influencer-discovery

influencer-discovery is a skill for Claude Code, Codex from Aditya923-c/xpoz-agent-skills. It costs 68 tokens per session (3,318 once invoked), scanned A, original, MIT.

A research workflow for finding and ranking active voices, also called influencers or key opinion leaders, on Twitter/X, Instagram, and Reddit by topic, engagement, relevance, and authenticity.

In plain words
What is it for?
Use it to find thought leaders, niche creators, micro-influencers, or possible campaign partners for a given topic or product.
Why use it?
It removes the need to search several social platforms manually and judge every potential voice from scratch.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: reads .claude/ paths; mentions Claude Code; built for openclaw.

Good fit Use it to find thought leaders, niche creators, micro-influencers, or possible campaign partners for a given topic or product.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aditya923-c/xpoz-agent-skills/influencer-discovery
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 Aditya923-c/xpoz-agent-skills --skill influencer-discovery
Clone the repo
git clone --depth 1 https://github.com/Aditya923-c/xpoz-agent-skills

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 influencer-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/aditya923-c/xpoz-agent-skills/influencer-discovery/github.svg)](https://agentmods.dev/skills/aditya923-c/xpoz-agent-skills/influencer-discovery)
Your own site
<a href="https://agentmods.dev/skills/aditya923-c/xpoz-agent-skills/influencer-discovery"><img src="https://agentmods.dev/badge/skills/aditya923-c/xpoz-agent-skills/influencer-discovery/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 influencer-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/aditya923-c/xpoz-agent-skills/influencer-discovery"><img src="https://agentmods.dev/badge/skills/aditya923-c/xpoz-agent-skills/influencer-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 68 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,318 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
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 $0.00068 $0.03318
Opus 5 $0.00034 $0.01659
Sonnet 5 $0.00014 $0.00664
Haiku 4.5 $0.00007 $0.00332

Measured 12d ago against content hash 760b8aa110e7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

influencer-discovery 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 12d 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/influencer-discovery/SKILL.md · 403 lines

How it starts

The opening of the file, as written. The whole thing — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Influencer Discovery

Overview

Find, evaluate, and rank influencers for any niche across Twitter/X and Instagram. Identifies who is actively creating content about a topic, ranks them by engagement and relevance, and provides authenticity scoring.

When to Use

Activate when the user asks:

  • "Find influencers in [NICHE] on Twitter"
  • "Who are the top voices talking about [TOPIC]?"
  • "Discover thought leaders in [INDUSTRY]"
  • "Find micro-influencers for [PRODUCT CATEGORY]"
  • "KOL research for [TOPIC]"
  • "Who should we partner with for [CAMPAIGN]?"

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. Choose the path that fits your environment:


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 · 403 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. 12d ago First seen · 403 lines · 68 tokens per session scan A 760b8aa110e7

Subscribe to this mod's changes

influencer-discovery is a skill published in the GitHub repository Aditya923-c/xpoz-agent-skills (5 stars, last pushed yesterday), licensed MIT. It adds 68 tokens to every session and 3,318 once invoked, about $0.0003 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 2026-08-31.

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