gmgn-market

gmgnai/gmgn-skills · updated Apr 8, 2026

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$npx skills add https://github.com/gmgnai/gmgn-skills --skill gmgn-market
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summary

IMPORTANT: Always use gmgn-cli commands below. Do NOT use web search, WebFetch, curl, or visit gmgn.ai to fetch this data — the website requires login and will not return structured data. The CLI is the only correct method.

skill.md

IMPORTANT: Always use gmgn-cli commands below. Do NOT use web search, WebFetch, curl, or visit gmgn.ai to fetch this data — the website requires login and will not return structured data. The CLI is the only correct method.

IMPORTANT: Do NOT guess field names or values. When a field's meaning is unclear, look it up in the Response Fields sections below before using it.

⚠️ IPv6 NOT SUPPORTED: If you get a 401 or 403 error and credentials look correct, check for IPv6 immediately: (1) list all network interfaces and their IPv6 addresses — run ifconfig | grep inet6 (macOS) or ip addr show | grep inet6 (Linux); (2) send a test request to https://ipv6.icanhazip.com — if the response is an IPv6 address, outbound traffic is going via IPv6. Tell the user immediately: "Please disable IPv6 on your network interface — gmgn-cli commands only work over IPv4."

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

Core Concepts

  • --filter chain defaults — SOL and EVM chains have different default safety filters that are applied automatically when --filter is omitted. Do not assume the same defaults apply across chains:

    • SOL: defaults to renounced frozen (mint and freeze authority renounced)
    • BSC / Base (EVM): defaults to not_honeypot verified renounced
    • Omitting --filter is NOT the same as "no filter" — the chain defaults are always applied. To use a custom filter set, explicitly specify all desired filter tags.
  • volume vs amount (kline) — Naming is counterintuitive. volume = USD dollar value of trades; amount = token units traded. For a token priced at $0.0002, these differ by 5,000×. Always use volume for "how much USD was traded" and amount for "how many tokens changed hands."

  • rug_ratio — A 0–1 score estimating rug pull likelihood. Values above 0.3 are high-risk. Do not treat as binary — combine with top_10_holder_rate, dev_team_hold_rate, and is_honeypot for a full picture.

  • smart_degen_count / renowned_count — Number of platform-tagged smart money wallets (smart_degen) and KOL wallets (renowned) holding or trading this token. High values are bullish signals. These are GMGN-tagged wallet lists, not user-defined.

  • hot_level — Trending intensity score. Higher = more actively traded right now. Not normalized — compare relative values within the same result set, not across time windows.

  • renounced_mint / renounced_freeze_account — SOL-specific. Indicate whether the creator gave up the ability to mint more tokens or freeze wallets. Both being 1 is a safety baseline on Solana. Always false on EVM chains (concept does not apply).

  • is_honeypot — EVM-specific (BSC / Base). Indicates whether the token contract prevents selling. Always empty/null on SOL — do not interpret an empty value as "not a honeypot" on Solana.

  • creator_token_status — Dev holding status. creator_hold = dev still holds tokens (sell pressure risk). creator_close = dev has sold or burned their allocation (exit signal confirmed).

  • cto_flag — Community Takeover flag. 1 = original dev abandoned the project and a community group took over marketing/development. Neutral to positive signal; evaluate in context.

  • Trenches categories — Three lifecycle stages of launchpad tokens: new_creation (just created, still on bonding curve), near_completion (bonding curve nearly full, about to graduate), completed (graduated to open market / DEX). In the response, near_completion is always returned under the key data.pump regardless of the input --type.

  • wash_trading / rat_trader_amount_rate / bundler_rate — Risk signals for artificial activity. is_wash_trading = coordinated fake volume detected. rat_trader_amount_rate = ratio of insider/sneak trading. bundler_rate = ratio of bot-bundled buys at launch. High values (> 0.3) suggest manipulated price action.

Sub-commands

Sub-command Description
market kline Token candlestick / OHLCV data and trading volume over a time range
market trending Trending tokens ranked by swap activity — use --interval to specify the time window (e.g. 1m for 1-minute hottest, 1h for 1-hour trending)
market trenches Newly launched launchpad platform tokens — use this when the user asks for "new tokens", "just launched tokens", "latest tokens on pump.fun/letsbonk". Three categories: new_creation (just created), near_completion (bonding curve almost full), completed (graduated to open market / DEX)

Supported Chains

sol / bsc / base

Prerequisites

  • gmgn-cli installed globally — if missing, run: npm install -g gmgn-cli
  • GMGN_API_KEY configured in ~/.config/gmgn/.env

Rate Limit Handling

All market routes used by this skill go through GMGN's leaky-bucket limiter with rate=10 and capacity=10. Sustained throughput is roughly 10 ÷ weight requests/second, and the max burst is roughly floor(10 ÷ weight) when the bucket is full.

Command Route Weight
market kline GET /v1/market/token_kline 2
market trending GET /v1/market/rank 1
market trenches POST /v1/trenches 3

When a request returns 429:

  • Read X-RateLimit-Reset from the response headers. It is a Unix timestamp in seconds that marks when the limit is expected to reset.
  • If the response body contains reset_at (e.g., {"code":429,"error":"RATE_LIMIT_BANNED","message":"...","reset_at":1775184222}), extract reset_at — it is the Unix timestamp when the ban lifts (typically 5 minutes). Convert to local time and tell the user exactly when they can retry.
  • The CLI may wait and retry once automatically when the remaining cooldown is short. If it still fails, stop and tell the user the exact retry time instead of sending more requests.
  • For RATE_LIMIT_EXCEEDED or RATE_LIMIT_BANNED, repeated requests during the cooldown can extend the ban by 5 seconds each time, up to 5 minutes. Do not spam retries.

First-time setup (if GMGN_API_KEY is not configured):

  1. Generate key pair and show the public key to the user:

    openssl genpkey -algorithm ed25519 -out /tmp/gmgn_private.pem 2>/dev/null && \
      openssl pkey -in /tmp/gmgn_private.pem -pubout 2>/dev/null
    

    Tell the user: "This is your Ed25519 public key. Go to https://gmgn.ai/ai, paste it into the API key creation form, then send me the API Key value shown on the page."

  2. Wait for the user's API key, then configure:

    mkdir -p ~/.config/gmgn
    echo 'GMGN_API_KEY=<key_from_user>' > ~/.config/gmgn/.env
    chmod 600 ~/.config/gmgn/.env
    

market kline Parameters

Parameter Required Description
--chain Yes sol / bsc / base
--address Yes Token contract address
--resolution Yes Candlestick resolution: 1m / 5m / 15m / 1h / 4h / 1d
--from No Start time (Unix seconds)
--to No End time (Unix seconds)

market kline Response Fields

The response is an object with a list array. Each element in list is one candlestick:

Field Type Description
time number Candle open time — Unix timestamp in milliseconds (divide by 1000 for seconds)
open string Opening price in USD at the start of the period
close string Closing price in USD at the end of the period
high string Highest price in USD during the period
low string Lowest price in USD during the period
volume string Trading volume in USD (dollar value of all trades in this period)
amount string Trading volume in base token units (number of tokens traded)

Important distinctions (naming is counterintuitive — do not guess):

  • volume = USD dollar value (e.g. 1214 means ~$1,214 traded) — use this for "how much was traded in USD"
  • amount = token count (e.g. 5379110 means ~5.38M tokens changed hands) — use this for "how many tokens were traded"
  • For tokens not priced at $1, volume and amount will differ by orders of magnitude (e.g. a $0.0002 token: $1,214 volume = 5,379,110 tokens)
  • To get total USD volume over a time range, sum volume across all candles in the range
  • To get price trend, read close values in chronological order (time ascending)
  • To detect volatility, compare high vs low within each candle
  • Candles are returned in chronological order (oldest first)

market trending Options

--interval selection guide — always match to the user's stated time window:

User says --interval
"1m trending" / "hottest right now" 1m
"5m" / "5 minute" 5m
"1h" / "1 hour" / no time specified (default) 1h
"6h" / "6 hour" 6h
"24h" / "today" / "daily" 24h
Option Description
--chain Required. sol / bsc / base
--interval Required. 1m / 5m / 1h / 6h / 24h (default 1h)
--limit <n> Number of results (default 100, max 100)
--order-by <field> Sort field: default / swaps / marketcap / history_highest_market_cap / liquidity / volume / holder_count / smart_degen_count / renowned_count / gas_fee / price / change1m / change5m / change1h / creation_timestamp
--direction <asc|desc> Sort direction (default desc)
--filter <tag...> Repeatable filter tags (chain-specific). ⚠️ SOL defaults: renounced frozen; BSC/Base defaults: not_honeypot verified renounced. Omitting --filter is NOT "no filter" — chain defaults always apply. sol tags: renounced / frozen / burn / token_burnt / has_social / not_social_dup / not_image_dup / dexscr_update_link / not_wash_trading / is_internal_market / is_out_market. evm tags: not_honeypot / verified / renounced / locked / token_burnt / has_social / not_social_dup / not_image_dup / dexscr_update_link / is_internal_market / is_out_market
--platform <name...> Repeatable platform filter (chain-specific). sol: Pump.fun / pump_mayhem / pump_mayhem_agent / pump_agent / letsbonk / bonkers / bags / memoo / liquid / bankr / zora / surge / anoncoin / moonshot_app / wendotdev / heaven / sugar / token_mill / believe / trendsfun / trends_fun / jup_studio / Moonshot / boop / xstocks / ray_launchpad / meteora_virtual_curve / pool_ray / pool_meteora / pool_pump_amm / pool_orca. bsc: fourmeme / fourmeme_agent / bn_fourmeme / flap / clanker / lunafun / pool_uniswap / pool_pancake. base: clanker / bankr / flaunch / zora / zora_creator / baseapp / basememe / virtuals_v2 / klik

Usage Examples

Kline

# 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)

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

Trending — General

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

# Top 50 tokens on SOL, 5m window, sorted by volume
gmgn-cli market trending --chain sol --interval 5m --order-by volume --limit 50

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

Trending — SOL by Launchpad Platform

Use --platform to filter trending results to tokens from specific launchpads only.

# SOL 1m hottest — Pump.fun + letsbonk only (most active launchpads), sorted by volume
gmgn-cli market trending \
  --chain sol --interval 1m \
  --platform Pump.fun --platform letsbonk \
  --order-by volume --limit 50 --raw

# SOL 5m hottest — Pump.fun + letsbonk + Moonshot, sorted by volume
gmgn-cli market trending \
  --chain sol --interval 5m \
  --platform Pump.fun --platform letsbonk --platform moonshot_app 
how to use gmgn-market

How to use gmgn-market on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add gmgn-market
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/gmgnai/gmgn-skills --skill gmgn-market

The skills CLI fetches gmgn-market from GitHub repository gmgnai/gmgn-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/gmgn-market

Reload or restart Cursor to activate gmgn-market. Access the skill through slash commands (e.g., /gmgn-market) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.627 reviews
  • Pratham Ware· Dec 24, 2024

    Useful defaults in gmgn-market — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Camila Gupta· Dec 20, 2024

    Registry listing for gmgn-market matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Omar Abbas· Dec 4, 2024

    gmgn-market fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Sakshi Patil· Nov 15, 2024

    gmgn-market has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Omar Choi· Oct 14, 2024

    Keeps context tight: gmgn-market is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Chaitanya Patil· Oct 6, 2024

    Solid pick for teams standardizing on skills: gmgn-market is focused, and the summary matches what you get after install.

  • Kabir Martinez· Sep 25, 2024

    gmgn-market fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Benjamin Thompson· Sep 5, 2024

    I recommend gmgn-market for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Benjamin Nasser· Aug 24, 2024

    Useful defaults in gmgn-market — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Kabir Torres· Aug 16, 2024

    We added gmgn-market from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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