leamsigc/shortsgenerator/facebook-research-analyze
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Analyze state — reads top 5 posts from scrape phase and performs deep deconstruction of each: audience retention signals, engagement patterns, hook analysis, content structure, psychological triggers. Also analyzes competitor top content for benchmarking. Generates detailed "why it worked" analysis with actionable replication guidance and competitive comparison. Also runs the video-ideas pipeline (shared video-idea-research playbook): the 10 Facebook videos/reels with the most views in a window…
Install
npx vskill@latest install leamsigc/shortsgenerator/facebook-research-analyzeSKILL.md
Facebook Research — Analyze State (v2)
Deep deconstruction of the top 5 performing videos and competitor top content.
Preconditions
- Scrape completed:
.fb-research/projects/<project-name>/page-analytics.json - Competitors scraped:
.fb-research/projects/<project-name>/competitors/ - Config exists:
.fb-research/projects/<project-name>/config.json
Steps
1. Load Data
Extract: top 5 posts, page niche, your topics, competitor names.
2. For Each of the Top 5 Posts, Perform Deep Analysis
Re-navigate to each post URL:
Wait for full load. Extract the complete caption/description and engagement:
3. Deconstruction Framework — Analyze Each Post Against
For each of the top 5 posts, analyze these dimensions:
A. The Hook (First 3 seconds)
- Hook type: Question, bold statement, pattern interrupt, curiosity gap, statistic, controversy, story, prediction
- Hook text: The exact first line/sentence
- Hook strength: Strong / Medium / Weak — based on whether it creates an information gap
- Visual hook: What's happening on screen in the first frame (from screenshot)
- Audio hook: Music stinger, sound effect, voice tone shift, silence
B. Content Structure
- Format: Talking head, text overlay, montage, screen recording, story, tutorial, listicle, reaction
- Pacing: Fast cuts, slow build, steady, rhythmic
- Length: Short (<30s), Medium (30-60s), Long (60-120s), Extended (>120s)
- Pattern: Problem → Solution, Story → Lesson, Question → Answer, Controversy → Discussion, Tease → Reveal
- Scene breakdown: Number of distinct scenes/segments
C. Psychological Triggers Detected
| Trigger | Present? | Evidence | Intensity (1-10) | |
|---|---|---|---|---|
| Curiosity gap | Yes/No | Open loop in hook | /10 | |
| Social proof | Yes/No | Shows popularity/numbers | /10 | |
| Identity | Yes/No | "This is for people who..." | /10 | |
| Loss aversion | Yes/No | "Don't miss..." | /10 | |
| Authority | Yes/No | Expert positioning | /10 | |
| Pattern interrupt | Yes/No | Unexpected angle | /10 | |
| Controversy | Yes/No | Polarizing take | /10 | |
| Emotional | Yes/No | Anger/joy/surprise/sadness | /10 | |
| Relatability | Yes/No | "This is so me" factor | /10 | |
| Urgency/scarcity | Yes/No | Limited time/opportunity | /10 | |
| Reciprocity | Yes/No | Gives value before asking | /10 | |
| Anchoring | Yes/No | Specific numbers feel researched | /10 |
D. Engagement Analysis
- Like-to-View ratio: Calculate as proxy for engagement quality
- Comment-to-View ratio: High ratio suggests strong opinion/discussion trigger
- Share-to-View ratio: High ratio suggests strong identity signaling or utility
- Comment sentiment: Positive, mixed, controversial, supportive, questioning — infer from visible comments and emoji reactions
- What drives comments: Question asked?, Controversial opinion?, Fill-in-the-blank?, Tag a friend?, Correction invitation?
- Top comment themes: What are people talking about in the top 3-5 visible comments?
- Emoji reactions breakdown: Which reactions dominate? (Like, Love, Care, HaHa, Wow, Sad, Angry)
E. Caption Analysis
- Length: Short (<50 chars), Medium (50-200), Long (200-500), Very Long (>500)
- Structure: Single line, multi-paragraph, bullet points, numbered list
- CTA: Explicit (comment/share/follow) or Implicit, Type (question, poll, tag, link, shop)
- Emoji usage: Count, placement (start/middle/end), which emojis
- Hashtags: Count, relevance (branded vs viral vs niche), placement (inline vs end)
- First line: Does it restate the hook or add new info?
- Line breaks: Short punchy lines vs dense paragraphs
F. Production Quality
- Lighting: Professional / Natural / Low / Dramatic
- Audio: Clear Voiceover / Background music / Natural sound / Mixed
- Editing: Minimal / Moderate / Heavy / Cinematic
- Text overlays: Yes/No, style (bold, animated, minimal, kinetic typography)
- Subtitles: Auto-generated / Styled captions / None — placement and style
- Color grading: Warm / Cool / Natural / High contrast / Vintage
G. Timing & Posting Pattern
- Posting time: Inferred from date text
- Day of week: Inferred from context
- Post type: Reel / Video / Live / Crosspost
- First-hour engagement: Estimated from available signals
4. Generate Analysis File
Create a markdown file with the full deconstruction for each post.
5. Analyze Competitor Top Content
For each competitor, read their scraped data:
Apply the same deconstruction framework (steps 2-3) to the competitor's top 3 posts. Add a comparative lens:
6. Identify Cross-Post and Cross-Competitor Patterns
7. Save Analysis
8. Video Ideas Pipeline (skill video-idea-research)
The deep deconstruction above feeds the ideas pipeline. When the user wants video ideas for a topic, run the shared playbook with platform:facebook:
- Top 10 by views in the window — rank the page's + competitors' top
reels/videos by view count (watch pages show real view counts; fallback to engagement proxies and say so). Never invent numbers.
- Outlines — hook (first caption line / transcript opening), beats, CTA,
format, duration. Use the downloaded reels + transcribe.sh (Whisper) when the skeleton matters.
- 5 ideas, each grounded in ≥2 top videos with their real view counts.
- 3 scripts per idea — viral (
viral_shorts), storytelling
(storytelling), inspiration (motivational); TTS-ready, [HOOK]/[CTA] labels only. Optional backend seeding: ``bash curl -s -X POST http://localhost:8080/api/script \ -H "Content-Type: application/json" \ -d '{"videoSubject":"<idea title>","extraPrompt":"<angle + outline context>","aiModel":"g4f","scriptTemplate":"viral_shorts","scriptLength":"standard"}' ``
- YouTube metadata per idea (title via the
youtube-titlerubric,
description, tags, keywords).
- Save
video-ideas.jsonin the session folder and render the report:
``bash node "$CLAUDE_SKILL_ROOT/scripts/build-ideas-report.js" <path-to-video-ideas.json> # fallback: node ./skills/video-idea-research/scripts/build-ideas-report.js <path> ` → ideas-report.md + REPORT.html`: top 10 videos + 5 ideas with scripts and metadata.
9. Report Completion
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
- SKILL.md in the repositoryCheckingskills/facebook-research-analyze/SKILL.md
Popularity
- Repository stars
- 348
- Repository forks
- 75
- 7-day trend score
- 29
Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.
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