# Social Data

> Fetch public social posts and analyze social/campaign metrics. Use this skill whenever the user asks to collect public social discussion, analyze social sentiment inputs, calculate engagement/CTR/ROI, compare platforms/content, or turn social samples into traceable findings.

- Skill: `vikynofebriputra-creator/social-data` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds@latest add vikynofebriputra-creator/social-data`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vikynofebriputra-creator/social-data/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Marketing & Growth
- Author: vikynofebriputra-creator (https://skillmd.com/u/vikynofebriputra-creator)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vikynofebriputra-creator/social-data

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# Social Data

Use this skill for two related jobs:

- `fetch`: collect public social posts from a specified platform and return structured, deduplicated items.
- `analyze`: calculate and interpret metrics from user-provided social posts, campaign exports, or fetched social samples.

Keep the two steps distinct. Fetching returns evidence samples; analysis turns user-provided or fetched data into metrics and recommendations.

## When To Use

- The user asks what people are saying about a brand, product, competitor, topic, or pain point on Xiaohongshu, X/Twitter, Reddit, YouTube, or Bilibili.
- The user provides social post/campaign data and asks for engagement rate, CTR, ROI, ROAS, CPC, CPE, CPM, CPA, top/bottom posts, platform comparison, or content recommendations.
- The user wants social evidence for reputation, buzz, user complaints, market feedback, campaign performance, or next content experiments.

Do not use for:

- Private messages, login-gated content, paid API bypassing, scraping restricted pages, or non-public data.
- Claiming that zero fetched results means no discussion exists.
- Treating benchmark comparisons as facts without user-provided or current benchmark context.
- Replacing platform analytics exports when the user needs complete official reporting.

## How To Call

1. Choose the mode:
   - `fetch`: user needs public posts or discussion samples.
   - `analyze`: user already has post/campaign data or wants metrics/recommendations from fetched samples.
   - `fetch_then_analyze`: user asks for a topic analysis and no data is provided.

2. For fetch mode:
   - Follow `references/fetching.md`.
   - One platform per script call: `xhs`, `twitter`, `reddit`, `youtube`, or `bilibili`.
   - Expand the user's topic into 3-8 short divergent keyword groups unless the user explicitly restricts keywords.
   - Report `diag.status`, failures, empty results, and platform dependency limits.

3. For analysis mode:
   - Follow `references/metrics.md`.
   - Validate fields before calculating.
   - Label evidence as direct calculation, limited inference, or assumption.
   - Separate organic and paid performance when possible.

4. Use scripts only when useful:
   - Fetch public samples:
     `$ORKAS_NODE $ORKAS_PC_DIR/bin/run-skill.cjs social-data fetch -- <platform> <keywords> [options]`
   - Calculate metrics from JSON:
     `$ORKAS_NODE $ORKAS_PC_DIR/bin/run-skill.cjs social-data calculate_metrics -- data.json`
   - Analyze performance from JSON:
     `$ORKAS_NODE $ORKAS_PC_DIR/bin/run-skill.cjs social-data analyze_performance -- data.json`

5. Return traceable conclusions.
   - Tie claims back to item URLs, post IDs, comments, or user-provided rows.
   - Include data quality warnings and next tests.
   - Do not synthesize sample posts when fetch fails.

## Default Output

For fetched discussion:

```markdown
# Social Data Fetch Result

## Scope
- Platform:
- Topic:
- Keywords:
- Status:

## Sample Summary
- Items:
- Top repeated themes:
- Notable examples:

## Diagnostics
- Raw hits:
- Deduplicated:
- Failed:
- Limits:

## Next Analysis
- ...
```

For performance analysis:

```markdown
# Social Performance Analysis

## Bottom Line

## Data Quality
- Sample size:
- Missing fields:
- Abnormal values:
- Confidence:

## Key Metrics
| Metric | Value | Meaning | Evidence |
|---|---:|---|---|

## Top / Bottom Content
| Rank | Post | Platform | Format | Metric | Possible reason |
|---|---|---|---|---:|---|

## ROI And Cost
- Spend:
- Revenue / value:
- ROI:
- ROAS:
- CPC / CPE / CPA:

## Recommendations
1. ...
```

## External Dependencies

- Python 3 for bundled scripts.
- Fetch mode may need `requests`, `browser_cookie3`, `curl_cffi`, `xreach`, `yt-dlp`, or a local Xiaohongshu proxy depending on platform.
- Analysis mode needs user-provided JSON, table data, or exported platform/campaign metrics.

## Limits And Known Issues

- Fetch coverage varies by platform, login state, rate limits, anti-bot behavior, and local dependencies.
- Public fetches are samples, not complete platform analytics.
- Anonymous Reddit/Bilibili recall can be weak; browser login cookies may improve results.
- Xiaohongshu requires an external local proxy service at `http://localhost:18060`.
- ROI estimates based on engagement value are assumptions unless the user provides real revenue, conversion value, or lead value.
- Benchmark values can become stale and should be treated as rough context, not a pass/fail truth.