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 agiprolabs/claude-trading-skills --skill token-holder-analysisgit clone --depth 1 https://github.com/agiprolabs/claude-trading-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/agiprolabs/claude-trading-skills/token-holder-analysis)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/token-holder-analysis"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/token-holder-analysis/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/agiprolabs/claude-trading-skills/token-holder-analysis"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/token-holder-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00021 | $0.02787 |
| Opus 5 | $0.00010 | $0.01393 |
| Sonnet 5 | $0.00004 | $0.00557 |
| Haiku 4.5 | $0.00002 | $0.00279 |
Grade A, and why
token-holder-analysis 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.
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.
How it starts
The opening of the file, as written. The whole thing — 338 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Token Holder Analysis — Concentration, Distribution & Risk
Analyze who holds a token, how concentrated ownership is, and whether insider patterns suggest risk. This is a critical pre-trade safety check — high concentration means a few wallets can crash the price.
Quick Start
import httpx
import math
# Using Helius DAS API for holder data
HELIUS_KEY = os.getenv("HELIUS_API_KEY", "")
HELIUS = f"https://mainnet.helius-rpc.com/?api-key={HELIUS_KEY}"
# Or using SolanaTracker for holder + risk data
ST_KEY = os.getenv("SOLANATRACKER_API_KEY", "")
ST = "https://data.solanatracker.io"
# Get top holders via RPC
def get_top_holders(mint: str) -> list[dict]:
resp = httpx.post(HELIUS, json={
"jsonrpc": "2.0", "id": 1,
"method": "getTokenLargestAccounts",
"params": [mint],
})
return resp.json()["result"]["value"]
holders = get_top_holders("TOKEN_MINT")
Data Sources
| Source | What It Provides | Auth |
|---|---|---|
Solana RPC (getTokenLargestAccounts) |
Top 20 holders, supply | RPC key |
Helius DAS (getAsset, token accounts) |
Parsed holder data, metadata | API key |
SolanaTracker (/tokens/{t}/holders/top) |
Top 100 holders, bundler detection | API key |
Birdeye (/defi/token_security) |
Top 10 %, creator balance, freeze/mint auth | API key |
Concentration Metrics
Top-N Holder Percentage
The simplest measure — what % of supply do the top N holders control?
def top_n_percentage(holders: list[dict], supply: int, n: int = 10) -> float:
"""Calculate percentage held by top N holders.
Args:
holders: Sorted list of holders (largest first).
supply: Total token supply.
n: Number of top holders.
Returns:
Percentage (0-100) held by top N.
"""
top_n_amount = sum(int(h.get("amount", 0)) for h in holders[:n])
return top_n_amount / supply * 100 if supply > 0 else 0
Risk thresholds:
- Top 10 < 30%: Well distributed
- Top 10 30-50%: Moderate concentration
- Top 10 50-80%: High concentration — significant dump risk
- Top 10 > 80%: Extreme — likely controlled by a few wallets
What ships with it
5 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.
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 · 338 lines · 21 tokens per session scan A ae71db0a7b6c
token-holder-analysis is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 21 tokens to every session and 2,787 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 2026-08-30.
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