KryptoGO Meme Trader Agent Skill

Overview

This skill enables an AI agent to analyze and trade meme coins through the KryptoGO platform, combining deep on-chain cluster analysis with trade execution.

Analysis (multi-chain: Solana, BSC, Base, Monad): wallet clustering, accumulation/distribution detection, address behavior labels, network-wide accumulation signals (Pro/Alpha tier).

Trading (Solana only): portfolio monitoring with PnL tracking, swap execution via DEX aggregator, local transaction signing (private key never leaves the machine).

Default mode is supervised — all trades require user confirmation. Autonomous trading is available as opt-in. See references/autonomous-trading.md for autonomous mode, cron setup, and learning system details.

When to Use

• User asks to analyze a meme coin or token on Solana/BSC/Base/Monad

• User asks to trade, buy, or sell tokens

• User asks to scan for trending tokens or market opportunities

• User asks to monitor portfolio positions or check PnL

• Cron-triggered periodic portfolio monitoring and signal scanning

When NOT to Use

• BTC/ETH/major L1 macro analysis, NFTs, cross-chain bridging, non-DEX transactions, non-Solana trading

Setup Flow

1. Get API Key

• Go to kryptogo.xyz/account and create an API key

• Add to ~/.openclaw/workspace/.env : echo 'KRYPTOGO_API_KEY=sk_live_YOUR_KEY' >> ~/.openclaw/workspace/.env && chmod 600 ~/.openclaw/workspace/.env

Do NOT paste your API key directly in chat. Always set secrets via .env file.

2. Generate Agent Wallet

python3 scripts/setup.py

Creates a Solana keypair, saves to .env with chmod 600, prints public address to fund.

3. Fund the Wallet

Send SOL to the agent's public address (minimum 0.1 SOL).

Security Rules

• NEVER print, log, or include private keys in any message or CLI argument

• NEVER accept secrets pasted directly in chat — instruct users to set them in .env

• NEVER use the Read tool on .env — load credentials via source command only

• Runtime scripts do NOT read .env directly — all credentials are accessed via environment variables only, which must be pre-loaded by the caller (source ~/.openclaw/workspace/.env )

• Exception: scripts/setup.py reads and writes .env for initial keypair generation and address repair — this is the only script that touches credential files

• Private key stays in memory only during local signing — never sent to any server

Authentication

All endpoints require: Authorization: Bearer sk_live_<48 hex chars>

Tier Daily API Calls Trading Fee Signal Dashboard KOL Finder

Free 100 calls/day 1% No No

Pro 1,000 calls/day 0.5% Yes Yes

Alpha 5,000 calls/day 0% Yes Yes

Agent Behavior

Session Initialization

On every session start (including heartbeat/cron), the agent MUST load credentials BEFORE running any scripts:

source ~/.openclaw/workspace/.env

This is REQUIRED — scripts do not read .env directly. All credentials are accessed via environment variables only.

Default Mode: Supervised

By default, the agent operates in supervised mode: it analyzes tokens, presents recommendations, and waits for user approval before executing any trade. Stop-loss/take-profit conditions are reported to the user but not auto-executed.

To enable autonomous trading, set require_trade_confirmation: false in preferences. See references/autonomous-trading.md for full details.

Persistence (CRITICAL)

IMMEDIATELY after submitting a transaction, the agent MUST:

• Write trade details to memory/trading-journal.json with status: "OPEN"

• Include: token_symbol , token_address , entry_price , position_size_sol , tx_hash , timestamp

User Preferences

Store in memory/trading-preferences.json :

Preference Default Description

max_position_size

0.1 SOL Max SOL per trade

max_open_positions

5 Max concurrent open positions

max_daily_trades

20 Max trades per day

stop_loss_pct

30% Notify/sell when loss exceeds this

take_profit_pct

100% Notify/sell when gain exceeds this

min_market_cap

$500K Skip tokens below this

scan_count

10 Trending tokens per scan

risk_tolerance

"conservative" "conservative" (skip medium risk), "moderate" (ask on medium), "aggressive" (auto-trade medium)

require_trade_confirmation

true Set to false for autonomous mode

chains

["solana"] Chains to scan

Safety Guardrails

Trading Limits (Hard Caps)

Limit Default Overridable?

Max single trade 0.1 SOL Yes, via max_position_size

Max concurrent positions 5 Yes, via max_open_positions

Max daily trade count 20 Yes, via max_daily_trades

Price impact abort

10% No — always abort

Price impact warn

5% No — always warn

If any limit is hit, the agent must stop and notify the user.

Credential Isolation

Runtime scripts in this skill do NOT read .env files directly. All credentials are accessed via environment variables only, which must be pre-loaded by the caller (source ~/.openclaw/workspace/.env ). This ensures no runtime script can independently access or exfiltrate credential files.

Exception: scripts/setup.py reads and writes .env — it loads existing keys to avoid regeneration, backs up .env before changes, and writes new keypair entries. This is the only script that touches credential files, and it runs only during initial setup or explicit --force regeneration.

Automated Monitoring (Cron)

Quick Setup

Supervised mode (default): analysis + notifications, no auto-execution

source ~/.openclaw/workspace/.env && bash scripts/cron-examples.sh setup-default

Autonomous mode (opt-in): auto-buys and auto-sells

source ~/.openclaw/workspace/.env && bash scripts/cron-examples.sh setup-autonomous

Remove all cron jobs

bash scripts/cron-examples.sh teardown

Job Interval Default Behavior

stop-loss-tp

5 min Report triggered conditions, do NOT auto-sell

discovery-scan

1 hour Analyze and send recommendations, do NOT auto-buy

For full cron configuration, manual setup, heartbeat alternative, and monitoring workflow details, see references/autonomous-trading.md .

On-Chain Analysis Framework (7-Step Pipeline)

Step 1: Token Overview & Market Cap Filter

/token-overview?address=<mint>&chain_id=<id> — get name, price, market cap, holders, risk_level. Skip if market cap < min_market_cap .

Step 2: Cluster Analysis

/analyze/<mint>?chain_id=<id> — wallet clusters, top holders, metadata.

• ≥30-35% = "controlled" — major entity present

• ≥50% = high concentration risk

• Single cluster >50% → skip (rug pull risk)

Free tier limitation: Cluster analysis only returns the top 2 clusters. To see full cluster data, upgrade at kryptogo.xyz/pricing.

Step 3: Cluster Trend (Multi-Timeframe)

/analyze-cluster-change/<mint> — cluster_ratio

• changes across 15m/1h/4h/1d/7d.

Core insight: Price and cluster holdings DIVERGING is the key signal.

• Rising price + falling cluster % = distribution (bearish)

• Falling price + rising cluster % = accumulation (bullish)

Step 4: Address Labels + Sell Pressure Verification

• /token-wallet-labels → identify dev/sniper/bundle wallets

• /balance-history for each risky address → check if still holding

• Compute risky_ratio = active risky holdings / total cluster holdings

30% = high risk, 10-30% = medium, <10% = low

Labels represent behavioral history, not current holdings. Always verify via /balance-history .

Step 5: Deep Dive (Optional)

/balance-history , /balance-increase/<mint> , /top-holders-snapshot/<mint> , /analyze-dca-limit-orders/<mint> , /cluster-wallet-connections

Step 6: Decision

Apply Bullish Checklist from references/decision-framework.md .

Step 7: Execute Trade

Use scripts/swap.py for execution — handles wallet_address injection, error checking, and journal logging.

source ~/.openclaw/workspace/.env && python3 scripts/swap.py <token_mint> 0.1 source ~/.openclaw/workspace/.env && python3 scripts/swap.py <token_mint> <amount> --sell

API Quick Reference

Endpoint Method Purpose

/agent/account

GET Check tier & quota

/agent/trending-tokens

GET Scan trending tokens

/agent/portfolio

GET Wallet portfolio + PnL

/agent/swap

POST Build unsigned swap tx (Solana only)

/agent/submit

POST Submit signed tx (Solana only)

/token-overview

GET Token metadata & market data

/analyze/:token_mint

GET Full cluster analysis

/analyze-cluster-change/:token_mint

GET Cluster ratio trends

/balance-history

POST Time-series balance data

/wallet-labels

POST Behavior labels

/token-wallet-labels

POST Token-specific labels

/signal-dashboard

GET Curated accumulation signals (Pro+)

Full request/response details: see references/api-reference.md

Multi-Chain Support

Chain chain_id Analysis Trading

Solana 501

Yes Yes

BSC 56

Yes No

Base 8453

Yes No

Monad 143

Yes No

Error Handling

Code Meaning Action

400 Bad Request Check parameters

401 Unauthorized Check API key

402 Quota Exceeded Wait for daily reset or upgrade

403 Forbidden Requires higher tier

502/504 Server error Retry once after 10s

Operational Scripts

All scripts require credentials to be pre-loaded: source ~/.openclaw/workspace/.env before running.

source ~/.openclaw/workspace/.env && bash scripts/portfolio.sh # Portfolio check source ~/.openclaw/workspace/.env && bash scripts/trending.sh # Trending tokens source ~/.openclaw/workspace/.env && bash scripts/analysis.sh # Full analysis dashboard source ~/.openclaw/workspace/.env && python3 scripts/swap.py <mint> 0.1 # Buy source ~/.openclaw/workspace/.env && python3 scripts/swap.py <mint> <amt> --sell # Sell source ~/.openclaw/workspace/.env && bash scripts/test-api.sh # API connectivity test

Learning & Adaptation

The agent improves over time by recording trades, analyzing outcomes, and adjusting strategy. Every trade is logged to memory/trading-journal.json , losses trigger post-mortems, and periodic reviews propose parameter changes.

For full details on the learning system, trade journal format, post-mortem process, and strategy reviews, see references/autonomous-trading.md .

Core Concepts

Concept Key Insight

Cluster Group of wallets controlled by same entity

Cluster Ratio % of supply held by clusters. ≥30% = controlled, ≥50% = high risk

Developer Deployed the token. Highest dump risk

Sniper Bought within 1s of creation. Sell pressure if not cleared

Smart Money Realized profit >$100K. Accumulation often precedes price moves

Accumulation Cluster % rising + price consolidating = bullish

Distribution Price rising + cluster % falling = bearish

Full concepts guide: see references/concepts.md

Best Practices

• Always check /agent/account first to confirm tier and quota

• Always check /agent/portfolio on startup to detect existing positions

• Never expose private keys in logs, messages, or CLI arguments

• Validate price impact before submitting — abort >10%, warn >5%

• Sign and submit promptly — blockhash expires after ~60 seconds

• Persist state to memory/trading-state.json after every action

• Log every trade to journal — no exceptions

• Read memory/trading-lessons.md before scanning — avoid repeating known bad patterns

File Structure

kryptogo-meme-trader/ ├── SKILL.md ← You are here ├── package.json ├── .env.example ├── references/ │ ├── api-reference.md ← Full API docs │ ├── concepts.md ← Core concepts │ ├── decision-framework.md ← Entry/exit strategies │ └── autonomous-trading.md ← Autonomous mode, cron, learning system ├── scripts/ │ ├── setup.py ← First-time setup │ ├── cron-examples.sh ← Cron configurations │ ├── portfolio.sh / trending.sh / analysis.sh / test-api.sh │ ├── swap.py ← Swap executor │ └── trading-preferences.example.json └── examples/ ├── trading-workflow.py └── deep-analysis-workflow.py