Drift SDK
alsk1992/CloddsBot
Drift Protocol perpetual futures trading on Solana (direct SDK)
Behavioral classification, performance analysis, and trading style detection for Solana wallets
Install Wallet Profiling
$ npx skills add agiprolabs/claude-trading-skills --skill wallet-profiling -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install agiprolabs/claude-trading-skills wallet-profiling --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/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/wallet-profiling .claude/skills/wallet-profiling && 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 "wallet-profiling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profiling into .claude/skills/wallet-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wallet-profiling", 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/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profilingType 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 agiprolabs/claude-trading-skills --skill wallet-profiling -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install agiprolabs/claude-trading-skills wallet-profiling --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/wallet-profiling .agents/skills/wallet-profiling && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "wallet-profiling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profiling into .agents/skills/wallet-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wallet-profiling", 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 agiprolabs/claude-trading-skills --skill wallet-profiling -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install agiprolabs/claude-trading-skills wallet-profiling --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/wallet-profiling .cursor/skills/wallet-profiling && 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 "wallet-profiling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profiling into .cursor/skills/wallet-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wallet-profiling", 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/agiprolabs/claude-trading-skills.git --path skills/wallet-profiling--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 agiprolabs/claude-trading-skills --skill wallet-profiling -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install agiprolabs/claude-trading-skills wallet-profiling --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/wallet-profiling .gemini/skills/wallet-profiling && 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 "wallet-profiling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profiling into .gemini/skills/wallet-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wallet-profiling", 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 agiprolabs/claude-trading-skills wallet-profilingInstalls 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 agiprolabs/claude-trading-skills --skill wallet-profiling -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/wallet-profiling .github/skills/wallet-profiling && 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 "wallet-profiling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profiling into .github/skills/wallet-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wallet-profiling", 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 agiprolabs/claude-trading-skills --skill wallet-profiling -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install agiprolabs/claude-trading-skills wallet-profiling --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/wallet-profiling .opencode/skills/wallet-profiling && 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 "wallet-profiling" agent skill from https://github.com/agiprolabs/claude-trading-skills/tree/main/skills/wallet-profiling into .opencode/skills/wallet-profiling/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wallet-profiling", 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.
Facts
- Skill name
wallet-profiling- GitHub stars
- 410
- Token cost
- ~2.3k tokens
- SKILL.md length
- 922 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 68
- Repo updated
- First seen
- Licence
- MIT
At a glance
Behavioral classification, performance analysis, and trading style detection for Solana wallets
- Tasks that involve Trading and backtesting
- SKILL.md covers Why Wallet Profiling Matters, Wallet Classification, Performance Metrics and Data Sources, plus 5 more sections
- Runs Python scripts from its folder; calls uv; reaches data.solanatracker.io; needs ST_API_KEY
What it does
Wallet Profiling is an agent skill from agiprolabs/claude-trading-skills. Behavioral classification, performance analysis, and trading style detection for Solana wallets
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/classification_methods.md`, `references/performance_metrics.md` and `scripts/compare_wallets.py`).
It sits in Business, Finance & HR, covering Trading and backtesting. It works with Solana. The repository describes itself as: 68 trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools. The licence is MIT.
When your agent uses it
- Tasks that involve Trading and backtesting
Example prompts
- “/wallet-profiling”
Requirements
- Python 3
- A credential in ST_API_KEY
What it can do on your machine
Read from SKILL.md and the folder at commit 981e1d7. 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 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
data.solanatracker.io
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
ST_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Wallet Profiling loads about 2.3k tokens when it runs, and up to ~6.7k if it reads all its reference files. Until then it costs about 28 tokens; SKILL.md has 922 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.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from agiprolabs/claude-trading-skills at commit 981e1d7, republished under its MIT licence (© agiprolabs). 922 words, ~2,345 tokens.
Download SKILL.mdSave it as
.claude/skills/wallet-profiling/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- wallet-profiling
- description
- Behavioral classification, performance analysis, and trading style detection for Solana wallets
Wallet Profiling
Behavioral classification, performance analysis, and trading style detection for Solana wallets. Profile any wallet to understand how it trades, how well it performs, and whether it is worth following.
Why Wallet Profiling Matters
Copy-Trade Evaluation
Before mirroring another wallet's trades, you need evidence that its historical performance is genuine, consistent, and not the result of a single lucky hit. Profiling quantifies win rate, profit factor, hold time, and consistency so you can make informed decisions about which wallets merit attention.
Smart Money Identification
Wallets that consistently buy tokens early and exit profitably are signal sources. Profiling separates genuinely skilled traders from lucky gamblers and wash-trading bots. Key differentiators: sustained profit factor above 2.0, win rates above 45% across 100+ trades, and diversified token selection.
Counterparty Analysis
When a large wallet enters a position you hold, understanding its historical behavior (sniper vs. holder, bot vs. human) helps you anticipate what will happen next. A sniper wallet buying suggests a quick dump is coming; a swing trader buying suggests multi-day conviction.
Risk Assessment
Token holder analysis benefits from knowing whether top holders are bots, snipers, or genuine investors. A token where 60% of holders are classified as snipers has very different risk characteristics than one held primarily by swing traders.
Wallet Classification
By Trading Style
Classification is based on the median hold time across all closed trades:
| Style | Median Hold Time | Characteristics |
|---|---|---|
| Sniper | < 5 minutes | First-block buyers, MEV-adjacent, extremely fast exits |
| Scalper | 5 min – 1 hour | Quick momentum trades, high frequency |
| Day Trader | 1 – 24 hours | Intraday positions, moderate frequency |
| Swing Trader | 1 – 7 days | Multi-day conviction holds |
| Position Holder | > 7 days | Long-term accumulation, low frequency |
See references/classification_methods.md for the full classification algorithm.
By Trade Size
Based on median trade size in SOL:
| Tier | Median Trade Size | Typical Behavior |
|---|---|---|
| Whale | > 100 SOL | Market-moving entries, often front-run |
| Large | 10 – 100 SOL | Significant but not dominant |
| Medium | 1 – 10 SOL | Active retail traders |
| Small | < 1 SOL | Micro-cap gamblers, new wallets |
By Behavior Type
| Type | Detection Method |
|---|---|
| Bot | Low inter-trade timing variance (CV < 0.3), uniform sizing |
| Human | Variable timing, variable sizing, session-based activity |
| MEV | Sandwich patterns, consistent small profits, high frequency |
By Focus Area
| Focus | Detection Criteria |
|---|---|
| PumpFun Specialist | > 70% of trades on PumpFun-launched tokens |
| DEX Trader | Primarily swaps on Raydium/Orca/Meteora |
| DeFi Farmer | Frequent LP add/remove, staking operations |
| NFT Trader | Significant NFT marketplace interactions |
| Multi-Strategy | No single category exceeds 50% |
Performance Metrics
Core Metrics
Win Rate — Percentage of trades that are profitable.
win_rate = count(pnl > 0) / count(all_closed_trades)Minimum 30 trades for statistical significance. A 60% win rate across 200 trades is far more meaningful than 80% across 10 trades.
Average ROI Per Trade — Mean return across all closed positions.
avg_roi = mean((exit_value - entry_value) / entry_value)Include all fees: platform fees, priority fees, and estimated slippage.
Profit Factor — Ratio of gross profits to gross losses.
profit_factor = sum(winning_pnl) / abs(sum(losing_pnl))Interpretation: > 2.0 excellent, 1.5–2.0 good, 1.0–1.5 marginal, < 1.0 losing.
Total PnL — Cumulative profit/loss in SOL.
total_pnl = sum(all_trade_pnl)Maximum Drawdown — Largest peak-to-trough decline in cumulative PnL curve.
drawdown = (peak_equity - trough_equity) / peak_equitySharpe-Like Ratio — Risk-adjusted return metric.
sharpe = mean(trade_returns) / std(trade_returns) * sqrt(trades_per_year)See references/performance_metrics.md for detailed formulas, edge cases, and interpretation guidelines.
Activity Metrics
| Metric | Calculation | What It Reveals |
|---|---|---|
| Trades per day | total_trades / active_days | Activity level and capacity |
| Average hold time | mean(exit_time - entry_time) | Trading style confirmation |
| Token diversity | unique_tokens / total_trades | Specialization vs. diversification |
| Peak hours | mode(hour_of_trade) | Session patterns, timezone hints |
| Activity streaks | consecutive active days | Dedication and consistency |
Data Sources
Show full SKILL.md (375 more words)Show less
SolanaTracker PnL API (Primary)
The SolanaTracker API provides pre-computed PnL data per wallet per token.
import httpx
url = f"https://data.solanatracker.io/pnl/{wallet_address}"
headers = {"x-api-key": os.getenv("ST_API_KEY")}
resp = httpx.get(url, headers=headers)
pnl_data = resp.json()Response includes per-token: realized, unrealized, total_invested, total_sold, num_buys, num_sells, last_trade_time.
Helius Parsed Transactions (Detailed)
For granular transaction-level analysis, use the helius-api skill to fetch parsed transaction history. This provides exact timestamps, amounts, and program interactions.
Birdeye Trader Data
Birdeye's trader endpoints provide wallet-level analytics. See the birdeye-api skill for endpoint details.
DexScreener (Free Fallback)
DexScreener does not provide wallet-level PnL but can be used to validate token prices at trade timestamps.
Copy-Trade Evaluation Framework
Before following a wallet's trades, verify these criteria:
Minimum Requirements
- Trade history: At least 50 closed trades (100+ preferred)
- Time span: Active for at least 30 days
- Consistent performance: Rolling 7-day win rate standard deviation < 15%
- Reasonable sizing: No single trade > 20% of observed portfolio
- Diverse tokens: At least 10 unique tokens traded
Green Flags
- Profit factor > 1.8 sustained over 60+ days
- Win rate 45–65% (unrealistically high rates suggest wash trading)
- Moderate trade frequency (2–20 trades/day)
- Mixed hold times indicating adaptive strategy
- Gradual equity curve growth (not step-function jumps)
Red Flags
- New wallet (< 14 days old): Possible sybil or one-hit-wonder
- Single big win: One trade accounts for > 50% of total PnL
- Declining performance: Last-30-day metrics significantly below all-time
- Bot-like patterns: Uniform timing/sizing without proportional edge
- Extreme win rate: > 80% often indicates small wins with catastrophic losses
- Concentration: > 50% of PnL from a single token
- Wash trading signals: Repeated buy/sell of same token with minimal price movement
Risk Score Calculation
risk_score = 0 # 0 = low risk, 100 = high risk
if wallet_age_days < 14:
risk_score += 25
if top_trade_pnl_pct > 0.5:
risk_score += 20
if recent_pf < historical_pf * 0.7:
risk_score += 15
if bot_probability > 0.7:
risk_score += 15
if win_rate > 0.8:
risk_score += 10
if unique_tokens < 5:
risk_score += 15Integration with Other Skills
whale-tracking: Identify large wallets, then profile them here for behavioral contexttoken-holder-analysis: Profile top holders of a token to assess holder qualitysolana-onchain: Fetch raw transaction data for deep-dive analysishelius-api: Parsed transaction history for granular trade reconstructionbirdeye-api: Token price data for PnL validation
Quick Start
Profile a Single Wallet
# Set environment variables
# export WALLET_ADDRESS=YourTargetWallet...
# export ST_API_KEY=your_solanatracker_key (optional)
python scripts/profile_wallet.py
# Or use demo mode:
python scripts/profile_wallet.py --demoCompare Multiple Wallets
# export WALLET_ADDRESSES=Wallet1...,Wallet2...,Wallet3...
# export ST_API_KEY=your_solanatracker_key (optional)
python scripts/compare_wallets.py
# Or use demo mode:
python scripts/compare_wallets.py --demoFiles
| File | Description |
|---|---|
references/classification_methods.md | Hold time, size, bot detection, and focus classification algorithms |
references/performance_metrics.md | Detailed metric formulas, interpretation, edge cases, and decay detection |
scripts/profile_wallet.py | Profile a single wallet: fetch data, compute metrics, classify, report |
scripts/compare_wallets.py | Compare multiple wallets side-by-side with ranking |
Dependencies
uv pip install httpxEnvironment Variables
| Variable | Required | Description |
|---|---|---|
WALLET_ADDRESS | For profile_wallet.py | Solana wallet address to profile |
WALLET_ADDRESSES | For compare_wallets.py | Comma-separated wallet addresses |
ST_API_KEY | No | SolanaTracker API key for PnL data |
© agiprolabs, 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 4 other files (scripts, references) in skills/wallet-profiling of agiprolabs/claude-trading-skills.
- SKILL.md
- references/classification_methods.md
- references/performance_metrics.md
- scripts/compare_wallets.py
- scripts/profile_wallet.py
Open the folder on GitHubat commit 981e1d7
Compare with similar skills
Wallet Profiling 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 |
|---|---|---|---|---|---|---|
| Wallet Profiling this skillagiprolabs/claude-trading-skills | 410 | — | ~2.3k | Automated safety check: Pass | MIT | |
| Drift SDKalsk1992/CloddsBot | 2.9k | 1 repos | ~625 | Automated safety check: Pass | MIT | |
| Prismirfndi/prism-liquidity-agent | 122 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Solana Sniper Botnpc-live/clawfirm | 156 | — | ~913 | Automated safety check: Notes | None | |
| Solana Payments Wallets Tradingnpc-live/clawfirm | 156 | 1 repos | ~4.7k | Automated safety check: Pass | MIT | |
| Gmgn PortfolioGMGNAI/gmgn-skills | 604 | — | ~5.8k | Automated safety check: Notes | MIT |
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Questions about Wallet Profiling
What does Wallet Profiling do?
Behavioral classification, performance analysis, and trading style detection for Solana wallets. Wallet Profiling is an agent skill from agiprolabs/claude-trading-skills.
When should I use Wallet Profiling?
Wallet Profiling fits situations like: tasks that involve Trading and backtesting.
How do I install Wallet Profiling in Claude Code?
Run `npx skills add agiprolabs/claude-trading-skills --skill wallet-profiling -a claude-code`. Or copy the skill folder (skills/wallet-profiling in agiprolabs/claude-trading-skills) into .claude/skills/wallet-profiling in your project. Claude Code loads it when a task matches its description.
How do I install Wallet Profiling in Codex?
Run `npx skills add agiprolabs/claude-trading-skills --skill wallet-profiling -a codex`. Or copy the skill folder (skills/wallet-profiling in agiprolabs/claude-trading-skills) into .agents/skills/wallet-profiling in your project. Codex loads it when a task matches its description.
Can I use Wallet Profiling 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 agiprolabs/claude-trading-skills --skill wallet-profiling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wallet-profiling, .gemini/skills/wallet-profiling, .github/skills/wallet-profiling and .opencode/skills/wallet-profiling in your project.
What does Wallet Profiling need to run?
Going by SKILL.md and its folder, Wallet Profiling needs Python for the scripts in its folder, the command-line tools its instructions call (uv) and credentials named ST_API_KEY. Our summary lists: Python 3; A credential in ST_API_KEY.
Does Wallet Profiling access the network?
SKILL.md names 1 domain. In commands or code: data.solanatracker.io; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is Wallet Profiling 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Wallet Profiling use?
Wallet Profiling 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 Wallet Profiling use?
About 2.3k tokens (SKILL.md is roughly 9.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.3k tokens, read only when the agent opens those files.
What are the alternatives to Wallet Profiling?
Skills that share tags, products or a category with Wallet Profiling: Drift SDK (alsk1992/CloddsBot, 2.9k stars), Prism (irfndi/prism-liquidity-agent, 122 stars), Solana Sniper Bot (npc-live/clawfirm, 156 stars) and Solana Payments Wallets Trading (npc-live/clawfirm, 156 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Wallet Profiling?
agiprolabs (a GitHub user) maintains it in agiprolabs/claude-trading-skills, which has 410 GitHub stars. The repository holds 68 skills in this directory. The repository was last updated on September 3, 2026.
Source: agiprolabs/claude-trading-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.