social-media-intelligence

social-media-intelligence is a skill for Claude Code, Codex from HKUDS/Vibe-Trading. It costs 28 tokens per session (10,163 once invoked), scanned A, original, MIT.

A financial intelligence workflow that gathers signals from Twitter/X, Telegram, Discord, and Reddit. It examines what different groups are saying about markets, companies, and crypto assets.

In plain words
What is it for?
Use it to collect social posts, assess sentiment, study reactions to events, and develop sentiment-based trading signals.
Why use it?
It helps turn large amounts of fast-moving social discussion into information that can be compared or used in trading research. It also highlights that online signals can be subjective or manipulated.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to collect social posts, assess sentiment, study reactions to events, and develop sentiment-based trading signals.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/hkuds/vibe-trading/social-media-intelligence
About the project

Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.

HKUDS/Vibe-Trading · 33,177 stars · on GitHub · vibetrading.wiki

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 HKUDS/Vibe-Trading --skill social-media-intelligence
Clone the repo
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading

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.

agentmods badge for social-media-intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/hkuds/vibe-trading/social-media-intelligence/github.svg)](https://agentmods.dev/skills/hkuds/vibe-trading/social-media-intelligence)
Your own site
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/social-media-intelligence"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/social-media-intelligence/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.

agentmods 80×15 button for social-media-intelligence

Your own site · 80×15
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/social-media-intelligence"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/social-media-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 10,163 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. ✓ AI security review Fable 5.1 · 6 Sept 2026 📄 Read the review Third-party audits
  • Snyk fail 7 Sept 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 1253
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.10163
Opus 5 $0.00014 $0.05081
Sonnet 5 $0.00006 $0.02033
Haiku 4.5 $0.00003 $0.01016

Measured 2d ago against content hash 0a6f4d9f48a0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

social-media-intelligence 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 2d ago.

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

Copies of this mod

1 near-identical copy found in the catalogue:

agent/src/skills/social-media-intelligence/SKILL.md · 1,306 lines

How it starts

The opening of the file, as written. The whole thing — 1,306 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Social Media Intelligence

This skill integrates financial-intelligence collection methods and quantitative applications across Twitter/X, Telegram, Discord, and Reddit. Inspired by himself65/finance-skills modules such as discord-reader, telegram-reader, and twitter-reader.


1. Overview of the Four Major Financial Social Platforms

1.1 Twitter/X — The FinTwit Ecosystem

Core roles

Role Type Representative Account Traits Signal Value
Sell-side analyst Institutional backing, dense posting around earnings Medium, somewhat lagging
Fund manager Holdings views, industry judgment High, but mixed with subjective opinion
Macro commentator Fed interpretation, macro-data reaction High, a good sentiment barometer
Crypto KOL On-chain interpretation, project endorsement Highly volatile, high manipulation risk
Retail noise Meme spread, herd sentiment Contrarian signal value at extremes

Core FinTwit circles

  • $TICKER cashtag system directly maps discussion to the asset
  • Earnings-season sentiment patterns before and after reports
  • Real-time reaction speed to policy / macro events, often 15-60 minutes ahead of traditional media

1.2 Telegram — The Core Venue for Crypto Intelligence

Channel types

Channel Type Content Traits How to Use
Signal channels Specific buy/sell levels, stop-loss / take-profit Use as a sentiment thermometer, not for blind copy-trading
Research push channels Institutional PDF reports, on-chain data Aggregate information and extract key numbers
Macro flash channels Real-time interpretation of FOMC, CPI, etc. Event-driven signals
Official project channels Tokenomics updates, partnership announcements Potential alpha, but requires filtering
Whale alert channels Large on-chain transfer alerts Capital-flow signal

1.3 Discord — Quant Communities and Project Ecosystems

Read the full file on GitHub · 1,306 lines

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.

  1. 2d ago Changed · +10 lines 0a6f4d9f48a0
  2. 8d ago First seen · 1,296 lines · 28 tokens per session scan A ef47bebc4872

Subscribe to this mod's changes

social-media-intelligence is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 10,163 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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