renatocaliari/skills/local/cali-social-listening

Social Listening

Read-only pipeline to capture, summarize, and rank posts from X and LinkedIn. Never posts anything. Interaction is a separate, opt-in skill (social-engage) — never trigger its actions from here without the user explicitly asking.

Why X and LinkedIn use different strategies

The two sites have very different bot-detection profiles in 2026:

• X: GraphQL response interception is resilient and the anti-bot system is tolerable for moderate automated scrolling.
• LinkedIn: a session-fingerprinting update (Q1 2026) flags automation patterns within ~48h, with direct suspension instead of a warning. High-volume automated scrolling is a real account risk. That's why LinkedIn here runs in manual/light mode, reusing the user's real browser session.

Never apply the X capture script to LinkedIn or vice versa.

Setup (once)

1. pip install playwright langdetect --break-system-packages && playwright install chromium
2. Run scripts/x_login_once.py to open a visible Chromium window, log into X manually, and save the session to ~/.social-listening/x-state.json. After that, it never asks for login again.
3. There's no separate session setup for LinkedIn — the script connects to the user's real Chrome via CDP (see references/linkedin-safety-limits.md).
4. Edit config.yaml (copy from config.example.yaml) with: handles/topics that matter, languages (pt-BR, en), scroll rounds, output directory.

Flow

1. Capture — scripts/x_capture.py (X) and/or scripts/linkedin_capture.py (LinkedIn) → raw JSON dumps in data/raw/.
2. Rank — scripts/score_rank.py filters by language, computes engagement velocity, reply competition, and topic relevance → data/ranked.json.
3. Patterns + final rerank (you do this, not a script) — read data/ranked.json (top ~30) and:
   • Group by recurring theme/tone (what people are talking about, how they're talking about it).
   • For each post in the top 30, honestly assess: "do I actually have real experience to contribute here, or does this just match keywords?" Cut the ones that are only keyword coincidence without a genuine angle.
   • Produce a short digest: 3-5 observed patterns + a final list of posts with a suggested angle for contributing (not the reply text itself — that's social-engage).

Capture commands

# X — 10 scroll rounds (default), home timeline + search by config terms
python scripts/x_capture.py --source home --rounds 10
python scripts/x_capture.py --source search --query "agentic coding" --rounds 10

# LinkedIn — manual, light, at most 1x/day, 3-5 rounds
python scripts/linkedin_capture.py --rounds 4

# Rank what was captured
python scripts/score_rank.py --input data/raw/ --output data/ranked.json \
  --languages pt,en

Scoring (what score_rank.py computes)

• Engagement velocity: (likes + reposts + replies) / minutes_since_post
• Reply competition: inverse of reply count — fewer existing replies = more room/visibility for a new comment
• Topic relevance: keyword/topic match against config.yaml (swap for embeddings later if you want something more precise)

The final score is a weighted combination configurable in config.yaml (weights: {velocity: 0.4, reply_gap: 0.3, relevance: 0.3}).

Reference files

• references/x-graphql-endpoints.md — which endpoints to intercept and why
• references/linkedin-safety-limits.md — safe limits and why the mode differs