Agent skill

Gmgn Holder Analysis

by GMGNAI in GMGNAI/gmgn-skills

Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart…

MITAuto-check passed

Install Gmgn Holder Analysis

skills CLI
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GMGNAI/gmgn-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/gmgn-holder-analysis .claude/skills/gmgn-holder-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
gmgn-holder-analysis
GitHub stars
609
Token cost
~4.3k tokens
SKILL.md length
2,345 words
Files
2
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart…

  • User asks about holder analysis
  • SKILL.md covers Analysis Script, Output Rule, Field Reference and Rating Standard, plus 3 more sections
  • Runs Python scripts from its folder; calls python3 and npm
  • Whether a token is safe to buy based on its holder composition

What it does

Gmgn Holder Analysis is an agent skill from GMGNAI/gmgn-skills. Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart money signals, and an AI rating based purely on token structure. Use when user asks about holder analysis, 筹码分析, 持仓分析, chip structure, who is holding, or whether a token is safe to buy based on its holder composition.

Its SKILL.md is about 4.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `analyze.py`).

The repository describes itself as: GMGN OpenAPI skills for AI Agent — query tokens, wallets, and market data, and execute on-chain trades across Solana, BSC, and Base. The licence is MIT.

When your agent uses it

  • User asks about holder analysis
  • Whether a token is safe to buy based on its holder composition

Example prompts

  • “/gmgn-holder-analysis”

Requirements

  • Python 3
  • Node.js

What it can do on your machine

Read from SKILL.md and the folder at commit 4575ef5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Gmgn Holder Analysis loads about 4.3k tokens when it runs. Until then it costs about 109 tokens; SKILL.md has 2,345 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~4.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GMGNAI/gmgn-skills at commit 4575ef5, republished under its MIT licence (© GMGNAI). 2,345 words, ~4,267 tokens.

Download SKILL.mdSave it as .claude/skills/gmgn-holder-analysis/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
gmgn-holder-analysis
description
Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart money signals, and an AI rating based purely on token structure. Use when user asks about holder analysis, 筹码分析, 持仓分析, chip structure, who is holding, or whether a token is safe to buy based on its holder composition.
argument-hint
--chain <sol|bsc|base|eth|robinhood|arc|stable> --address <token_address>
metadata.cliHelp
gmgn-cli token holders --help && gmgn-cli portfolio created-tokens --help

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:

bash
python3 ~/.claude/skills/gmgn-holder-analysis/analyze.py <FILL_IN_TOKEN_ADDRESS> <FILL_IN_CHAIN> <FILL_IN_LANG>
  • FILL_IN_CHAIN: sol for Solana addresses; for EVM 0x... addresses use auto unless the user explicitly specifies a chain (bsc/eth/base)
  • FILL_IN_LANG: zh if user wrote Chinese, en if English, default zh

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.

The same suppression applies when token holders returns an empty list (token has no active holders left, or upstream stopped indexing it). Every percentage would render 0.00% and every threshold would pass, so the report would otherwise read "✅ Normal — no obvious dump risk". The script prints a no-data banner instead, replaces each "none found 🟢" line with ⚪, and rates ⚪ Cannot Assess. "No data" is never reported as "no risk".

In both cases no float percentage is printed at all — every one renders as n/a (无法评估) and every percentage flag renders ⚪. Printing the number with a caveat was not enough: a divide-by-zero float puts hold 100.00% and hold 0.00% in the same report, and a reader skimming past the banner reads Rat Trader 1 hold 100.00% as a finding. Wallet counts, token amounts, USD values, and market caps still print — they do not pass through float_share. Percentages on a total-supply basis also still print (burn, DEX, float share itself, and the chip-quality buckets), because those denominators are unaffected.

"Zero cost" must be proven, not inferred from a missing buy count

buy_tx_count_cur == 0 does not mean the wallet received its chips for free. Measured on musebook (robinhood): 38 wallets read buy_tx_count_cur: 0 and avg_cost: null, but 34 of them carry a finite unrealized_pnl ratio (+2.7%, +76.5%, +546%) — a ratio that cannot be computed without a cost basis — and 32 carry a fomo frontend tag, 2 a gmgn tag, which is the trace of active trading, the opposite of receiving an airdrop. What is actually missing is buy-transaction indexing on that chain, while the PnL fields come from a separate computation and arrive populated.

The error was systematic per chain, not per token. Never-bought share of supply, same batch: robinhood 27.0% and 41.2%, arc 26.3%, sol 4.8%. The 20% airdrop warn gate therefore fired on essentially every robinhood/arc token and essentially never on a sol token — a difference in index coverage, not in chip structure. Reporting an unpopulated field as a risk conclusion is exactly what this skill forbids.

So a wallet counts as zero-cost only when no cost evidence exists at all:

python
def has_cost_basis(h):
    return (h.get('avg_cost') or 0) > 0 or h.get('unrealized_pnl') is not None

Wallets with no indexed buy count but a real cost basis are not dropped from the report — they get their own neutral line, 买入未记录 / Buy count unindexed, with their float share. The fact that upstream did not index their buys is true and stays visible; it just does not drive a zero-cost gate. The same predicate now governs the 钻石手 / Diamond cohort (its stated rationale is "a wallet that never paid has no cost to hold through", which a proven cost basis satisfies), the 转入筹码未动 / Idle airdrop line (its "zero cost" caption was a false statement for those wallets), and the Top5 sell-risk classification (which was labelling a wallet at +6.6x as "zero-cost airdrop — can dump anytime").

Known limit: a genuine airdrop wallet whose unrealized_pnl is a huge ratio computed against a dust-level cost would be missed here. No second ratio threshold is set for it — there is no measured sample to calibrate one on.

The token's own contract address is not a holder wallet

Upstream returns it as an ordinary wallet — measured on musebook (robinhood): addr_type: 0, 0 buys, 0 sells, 15.01% of total supply, $5.06M, carrying a fresh_wallet tag. Left in the wallet cohort it pollutes biggest, Top10/Top20, airdrop, fresh, risk wallets and Top5 at once, and on its own trips the 🔴 danger gate as "largest wallet holds 16.30% — extreme concentration". The largest genuine wallet on that token is 4.10% of supply. Controls on the same batch — JOLLY (robinhood), SI (sol), ARGUS (arc) — carry no such row, so this is a per-token upstream classification gap, not a chain convention.

So the script partitions it out of normal and reports it on its own line, on a total-supply basis (合约自持 / Contract self-held), with its own warn gate at >10% of supply. It is not removed from the judgement: contract-held supply reaches the market as soon as one release transaction lands, so it stays visible and still counts toward the rating. It is a warn rather than a danger because that release is an observable prior step, unlike a whale who can sell at will — and there is no higher danger tier, because only one sample has been measured and a second threshold off one sample would be a guess.

It is deliberately not deducted from the float denominator. Burn is permanent and the DEX pool is the market itself, so neither can dump; self-held supply can. Removing it from the denominator would raise every other wallet's percentage on every token carrying this row, manufacturing new false positives while fixing one.

The same address is also excluded from the dev sock-puppet map: chips sent back to the token contract, or gas paid to it, is not "transferred to an internal wallet".

Holder object key fields
FieldTypeMeaning
addressstringWallet address
balancefloatCurrent token balance
amount_percentagefloatFraction of total supply (0–1). Multiply by 100 for %.
buy_tx_count_curintBuy transactions since creation. Not indexed on every chain — a 0 here is not proof the wallet never bought. See the cost-basis section above.
usd_valuefloatCurrent USD value of holdings
avg_costfloatAverage buy price per token
unrealized_pnlfloatUnrealized PnL ratio (0.5 = +50%)
unrealized_profitfloatUnrealized PnL in USD
realized_profitfloatRealized PnL in USD
profitfloatTotal PnL in USD (realized + unrealized). Also a valid --order-by field.
sell_tx_count_curintSell transactions since token creation
sell_amount_percentagefloatFraction of total buys that have been sold. Drives the accumulating/distributing verdict.
sell_volume_curfloatUSD volume sold since token creation
sell_amount_curfloatToken amount sold since token creation
history_transfer_out_amountfloatToken amount transferred out (not sold)
history_transfer_out_incomefloatUSD value of transferred-out tokens
token_transfer_outobject{address} — recipient of a transfer-out. Used to detect dev sock puppets when the recipient is itself in the top 100.
native_transferobject{from_address, amount, timestamp} — how the wallet was funded. Also the fallback sock-puppet probe: a top-100 holder whose gas came from the creator.
namestringWallet display name if known
start_holding_atintUnix timestamp of first buy
addr_typeint0=normal wallet, 1=burn/dead, 2=DEX/pool
maker_token_tagslistbundler, rat_trader, sniper, whale, top_holder, transfer_in, dev_team, creator
tagslistsmart_degen, pump_smart, renowned, fresh_wallet, wash_trader, kol
native_balancestringRaw native token balance. May be a decimal string — parse with float, not int. Denominator is known only for sol (1e9) and bsc/eth/base (1e18).
native_transferobject{from_address, amount, timestamp} — how wallet was funded. Drives 关联资金.
twitter_namestringTwitter handle if known
Show full SKILL.md (953 more words)Show less
Created-tokens response fields
FieldMeaning
inner_countUnmigrated token count
open_countMigrated token count
tokens[].market_capCurrent market cap in USD
tokens[].symbolToken symbol
tokens[].is_opentrue = migrated
creator_ath_info.ath_mcAll-time high MC across all created tokens
creator_ath_info.ath_tokenToken address of the ATH token
creator_ath_info.token_symbolSymbol of the ATH token
creator_ath_info.token_nameName of the ATH token

Rating Standard

All thresholds below are share of tradeable float, matching the script. They are not comparable to GMGN's own UI, which reports share of total supply — on a token whose LP holds 56% of supply, the same wallet reads 2.4× higher here.

The Advice section prints every triggered reason, not just the first: a ⚠️ Caution rating means two or more warns fired by definition, so printing one left the reader unable to see why the rating was what it was.

Entry timing pressure (批次浮盈/出货) does NOT affect the overall rating — it only affects section display.

Rating (ZH)Rating (EN)EmojiCondition
无法评估Cannot Assess⚪Tradeable float <2% of supply, or upstream returned zero holders (all percentage rules suppressed; dev sock puppet still escalates to 🔴)
不建议买Not Recommended🔴Any: rat traders >5% / largest wallet >10% / dev sock puppet

| 谨慎参与 | Caution | ⚠️ | ≥2 of: Dev still holding >1% / airdrop >20% / risk wallets >35% / linked >15% / contract self-holds >10% of supply | | 可轻仓 | Light Position | 🟡 | Exactly 1 of above warns | | 正常参与 | Normal | ✅ | None of the above |

Per-metric flag thresholds
Metric🔴🟡🟢
Top10 concentration>60%>40%≤40%
Top20 concentration>75%>55%≤55%
Airdropped chips (never bought)>25%>10%≤10%
Risk wallets>35%>15%≤15%
Linked funding>25%>10%≤10%
Zero-balance wallets—>10%≤10%

Diamond hands invert (more is better) and use their own emoji set: ✅ >60% / 🟡 >35% / ⚠️ ≤35%. Diamond hands require a cost basis (has_cost_basis, above) — a wallet that never paid has no cost to hold through, so genuinely zero-cost recipients are reported separately as "转入筹码未动 / Idle airdrop" rather than being credited as diamond hands. The test is the cost basis, not buy_tx_count_cur > 0: on chains where buy counts are not indexed the latter discards most real diamond hands (measured on JOLLY: 34 → 75 wallets, 21.1% → 55.7% of float).

Linked funding is escalated to at least 🟡 whenever any group was funded within 60s, regardless of size — scripted batch funding is a structural signal, not a magnitude one.

Three mutually exclusive buckets over the chips held by observed wallets (denominator is normal_pct, i.e. total-supply basis, not float): bought in with no risk tag / zero-cost airdrop (by has_cost_basis, above — not by a missing buy count) / risk-tagged. They are reported separately rather than collapsed into one "healthy chips" number, because a risk tag means a proven-bad address while zero-cost airdrop only means unknown provenance.

Headline flag, first match wins: 🔴 risk-tagged >30% · 🟢 clean ≥50% · 🟡 clean ≥30% · 🟡 when zero-cost airdrop accounts for ≥80% of the non-clean remainder · 🔴 otherwise. So an airdrop-distributed token reads 🟡 with its composition spelled out, not "healthy chips 0.0% 🔴".

The composition is total-supply based, so it survives a degenerate float and its three percentages still print — but the headline flag is neutralized to ⚪ (and the chips' share of supply appended) whenever the rating is ⚪ Cannot Assess, since a 🔴/🟢 verdict over dust-level chips would contradict the rating above it.

Supported Chains

sol, bsc, base, eth, robinhood, arc, stable

Dev lookup is dual-source

token holders --tag dev is the primary source, but it returns {"list": []} on some launchpads — measured on all five bankr tokens in robinhood's 24h trending top 50, while pons_v2 / longxyz tokens on the same chain return the creator normally. It can also return rows with no creator tag at all (measured on a flap token on bsc). Either way the whole Dev section, including the dev sock-puppet danger gate, would silently go dead.

So when no creator-tagged row comes back, the creator address is recovered from token info's dev.creator_address, and dev.creator_token_balance supplies its balance. That call is made only when the primary source fails, so it costs nothing on tokens where the tag works.

token info does not carry realized_profit or token_transfer_out, so on the fallback path:

  • the realized-profit figure is omitted entirely — printing $0 would report an unmeasured field as a measurement
  • the sock-puppet gate switches judge: instead of "dev's chips went to a top-100 wallet", it asks whether any top-100 holder's native_transfer.from_address is the creator, i.e. the dev paid that wallet's gas. A hit is reported as the danger and names the wallets; a miss is not reported as clean, since only 57% of holders carry that field on the measured token.

Notes

  • balance >= 1 threshold avoids dust false positives when identifying dev holdings
  • SOL native_balance is in lamports (÷1e9); bsc/eth/base are in wei (÷1e18). Decimals for arc/stable/robinhood are unconfirmed, so the buying-power section reports "not assessed" on those chains rather than printing a converted figure that would be wrong.
  • Holder buying power needs a live native-token price, fetched with token info on the wrapped native address (So111…1112 / WBNB / WETH / Base WETH). When that call fails, the section falls back to native units and prints no USD figure.
  • total_supply is estimated as the median of balance / amount_percentage across normal wallets
  • cur_price is estimated as the median of usd_value / balance across normal wallets
  • Entry MC = total_supply * avg_cost, shown alongside unrealized PnL for every Top5 wallet
  • Top5 displays Twitter name when available; else first4...last4 format
  • Risk-wallet subtotals are per-category and can exceed the deduped total; the script prints how many wallets carry more than one risk tag when that happens.
  • creator_ath_info.ath_mc can lag behind the token's current MC after a fast pump (upstream ath_price has been seen equal to price_24h). The script cannot recompute it, so when the reported ATH sits more than 5% below the current MC it prints a staleness warning next to the figure instead of presenting it as the dev's peak.

© GMGNAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/gmgn-holder-analysis of GMGNAI/gmgn-skills.

  • SKILL.md
  • analyze.py

Open the folder on GitHubat commit 4575ef5

Compare with similar skills

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Questions about Gmgn Holder Analysis

What does Gmgn Holder Analysis do?

Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart…. Gmgn Holder Analysis is an agent skill from GMGNAI/gmgn-skills. Token holder chip analysis — deep analysis of holder structure including chip distribution, entry cost, whale/dev/KOL behavior, risk wallets (rat traders, bundlers, snipers), related wallets, smart money signals, and an AI rating based purely on token structure.

When should I use Gmgn Holder Analysis?

Gmgn Holder Analysis fits situations like: user asks about holder analysis; whether a token is safe to buy based on its holder composition.

How do I install Gmgn Holder Analysis in Claude Code?

Run `npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a claude-code`. Or copy the skill folder (skills/gmgn-holder-analysis in GMGNAI/gmgn-skills) into .claude/skills/gmgn-holder-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Gmgn Holder Analysis in Codex?

Run `npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a codex`. Or copy the skill folder (skills/gmgn-holder-analysis in GMGNAI/gmgn-skills) into .agents/skills/gmgn-holder-analysis in your project. Codex loads it when a task matches its description.

Can I use Gmgn Holder Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/gmgn-holder-analysis, .gemini/skills/gmgn-holder-analysis, .github/skills/gmgn-holder-analysis and .opencode/skills/gmgn-holder-analysis in your project.

What does Gmgn Holder Analysis need to run?

Going by SKILL.md and its folder, Gmgn Holder Analysis needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and npm). Our summary lists: Python 3; Node.js.

Does Gmgn Holder Analysis access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Gmgn Holder Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Gmgn Holder Analysis use?

Gmgn Holder Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Gmgn Holder Analysis use?

About 4.3k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Gmgn Holder Analysis?

Skills that share tags, products or a category with Gmgn Holder Analysis: Distributed Triage (pytorch/pytorch, 104k stars), Distributed Tracing (wshobson/agents, 40k stars), Distribute Skill To All Agents (sickn33/agentic-awesome-skills, 47k stars) and Distributed Training (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Gmgn Holder Analysis?

GMGNAI (a GitHub user) maintains it in GMGNAI/gmgn-skills, which has 609 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 28, 2026.

Source: GMGNAI/gmgn-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.