Distributed Triage
pytorch/pytorch
Sub-triages issues in the oncall:distributed queue by assigning distributed module labels, routing to sub-oncalls, and marking triaged.
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…
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysis --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "gmgn-holder-analysis" agent skill from https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysis into .claude/skills/gmgn-holder-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gmgn-holder-analysis", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysisType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysis --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GMGNAI/gmgn-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/gmgn-holder-analysis .agents/skills/gmgn-holder-analysis && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "gmgn-holder-analysis" agent skill from https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysis into .agents/skills/gmgn-holder-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gmgn-holder-analysis", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysis --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GMGNAI/gmgn-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/gmgn-holder-analysis .cursor/skills/gmgn-holder-analysis && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "gmgn-holder-analysis" agent skill from https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysis into .cursor/skills/gmgn-holder-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gmgn-holder-analysis", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GMGNAI/gmgn-skills.git --path skills/gmgn-holder-analysis--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysis --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GMGNAI/gmgn-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/gmgn-holder-analysis .gemini/skills/gmgn-holder-analysis && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "gmgn-holder-analysis" agent skill from https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysis into .gemini/skills/gmgn-holder-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gmgn-holder-analysis", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysisInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GMGNAI/gmgn-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/gmgn-holder-analysis .github/skills/gmgn-holder-analysis && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "gmgn-holder-analysis" agent skill from https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysis into .github/skills/gmgn-holder-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gmgn-holder-analysis", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GMGNAI/gmgn-skills --skill gmgn-holder-analysis -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GMGNAI/gmgn-skills gmgn-holder-analysis --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GMGNAI/gmgn-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/gmgn-holder-analysis .opencode/skills/gmgn-holder-analysis && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "gmgn-holder-analysis" agent skill from https://github.com/GMGNAI/gmgn-skills/tree/main/skills/gmgn-holder-analysis into .opencode/skills/gmgn-holder-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "gmgn-holder-analysis", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
gmgn-holder-analysisToken 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. 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.
Read from SKILL.md and the folder at commit 4575ef5. It shows what the files ask for, not the result of running them.
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.
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
python3npmFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from GMGNAI/gmgn-skills at commit 4575ef5, republished under its MIT licence (© GMGNAI). 2,345 words, ~4,267 tokens.
.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.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').
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>sol for Solana addresses; for EVM 0x... addresses use auto unless the user explicitly specifies a chain (bsc/eth/base)zh if user wrote Chinese, en if English, default zhAfter 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.
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.
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:
def has_cost_basis(h):
return (h.get('avg_cost') or 0) > 0 or h.get('unrealized_pnl') is not NoneWallets 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.
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".
| Field | Type | Meaning |
|---|---|---|
address | string | Wallet address |
balance | float | Current token balance |
amount_percentage | float | Fraction of total supply (0–1). Multiply by 100 for %. |
buy_tx_count_cur | int | Buy 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_value | float | Current USD value of holdings |
avg_cost | float | Average buy price per token |
unrealized_pnl | float | Unrealized PnL ratio (0.5 = +50%) |
unrealized_profit | float | Unrealized PnL in USD |
realized_profit | float | Realized PnL in USD |
profit | float | Total PnL in USD (realized + unrealized). Also a valid --order-by field. |
sell_tx_count_cur | int | Sell transactions since token creation |
sell_amount_percentage | float | Fraction of total buys that have been sold. Drives the accumulating/distributing verdict. |
sell_volume_cur | float | USD volume sold since token creation |
sell_amount_cur | float | Token amount sold since token creation |
history_transfer_out_amount | float | Token amount transferred out (not sold) |
history_transfer_out_income | float | USD value of transferred-out tokens |
token_transfer_out | object | {address} — recipient of a transfer-out. Used to detect dev sock puppets when the recipient is itself in the top 100. |
native_transfer | object | {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. |
name | string | Wallet display name if known |
start_holding_at | int | Unix timestamp of first buy |
addr_type | int | 0=normal wallet, 1=burn/dead, 2=DEX/pool |
maker_token_tags | list | bundler, rat_trader, sniper, whale, top_holder, transfer_in, dev_team, creator |
tags | list | smart_degen, pump_smart, renowned, fresh_wallet, wash_trader, kol |
native_balance | string | Raw 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_transfer | object | {from_address, amount, timestamp} — how wallet was funded. Drives 关联资金. |
twitter_name | string | Twitter handle if known |
| Field | Meaning |
|---|---|
inner_count | Unmigrated token count |
open_count | Migrated token count |
tokens[].market_cap | Current market cap in USD |
tokens[].symbol | Token symbol |
tokens[].is_open | true = migrated |
creator_ath_info.ath_mc | All-time high MC across all created tokens |
creator_ath_info.ath_token | Token address of the ATH token |
creator_ath_info.token_symbol | Symbol of the ATH token |
creator_ath_info.token_name | Name of the ATH token |
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) | Emoji | Condition |
|---|---|---|---|
| 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 |
| 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.
sol, bsc, base, eth, robinhood, arc, stable
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:
$0 would report an unmeasured
field as a measurementnative_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.balance >= 1 threshold avoids dust false positives when identifying dev holdingsnative_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.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 walletscur_price is estimated as the median of usd_value / balance across normal walletstotal_supply * avg_cost, shown alongside unrealized PnL for every Top5 walletfirst4...last4 formatcreator_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
SKILL.md and 1 other file in skills/gmgn-holder-analysis of GMGNAI/gmgn-skills.
Open the folder on GitHubat commit 4575ef5
Gmgn Holder Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Gmgn Holder Analysis this skillGMGNAI/gmgn-skills | 609 | — | ~4.3k | Automated safety check: Pass | MIT | |
| Distributed Triagepytorch/pytorch | 104k | — | ~2.8k | Automated safety check: Pass | Custom licence | |
| Distributed Tracingwshobson/agents | 40k | 12 repos | ~527 | Automated safety check: Pass | MIT | |
| Distribute Skill To All Agentssickn33/agentic-awesome-skills | 47k | 1 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Distributed Trainingaiming-lab/AutoResearchClaw | 15k | — | ~216 | Automated safety check: Pass | MIT | |
| Debug Distributed Hangsgl-project/sglang | 37k | 2 repos | ~2.4k | Automated safety check: Pass | Apache-2.0 |
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wshobson/agents
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[FINANCIAL EXECUTION] Create and launch meme coins and crypto tokens on launchpads (Pump.fun, FourMeme, Bonk, BAGS, Flap, Klik, Clanker, etc.) via bonding curve fair launch, or query token creation…
GMGNAI/gmgn-skills
Get crypto and meme token price charts (K-line, candlestick, OHLCV), trending meme coin rankings by volume, newly launched tokens on launchpads (pump.fun, fourmeme, letsbonk, Raydium, etc.), the…
GMGNAI/gmgn-skills
Analyze one or many crypto wallets by address — holdings, batch realized/unrealized P&L, win rate, trading history, performance stats, specific token balance, and tokens created by a developer…
GMGNAI/gmgn-skills
Get real-time crypto buy/sell activity from Smart Money wallets, KOL influencer wallets, and personally followed wallets via GMGN API — alpha signals, whale tracking, meme token copy-trading ideas…
GMGNAI/gmgn-skills
The trader's decision dossier on a wallet — four pass/fail gates (is the record real, is the edge still working THIS week, can you actually get filled, does it cut losses) plus what the wallet is…
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.
Gmgn Holder Analysis fits situations like: user asks about holder analysis; whether a token is safe to buy based on its holder composition.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.