list_videos isn't available, point the user to the connect page above and stop.

2. Find the winners and flops. Call:

   list_videos(sort="top", limit=20)
   

   Rank by metrics.views. Take the top ~5 and the bottom ~5. Exclude Instagram videos with views == 0 (Instagram hides reel plays, so a 0 there isn't a real flop).

3. Fetch each video's full analysis. For every selected video, call:

   get_video_analysis(platform, post_id)
   

   This returns the transcript, hooks, and suggested hooks the teardown needs.

   • Right after ingest this may return {"status": "pending"} — that's not an error. Wait ~20s and retry. Only include a video once its analysis is ready (it has a transcript).
   • Skip any video that still has no transcript after retrying; it just won't count toward the teardown.

4. Assemble the input file. Collect the get_video_analysis results into one JSON file named teardown.json in the working directory, shaped:

   {
     "top":    [ <get_video_analysis result>, <...> ],
     "bottom": [ <get_video_analysis result>, <...> ]
   }
   

   Put each analysis object verbatim — the script reads post, transcript, transcript_segments, hooks, and suggested_hooks itself.

5. Run the analysis (it has no third-party dependencies):

   python scripts/analyze.py teardown.json
   

   The script reads teardown.json, computes per-video metrics, compares the two cohorts, prints a Markdown summary to stdout, and writes hook-retention-teardown.html.

6. Relay the result. Present the Markdown summary the script printed, then offer the user the generated hook-retention-teardown.html to download / open (on Claude.ai it appears as a downloadable file).

What the script does

For every video that has a transcript, it computes deterministic text/pacing metrics, then aggregates each as a cohort median (numeric) or majority share (categorical) for top vs bottom and surfaces the dimensions where they differ most:

• Pacing — words per second (transcript word count ÷ duration) and an approximate time-to-first-hook (start of the first segment carrying a question, number, or strong opener).
• Language — first-person markers (I, my, we) vs instructional ones (you, your) as a ratio; specificity (density of numbers, %, $, proper nouns); curiosity/emotion words ("actually", "nobody", "secret", "why"…); and CTA presence + rough position (early / middle / late / absent).
• Hook style — the opening (first ~12 transcript words, or the top suggested/extracted hook) classified as question, bold-claim, story, listicle, or visual/other.

The report shows: callouts (videos analyzed, the biggest differentiator, winners' median words/sec), a side-by-side table of winners vs flops per dimension, a breakdown of hook styles among the winners, and three plain-English patterns to copy. See references/methodology.md for the exact definitions.

Notes

• Needs a real comparison. It requires at least 2 videos with transcripts in each cohort (or 3 total) and will tell you to fetch more / wait for pending analyses rather than invent findings.
• Describes, not explains. These are counts on the creator's own transcripts — they show what differs between winners and flops, not why it works. Treat each pattern as a lead to A/B test.
• Re-runnable. Suggest re-running as the account grows and the back catalog of winners and flops changes.
• Part of a larger loop. This is the hook-study stage of the going-viral strategy loop — pair it with the audit and gap skills for the full research-to-review cycle.