Lev Social

lev-social

[WHAT] Social media research skill integrating Bird CLI (Twitter/X) and PostCrawl (Reddit/TikTok) for sentiment analysis and trend discovery.

[HOW] Executes search queries across platforms, aggregates results, extracts sentiment patterns, and generates research reports.

[WHEN] Use for market research, competitive analysis, sentiment tracking, community feedback collection, and trend identification.

Position in the unified model: prefer lev-research / lev timetravel search -s social as the main entry point. Use this skill directly only when you are working on the social adapter itself or need raw platform-specific collection behavior.

Architecture Context

Social research outputs land in project workshop dirs, not hardcoded paths. Resolve output location via fractal config: workshop.reports.social in .lev/config.yaml or default to .lev/workshop/reports/social/. For lev architecture context: ls ~/lev/core/ | sort and check architecture-primer.md.

Prerequisites

• Bird CLI: /opt/homebrew/bin/bird (Twitter/X GraphQL API)
• PostCrawl: pip install postcrawl (Reddit/TikTok API)
• Exa API: EXA_API_KEY env var (background research)
• Tavily API: TAVILY_API_KEY env var (supplemental search)
• ScrapCreators: SCRAPCREATORS_API_KEY env var (27+ platform unified API — alternative to Bird+PostCrawl)

Commands

If the user asked for "research" rather than "run Bird/PostCrawl directly", route through:

lev timetravel search "query" -s social

Use the direct commands below only when the social-specific raw collection matters.

Twitter Search (Bird CLI)

# Basic search
bird search "query" -n 20 --json

# Search with pagination
bird search "query" --all --max-pages 5 --json

# Search operators
bird search "from:username query"
bird search "query min_faves:10"
bird search "@mention topic"

Reddit/TikTok Search (PostCrawl)

from postcrawl import PostCrawl

pc = PostCrawl(api_key=os.environ["POSTCRAWL_API_KEY"])

# Search
results = await pc.search(
    social_platforms=["reddit"],
    query="topic keywords",
    results=50
)

# Extract with comments
posts = await pc.extract(
    urls=["https://reddit.com/r/..."],
    include_comments=True,
    comment_filter_config={"min_score": 10}
)

Background Research (Exa)

curl -s "https://api.exa.ai/search" \
  -H "x-api-key: ${EXA_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "topic for research",
    "type": "auto",
    "numResults": 20,
    "category": "tweet"
  }'

Research Workflow

1. Query Expansion

Design queries per category with:

• Core terms
• Sentiment indicators (positive/negative)
• Platform-specific operators
• Alternative phrasings

2. Multi-Platform Collection

# Twitter (Bird)
bird search "query" -n 50 --json > raw/twitter-cat1.json

# Reddit (PostCrawl via Python)
python -c "..." > raw/reddit-cat1.json

# Background (Exa)
curl ... > raw/exa-context.json

3. Sentiment Extraction

Parse JSON, extract:

• Engagement metrics (likes, retweets, upvotes)
• Author metadata
• Timestamp distribution
• Sentiment keywords

4. Report Generation

Aggregate into:

• Sentiment by category
• Top insights (high-engagement content)
• Pain points (negative sentiment)
• Opportunities (unmet needs)

Team Mode Pattern

For large research projects:

| Role | Platform | Responsibility |
|------|----------|----------------|
| twitter-researcher-N | Bird CLI | Execute query batches |
| reddit-researcher | PostCrawl | Subreddit extraction |
| context-gatherer | Exa | Background articles |
| synthesizer | All | Aggregate + report |

Output Artifacts

Standard output structure:

~/lev/ideas/{research-topic}/
├── query-expansion-plan.md
├── raw/
│   ├── twitter-*.json
│   ├── reddit-*.json
│   └── exa-*.json
├── sentiment-by-category.md
├── top-insights.md
└── final-report.md

BD Integration

Create epic for research tracking:

bd create --type epic --title "Research: {topic}" --priority P0

Update with findings:

bd update {epic-id} --notes "Phase 1 complete: {summary}"

Example: OpenClaw Research

# Phase 1: Twitter sentiment
bird search "openclaw hosting" -n 50 --json > raw/twitter-hosting.json
bird search "openclaw pain point" -n 50 --json > raw/twitter-pain.json

# Phase 2: Reddit supplement
postcrawl search --platforms reddit --query "openclaw" --results 50

# Phase 3: Synthesize
# (Agent aggregates JSON, extracts patterns, generates report)

Related Skills

• lev-research - General research orchestration
• lev-intake - URL/content intake
• lev-find - Local code/docs/task retrieval

Technique Map

• Role definition - Clarifies operating scope and prevents ambiguous execution.
• Context enrichment - Captures required inputs before actions.
• Output structuring - Standardizes deliverables for consistent reuse.
• Step-by-step workflow - Reduces errors by making execution order explicit.
• Edge-case handling - Documents safe fallbacks when assumptions fail.

Technique Notes

These techniques improve reliability by making intent, inputs, outputs, and fallback paths explicit. Keep this section concise and additive so existing domain guidance remains primary.

Prompt Architect Overlay

Role Definition

You are the prompt-architect-enhanced specialist for lev-social, responsible for deterministic execution of this skill's guidance while preserving existing workflow and constraints.

Input Contract

• Required: clear user intent and relevant context for this skill.
• Preferred: repository/project constraints, existing artifacts, and success criteria.
• If context is missing, ask focused questions before proceeding.

Output Contract

• Provide structured, actionable outputs aligned to this skill's existing format.
• Include assumptions and next steps when appropriate.
• Preserve compatibility with existing sections and related skills.

Edge Cases & Fallbacks

• If prerequisites are missing, provide a minimal safe path and request missing inputs.
• If scope is ambiguous, narrow to the highest-confidence sub-task.
• If a requested action conflicts with existing constraints, explain and offer compliant alternatives.