A tool for collecting public social posts and analyzing social or campaign data. It can turn posts or campaign exports into metrics, comparisons, and traceable findings.

Use it to study public discussion on X, Reddit, YouTube, Bilibili, or Xiaohongshu, and to compare posts, platforms, sentiment inputs, or campaign performance.

It keeps evidence collection separate from interpretation and avoids treating a small sample as the whole conversation. It also helps calculate results such as engagement, click-through rate, cost, and return metrics when the needed data exists.

social-data — Skill for Claude Code and Codex by Orkas-AI

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add Orkas-AI/Orkas-Awesome-AgentSkills --skill social-data

Clone the repo

git clone --depth 1 https://github.com/Orkas-AI/Orkas-Awesome-AgentSkills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/social-data/github.svg)](https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/social-data)

Your own site

<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/social-data"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/social-data/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/orkas-ai/orkas-awesome-agentskills/social-data"><img src="https://agentmods.dev/badge/skills/orkas-ai/orkas-awesome-agentskills/social-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,326 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 88% copy Near-identical to another mod in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00052	$0.01326
Opus 5	$0.00026	$0.00663
Sonnet 5	$0.00010	$0.00265
Haiku 4.5	$0.00005	$0.00133

Measured 12d ago against content hash 8a234c1c985e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

social-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/analyze_performance.py, scripts/calculate_metrics.py, scripts/fetch.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

88% identical to social-data — 28 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

data/skills/social-data/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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.
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.
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.

Read the full file on GitHub · 137 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

12d ago First seen · 137 lines · 52 tokens per session scan A 8a234c1c985e

Subscribe to this mod's changes

social-data is a skill published in the GitHub repository Orkas-AI/Orkas-Awesome-AgentSkills (13 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 1,326 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to social-data, differing in 28 lines, and is treated as a copy.

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