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.
npx skills add XPOZpublic/xpoz-agent-skills --skill social-sentiment-analyzergit clone --depth 1 https://github.com/XPOZpublic/xpoz-agent-skillsWrote 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.
[](https://agentmods.dev/skills/xpozpublic/xpoz-agent-skills/social-sentiment-analyzer)<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.
<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>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 | $0.00072 | $0.03151 |
| Opus 5 | $0.00036 | $0.01576 |
| Sonnet 5 | $0.00014 | $0.00630 |
| Haiku 4.5 | $0.00007 | $0.00315 |
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) 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)
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.
- 2d ago Changed · -11 lines scan B → A d59053be8b30
- 12d ago First seen · 403 lines · 72 tokens per session scan B f2d454c1b99e
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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