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 GMGNAI/gmgn-skills --skill gmgn-holder-analysisgit clone --depth 1 https://github.com/GMGNAI/gmgn-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/gmgnai/gmgn-skills/gmgn-holder-analysis)<a href="https://agentmods.dev/skills/gmgnai/gmgn-skills/gmgn-holder-analysis"><img src="https://agentmods.dev/badge/skills/gmgnai/gmgn-skills/gmgn-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/gmgnai/gmgn-skills/gmgn-holder-analysis"><img src="https://agentmods.dev/badge/skills/gmgnai/gmgn-skills/gmgn-holder-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
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 System Prompt Leakage · line 26 Skill contains instructions that could directly expose system prompts, internal rules, or hidden instructions to users or external parties.Fix: Remove any instructions that reveal, print, or output system prompts or internal rules. System instructions should never be exposed to end users.
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 | $0.00096 | $0.02897 |
| Opus 5 | $0.00048 | $0.01448 |
| Sonnet 5 | $0.00019 | $0.00579 |
| Haiku 4.5 | $0.00010 | $0.00290 |
Grade A, and why
gmgn-holder-analysis scanned grade A with 1 finding 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 today.
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.
**IMPORTANT: Always use `gmgn-cli` commands. Do NOT use curl, WebFetch, or visit gmgn.ai.** How it starts
The opening of the file, as written. The whole thing — 176 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BEFORE RUNNING ANY COMMAND: Run gmgn-cli config --check. If exit code is 0, proceed normally. If exit code is 1, run gmgn-cli config and show output, then apply the key with gmgn-cli config --apply <KEY>. If unknown option, tell user to run npm install -g gmgn-cli.
IMPORTANT: Always use gmgn-cli commands. Do NOT use curl, WebFetch, or visit gmgn.ai.
When the user asks to analyze holders for a token, extract --chain and --address from their message, then run the analysis script below. Also detect the user's language: set LANG to 'zh' if the user wrote in Chinese, 'en' if in English (default 'zh').
Analysis Script
Run the following command, replacing the placeholders with the actual values:
python3 ~/.claude/skills/gmgn-holder-analysis/analyze.py <FILL_IN_TOKEN_ADDRESS> <FILL_IN_CHAIN> <FILL_IN_LANG>
- FILL_IN_CHAIN:
solfor Solana addresses; for EVM0x...addresses useautounless the user explicitly specifies a chain (bsc/eth/base) - FILL_IN_LANG:
zhif user wrote Chinese,enif English, defaultzh
Output Rule
After the script finishes, paste the complete stdout verbatim into your reply — every line, every section, nothing omitted or summarized. Do NOT add any introduction, commentary, or summary before or after the output block.
Field Reference
All holding percentages the script prints are share of tradeable float (1 - burn - DEX), not
share of total supply. amount_percentage from the API is share of total supply; the script
re-bases it. Because only the top 100 holders are fetched, a float percentage is a floor when
those 100 wallets do not cover the whole float; the footer reports the actual coverage and states
which case applies — floors when coverage <99.5%, complete values when the top 100 cover all of it.
When burn + DEX leave less than 2% of supply tradeable (typically a launchpad token before
migration), the float denominator degenerates: every / float_share inflates dust wallets to
double digits or 100%. The script detects this, prints a banner with absolute token/USD figures
instead, and sets the rating to ⚪ Cannot Assess.
What ships with it
1 file 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.
- today Changed 3ef444d241e3
- 12d ago First seen · 176 lines · 96 tokens per session scan A e0bcd9ac909f
gmgn-holder-analysis is a skill published in the GitHub repository GMGNAI/gmgn-skills (522 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 2,897 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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