# Social Me > Research a topic across YouTube, Instagram, X and TikTok with Apify, read the top YouTube videos' transcripts and comments, and return a brief of what is working, where the gaps are, and what to make next. Every number is pulled or computed from real data, never invented. Triggers on - social me, research this topic across platforms, what is working on social, cross-platform research, find the content gaps, topic brief, scout a niche, what should I make about. - Skill: `tylerprogramming/social-me` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add tylerprogramming/social-me` - Raw SKILL.md: https://api.skillmd.com/api/skills/tylerprogramming/social-me/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: tylerprogramming (https://skillmd.com/u/tylerprogramming) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tylerprogramming/social-me --- # social-me Give it a topic. It pulls the real top content on four platforms, reads the winners' transcripts and their comment sections, computes every number with a script instead of by eye, and writes a brief that ends in a decision: which video, short, carousel or post gets made next. ## Inputs | input | default | notes | |---|---|---| | topic | required | e.g. "claude code skills", "AI agents for solopreneurs" | | platforms | all four | `youtube` `instagram` `x` `tiktok` | | window | last 3 months | **YouTube is fixed at 30 days** - the actor has no 3-month filter that survives view sorting. Say so in the brief. | | per_platform | 25 | items pulled per platform | | depth | 3 | YouTube videos read in full (transcript + comments) | Everything runs every time. There is no quick mode: the expensive stages are priced per item, not per run, so `depth` and `per_platform` are the cost dials. ## Where it writes `~/content/research/social/YYYY-MM-DD-/` ``` brief.md the deliverable raw-.json exactly what Apify returned (the audit trail) raw-instagram-profiles.json follower counts for the IG accounts in the pull raw-yt-comments-top.json reaction pass raw-yt-comments-new.json demand pass raw-tiktok-comments.json demand pass (optional) norm-.json normalized records from rank.py labels.json the subtopic labels you wrote for this run transcripts/ .txt ``` Instagram handles discovered along the way get appended to `~/content/BRAIN/instagram/watchlist.md` with their follower count, their engagement rate at discovery, and a `[found YYYY-MM-DD via ]` stamp, so the next run scrapes them directly instead of rediscovering them. ## Process ### 0. Load context first Read `BRAIN/tyler-voice.md`, `BRAIN/youtube/brain.md`, `BRAIN/instagram/brain.md` and `BRAIN/instagram/watchlist.md`. Then check `~/content/research/` and `~/content/youtube/ideas/video-ideas.md` for prior work on this topic - a gap Tyler already covered is not a gap. ### 1. Pull, and get the raw data onto disk `reference/actors.md` has the verified call for every actor and every gotcha that has bitten this skill. Follow it. Seven runs: YouTube search, TikTok search, X search, Instagram hashtag, Instagram profiles, YouTube subtitles, YouTube comments. Then curl each dataset straight to a file. No token needed, full fidelity, nothing retyped: ```bash curl -s "https://api.apify.com/v2/datasets//items?format=json&clean=true" -o raw-.json ``` Do that before you look at anything. If a stage returns nothing or errors, write that line into the brief and keep going. A brief that says "Instagram returned no usable items" is worth more than a brief with a plausible Instagram section in it. ### 2. Rank with the script, not by eye ```bash python3 scripts/rank.py --platform youtube --in raw-youtube.json --out norm-youtube.json --top 10 python3 scripts/rank.py --platform instagram --in raw-instagram.json --profiles raw-instagram-profiles.json \ --out norm-instagram.json --top 10 ``` Paste its tables into the brief as printed. Missing values print `n/a` and stay `n/a`. The signal to chase is **reach per follower**. A 768-follower account at 44K views is a stronger read than a 149K-follower account at 367K. On Instagram there is no reach at all, so the equivalent is **engagement per follower** - call it that, every time, and never call it reach. ### 3. Read the winners **Transcripts.** yt-dlp is blocked (`YTDLP_FAILED` on every video, 2026-08-29), so go straight to Apify with all `depth` videos as `startUrls` in ONE run, and save `subtitles[].plaintext` to `transcripts/.txt`. `scripts/yt_transcript.py` stays as the cheap fallback for the day YouTube relents. From each transcript pull the **first 60 seconds verbatim** (that is the hook, quote it), what the video actually delivers in one line, and the structure - segments, demo, where the CTA lands. **Comments, two passes, never mixed:** ```bash python3 scripts/comments.py --top raw-yt-comments-top.json --new raw-yt-comments-new.json \ --tiktok raw-tiktok-comments.json ``` - **Reaction** (`TOP_COMMENTS`, 25/video): which promise landed. Title and thumbnail input. - **Demand** (`NEWEST_FIRST`, 100/video): what people are stuck on. Content input. The top of a comment section is praise, jokes and the creator's own pinned link. Measured on a 219-comment video: 3 of 25 top comments contained a question, against 31% of the newest ones. **Never quote a reaction comment as evidence of demand.** For TikTok, Instagram and X posts, read the top items' opening line, the format, the length and the CTA. Comment-gates especially: they are the Instagram norm and the TikTok exception. ### 4. Find the patterns and the gaps **Subtopics, mechanically, then labelled by you:** ```bash python3 scripts/subtopics.py candidates --dir . # n-grams, noisy on purpose # read them, merge synonyms, write labels.json python3 scripts/subtopics.py score --dir . --labels labels.json ``` The index is median reach of matching items over that platform's median. High index + low share is a gap candidate. High share + index at or below 1.0 is saturated. Both lists are candidates: confirm each against the actual items before it reaches the brief. A **gap** needs both halves from the data: demand (a repeated question from the comment pass, or a subtopic index above 1.3 on thin share) and thin coverage (a count you can point at). Anything with only one half goes under "hunches", not gaps. ### 5. Write the brief Use `templates/brief.md`. The new evidence **replaces inference, it does not stack on top of it** - quote a real question instead of arguing from an adjacent video's performance, and cite the subtopic table instead of hand-counting title shapes. Every number carries its source. Every claim points at a real item. ### 6. Hand off Close with the single thing to make first and the skill that makes it: `/yt-package` for long-form, `/yt-shorts` for shorts, `/instagram-writer` for a carousel, `/linkedin-writer` for the build post. ## Rules - **Never invent a number, a title, a handle, or a quote.** If it is not in the raw JSON, a transcript, or a comment file, it does not go in the brief. - Numbers come from the scripts. Do not do the arithmetic in your head. - Quote hooks and comments verbatim, in quotation marks, with the author and the URL. - Engagement is not reach. Label the Instagram column honestly or do not print it. - Say what failed. Partial and honest beats complete and made up. - Follow `BRAIN/tyler-voice.md`. No em dashes. No money amounts in proposed titles. The subject is Claude Code automation, never platform or growth strategy - if an idea could headline a YouTube growth channel, rewrite it as an automation insight that transfers to anyone's repetitive work. - Tyler is a software engineer. Ideas should show the build, not pitch beginners. - Report the ideas, do not schedule or publish anything.