social-sentiment-analyzer

social-sentiment-analyzer is a skill for Claude Code from XPOZpublic/xpoz-agent-skills. It costs 72 tokens per session (3,151 once invoked), scanned A, original, MIT.

A tool for analysing public opinion about a brand, product or topic on Twitter/X, Reddit and Instagram. It collects posts, labels their sentiment as positive, neutral or negative, finds recurring themes, and produces a report.

In plain words
What is it for?
Use it for brand, product, event or topic sentiment analysis and for questions about what people are saying on social media.
Why use it?
It replaces manual review of posts across several social networks with one structured analysis. It also shows whether reactions are generally favourable, neutral or unfavourable and what people repeatedly discuss.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Claude Code; built for openclaw; mentions Cursor.

Part of the xpoz plugin — 14 skills shipped together

Good fit Use it for brand, product, event or topic sentiment analysis and for questions about what people are saying on social media.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer
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 XPOZpublic/xpoz-agent-skills --skill social-sentiment-analyzer
Clone the repo
git clone --depth 1 https://github.com/XPOZpublic/xpoz-agent-skills

Made for: Claude Code.

Or install xpoz, the plugin that ships this one along with the rest of its 14 skills.

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-sentiment-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer/github.svg)](https://agentmods.dev/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer)
Your own site
<a href="https://agentmods.dev/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer/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-sentiment-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 72 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,151 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 2 findings. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00072 $0.03151
Opus 5 $0.00036 $0.01576
Sonnet 5 $0.00014 $0.00630
Haiku 4.5 $0.00007 $0.00315

Measured 2d ago against content hash d59053be8b30, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

social-sentiment-analyzer scanned grade A with 2 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

import secrets, hashlib, base64, urllib.parse, json, urllib.request, os

Runs shell commandslowCapability

Expected in a hook, worth knowing in a rule or an instructions file.

subprocess.run(['mcporter', 'config', 'remove', 'xpoz'], capture_output=True)
skills/social-sentiment-analyzer/SKILL.md · 392 lines

How it starts

The opening of the file, as written. The whole thing — 392 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Social Sentiment Analyzer

Overview

Analyze public sentiment for any brand, product, or topic across Twitter/X, Reddit, and Instagram. Fetches real posts, classifies sentiment, extracts themes, and produces a structured report.

When to Use

Activate when the user asks:

  • "What's the sentiment around [TOPIC]?"
  • "Analyze sentiment for [BRAND] on Twitter"
  • "What are people saying about [PRODUCT] on social media?"
  • "Is the reaction to [EVENT] positive or negative?"
  • "Social media opinion on [TOPIC]"

Setup & Authentication

Before fetching data, ensure Xpoz access is configured. Follow these checks in order.

Check 1: Already authenticated?

If you have MCP tools, try calling any Xpoz tool (e.g., checkAccessKeyStatus). If it works → skip to Step 1.

If you have the SDK, try:

from xpoz import XpozClient
client = XpozClient()  # reads XPOZ_API_KEY env var

If this succeeds without error → skip to Step 1.

If neither works, you need to authenticate. Get a free access key (below).


Recommended: a free access key

Real analyses need a real key: get a free access key (free tier, up to 75K results, no credit card). SDK and CLI users set it as XPOZ_API_KEY; MCP connections sign in with the same account via OAuth on first tool call (paths below).


Path A: MCP via mcporter (OpenClaw agents)

If mcporter is available:

mcporter call xpoz.checkAccessKeyStatus

If hasAccessKey: true → ready. If not:

mcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth

Then authenticate — generate the OAuth URL and send it to the user:

Step 1: Generate authorization URL

import secrets, hashlib, base64, urllib.parse, json, urllib.request, os

verifier = secrets.token_urlsafe(64)
challenge = base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()).rstrip(b'=').decode()
state = secrets.token_urlsafe(32)

# Dynamic client registration
reg_req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/register',
    data=json.dumps({
        'client_name': 'Agent Skills',
        'redirect_uris': ['https://www.xpoz.ai/oauth/openclaw'],
        'grant_types': ['authorization_code'],
        'response_types': ['code'],
        'token_endpoint_auth_method': 'none',
    }).encode(),
    headers={'Content-Type': 'application/json'},
)
reg_resp = json.loads(urllib.request.urlopen(reg_req).read())

params = urllib.parse.urlencode({
    'response_type': 'code',
    'client_id': reg_resp['client_id'],
    'code_challenge': challenge,
    'code_challenge_method': 'S256',
    'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw',
    'state': state,
    'scope': 'mcp:tools',
    'resource': 'https://mcp.xpoz.ai/',
})

auth_url = 'https://mcp.xpoz.ai/oauth/authorize?' + params

# Save state for token exchange
os.makedirs(os.path.expanduser('~/.cache/xpoz-oauth'), exist_ok=True)
with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json'), 'w') as f:
    json.dump({'verifier': verifier, 'state': state, 'client_id': reg_resp['client_id'],
               'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw'}, f)

print(auth_url)

Read the full file on GitHub · 392 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 · -11 lines scan B → A d59053be8b30
  2. 12d ago First seen · 403 lines · 72 tokens per session scan B f2d454c1b99e

Subscribe to this mod's changes

social-sentiment-analyzer is a skill published in the GitHub repository XPOZpublic/xpoz-agent-skills (15 stars, last pushed 3d ago), licensed MIT. It adds 72 tokens to every session and 3,151 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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