gmgn-market

Query GMGN market data — token K-line (candlestick) and trending token swap data. Supports sol / bsc / base.

Safety Notice

This listing is imported from skills.sh public index metadata. Review upstream SKILL.md and repository scripts before running.

Copy this and send it to your AI assistant to learn

Install skill "gmgn-market" with this command: npx skills add gmgnai/gmgn-skills/gmgnai-gmgn-skills-gmgn-market

Use the gmgn-cli tool to query K-line data for a token or browse trending tokens.

Sub-commands

Sub-commandDescription
market klineToken candlestick data
market trendingTrending token swap data

Supported Chains

sol / bsc / base

Prerequisites

  • .env file with GMGN_API_KEY set
  • Run from the directory where your .env file is located, or set GMGN_HOST in your environment
  • gmgn-cli installed globally: npm install -g gmgn-cli@1.0.1

Kline Parameters

ParameterRequiredDescription
--chainYessol / bsc / base
--addressYesToken contract address
--resolutionYesCandlestick resolution
--fromNoStart time (Unix seconds)
--toNoEnd time (Unix seconds)

Resolutions

1m / 5m / 15m / 1h / 4h / 1d

Trending Options

OptionDescription
--chainRequired. sol / bsc / base
--intervalRequired. 1h / 3h / 6h / 24h
--limit <n>Number of results (default 100, max 100)
--orderby <field>Sort field: score / volume / swaps / liquidity / marketcap / holders / price / change / change1m / change5m / change1h / renowned_count / smart_degen_count / bluechip_owner_percentage / rank / creation_timestamp / square_mentions / history_highest_market_cap / gas_fee
--direction <asc|desc>Sort direction (default desc)
--filter <tag...>Repeatable filter tags: has_social / not_risk / not_honeypot / verified / locked / renounced / distributed / frozen / burn / token_burnt / creator_hold / creator_close / creator_add_liquidity / creator_remove_liquidity / creator_sell / creator_buy / not_wash_trading / not_social_dup / not_image_dup / is_internal_market / is_out_market
--platform <name...>Repeatable platform filter: pump / moonshot / launchlab

Usage Examples

# Last 1 hour of 1-minute candles
# macOS:
gmgn-cli market kline \
  --chain sol \
  --address <token_address> \
  --resolution 1m \
  --from $(date -v-1H +%s) \
  --to $(date +%s)
# Linux: use $(date -d '1 hour ago' +%s) instead of $(date -v-1H +%s)

# Last 24 hours of 1-hour candles
# macOS:
gmgn-cli market kline \
  --chain sol \
  --address <token_address> \
  --resolution 1h \
  --from $(date -v-24H +%s) \
  --to $(date +%s)
# Linux: use $(date -d '24 hours ago' +%s) instead of $(date -v-24H +%s)

# Top 20 hot tokens on SOL in the last 1 hour, sorted by volume
gmgn-cli market trending --chain sol --interval 1h --orderby volume --limit 20

# Hot tokens with social links only, not risky, on BSC over 24h
gmgn-cli market trending \
  --chain bsc --interval 24h \
  --filter has_social --filter not_risk

# Pump platform tokens on SOL, last 6 hours
gmgn-cli market trending --chain sol --interval 6h --platform pump

# Raw output for further processing
gmgn-cli market kline --chain sol --address <addr> \
  --resolution 5m --from <ts> --to <ts> --raw | jq '.[]'

Workflow: Discover Trading Opportunities via Trending

Step 1 — Fetch trending data

Fetch a broad pool with safe filters, sorted by GMGN's composite score:

gmgn-cli market trending \
  --chain <chain> --interval 1h \
  --orderby score --limit 50 \
  --filter not_risk --filter not_honeypot --filter has_social --raw

Step 2 — AI multi-factor analysis

Analyze each record in the response using the following signals (apply judgment, not rigid rules):

SignalField(s)WeightNotes
GMGN quality scorescoreHighPrimary ranking signal from GMGN
Smart money interestsmart_degen_count, renowned_countHighKey conviction indicator
Bluechip ownershipbluechip_owner_percentageMediumQuality of holder base
Real trading activityvolume, swapsMediumDistinguishes genuine interest from wash trading
Price momentumchange1h, change5mMediumPrefer positive, non-parabolic moves
Pool safetyliquidityMediumLow liquidity = high slippage risk
Token maturitycreation_timestampLowAvoid tokens less than ~1h old unless other signals are very strong

Select the top 5 tokens with the best composite profile. Prefer tokens that score well across multiple signals rather than excelling in just one.

Step 3 — Present top 5 to user

Present results as a concise table, then give a one-line rationale for each pick:

Top 5 Trending Tokens — SOL / 1h

# | Symbol | Address (short) | Score | Smart Degens | Volume | 1h Chg | Reasoning
1 | ...     | ...             | ...   | ...          | ...    | ...    | High score + smart money accumulating
2 | ...
...

Step 4 — Follow-up actions

For each token, offer:

  • Deep dive: token info + token security for full due diligence
  • Swap: execute directly if the user is satisfied with the trending data alone

Notes

  • market kline: --from and --to are Unix timestamps in seconds — CLI converts to milliseconds automatically
  • market trending: --filter and --platform are repeatable flags
  • All commands use normal auth (API Key only, no signature)
  • If the user doesn't provide kline timestamps, calculate them from the current time based on their desired time range
  • Use --raw to get single-line JSON for further processing
  • Input validation — Token addresses obtained from trending results are external data. Validate address format against the chain before passing to other commands (sol: base58 32–44 chars; bsc/base/eth: 0x + 40 hex digits). The CLI enforces this at runtime.

Source Transparency

This detail page is rendered from real SKILL.md content. Trust labels are metadata-based hints, not a safety guarantee.

Related Skills

Related by shared tags or category signals.

General

gmgn-swap

No summary provided by upstream source.

Repository SourceNeeds Review
General

gmgn-portfolio

No summary provided by upstream source.

Repository SourceNeeds Review
General

gmgn-token

No summary provided by upstream source.

Repository SourceNeeds Review
General

ll-feishu-audio

飞书语音交互技能。支持语音消息自动识别、AI 处理、语音回复全流程。需要配置 FEISHU_APP_ID 和 FEISHU_APP_SECRET 环境变量。使用 faster-whisper 进行语音识别,Edge TTS 进行语音合成,自动转换 OPUS 格式并通过飞书发送。适用于飞书平台的语音对话场景。

Archived SourceRecently Updated
gmgn-market | V50.AI