Agent skill

Whale Tracking

by agiprolabs in agiprolabs/claude-trading-skills

Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens

MITAuto-check passedBackend & APIs

Install Whale Tracking

skills CLI
$ npx skills add agiprolabs/claude-trading-skills --skill whale-tracking -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install agiprolabs/claude-trading-skills whale-tracking --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/agiprolabs/claude-trading-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/whale-tracking .claude/skills/whale-tracking && rm -rf skills-src

Use ~/.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/

Facts

Skill name
whale-tracking
GitHub stars
410
Token cost
~3k tokens
SKILL.md length
1,109 words
Files
5 (incl. scripts, references)
Skills in repo
68
Repo updated
First seen
Licence
MIT

At a glance

Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens

  • Works in 5 steps: Top holders per token: Query… → Large transaction monitoring: Watch for… → Profitable trader rankings: Use… → …
  • Backend & APIs work in your project
  • SKILL.md covers Why Whale Tracking Matters, What Constitutes a Whale, Accumulation vs Distribution… and Whale Watchlist Management, plus 6 more sections
  • Runs Python scripts from its folder

What it does

Whale Tracking is an agent skill from agiprolabs/claude-trading-skills. Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens

Its SKILL.md is about 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/data_sources.md`, `references/detection_methods.md` and `scripts/track_whales.py`).

It sits in Backend & APIs. 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

  • Backend & APIs work in your project

Example prompts

  • “/whale-tracking”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Top holders per token: Query getTokenLargestAccounts for any token of interest
  2. Large transaction monitoring: Watch for trades > 100 SOL on key tokens
  3. Profitable trader rankings: Use SolanaTracker or Birdeye top trader endpoints
  4. Known fund wallets: Public wallet addresses of crypto funds and DAOs
  5. Cross-referencing: Wallets that appear in top holders of multiple successful tokens

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.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Whale Tracking loads about 3k tokens when it runs, and up to ~6.8k if it reads all its reference files. Until then it costs about 33 tokens; SKILL.md has 1,109 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~6.8k

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). 1,109 words, ~2,983 tokens.

Download SKILL.mdSave it as .claude/skills/whale-tracking/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
whale-tracking
description
Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens

Whale Tracking for Solana Tokens

Whale tracking monitors the on-chain behavior of large wallets to detect accumulation, distribution, and smart money movements before they become visible in price action. On Solana, where token ownership is highly concentrated and whale transactions can move markets instantly, tracking large holders is one of the highest-signal alpha sources available.

Why Whale Tracking Matters

A single large wallet selling 5% of a token's supply can crash the price 30-50% on thin Solana DEX liquidity. Conversely, a known profitable wallet accumulating a new token often precedes major price runs. Whale tracking converts on-chain transparency into actionable intelligence.

Key use cases:

  • Early warning: Detect large holder sells before the price impact fully propagates
  • Smart money following: Identify wallets with strong track records and monitor their new positions
  • Accumulation detection: Spot gradual buying by whales who split orders to avoid detection
  • Distribution detection: Catch insiders or early investors offloading positions
  • Risk assessment: Evaluate token concentration risk before entering a position

What Constitutes a Whale

Whale classification depends on context. A wallet holding $50K of a $1M market cap token is a whale; the same $50K in SOL is not. Use relative and absolute thresholds:

Absolute Thresholds
CategoryTrade SizePortfolio SizeTypical Behavior
Retail< 10 SOL< 100 SOLReactive, follows trends
Mid-size10-100 SOL100-1,000 SOLMixed strategies
Whale100-1,000 SOL1,000-10,000 SOLInformed, moves markets
Mega-whale> 1,000 SOL> 10,000 SOLMarket makers, funds, insiders
Relative Thresholds (Per Token)
MetricThresholdSignificance
% of supply held> 2%Significant holder
% of daily volume> 5% single tradeMarket-moving transaction
Top N holdersTop 20Core holder group
Concentration ratioTop 10 hold > 50%High concentration risk

Use both absolute and relative metrics. A 50 SOL trade in a $200K market cap token is whale-level; the same trade in a $50M token is retail.

Accumulation vs Distribution Patterns

Accumulation Signals

Accumulation is when a whale builds a position over time, often trying to minimize price impact.

DCA pattern (Dollar-Cost Averaging):

  • Multiple buys of similar size over hours or days
  • Buys at regular intervals (e.g., every 30 minutes)
  • Position grows steadily without large single transactions

Dip buying:

  • Buys concentrated during price dips
  • Larger-than-usual purchases when price drops > 10%
  • Position increases during periods of general selling

Multi-wallet accumulation:

  • New wallets funded from the same source
  • Each wallet buys small amounts of the same token
  • Positions later consolidated into a primary wallet

Detection heuristics:

accumulation_score = 0
if buy_count > sell_count * 2:       accumulation_score += 2
if avg_buy_size > avg_sell_size:     accumulation_score += 1
if position_change_7d > 0:          accumulation_score += 1
if buys_during_dips > buys_on_pump:  accumulation_score += 2
if dca_pattern_detected:             accumulation_score += 2
# Score >= 4 = likely accumulating
Distribution Signals

Distribution is when a whale reduces or exits a position, often gradually to avoid crashing the price.

Gradual selling:

  • Multiple sells over days, each < 5% of position
  • Sells increase in frequency over time
  • Position shrinks steadily

Transfer to exchange:

  • Tokens transferred to known exchange deposit addresses
  • Large transfers to Binance, OKX, Bybit hot wallets
  • Often precedes selling by hours or days

Rapid exit:

  • Single large market sell (> 20% of position)
  • Often triggers cascading liquidations
  • Visible as large red candles with high volume

Detection heuristics:

distribution_score = 0
if sell_count > buy_count * 2:        distribution_score += 2
if position_change_7d < 0:           distribution_score += 1
if transfers_to_exchanges > 0:       distribution_score += 3
if sell_frequency_increasing:        distribution_score += 2
if position_pct_remaining < 50:      distribution_score += 1
# Score >= 4 = likely distributing

Whale Watchlist Management

Maintain a watchlist of wallets worth tracking. Sources for discovering whale wallets:

Discovery Methods
  1. Top holders per token: Query getTokenLargestAccounts for any token of interest
  2. Large transaction monitoring: Watch for trades > 100 SOL on key tokens
  3. Profitable trader rankings: Use SolanaTracker or Birdeye top trader endpoints
  4. Known fund wallets: Public wallet addresses of crypto funds and DAOs
  5. Cross-referencing: Wallets that appear in top holders of multiple successful tokens
Watchlist Structure

Each watchlist entry should track:

python
whale_entry = {
    "address": "WhaLe...",
    "label": "Smart money #47",        # Human-readable label
    "discovered": "2026-01-15",         # When added to watchlist
    "discovery_reason": "top_trader",   # How they were found
    "win_rate": 0.72,                   # Historical trade win rate
    "avg_pnl": 3.4,                     # Average PnL multiplier
    "tokens_tracked": 12,               # Number of tokens held
    "last_active": "2026-03-09",        # Last on-chain activity
    "tags": ["dex_trader", "sniper"],   # Classification tags
}
Wallet Classification Tags
TagDescription
sniperBuys tokens within minutes of launch
dex_traderPrimarily trades on DEXes
accumulatorBuilds positions gradually
flipperShort hold times, quick profit-taking
insiderConnected to token teams (funded by deployer)
fundInstitutional or fund wallet
market_makerProvides liquidity, two-sided flow

Cross-Token Analysis

Whale tracking becomes most powerful when you analyze whale behavior across multiple tokens simultaneously.

Convergence Signals

When multiple tracked whales buy the same token independently, the signal strength compounds:

Whale CountSignalConfidence
1 whale buyingInformationalLow
2-3 whales buyingNotableMedium
4+ whales buyingStrong convergenceHigh
Whales + volume spikeConfirmed momentumVery high
Show full SKILL.md (438 more words)Show less
Cross-Token Flow Analysis

Track where whale capital flows:

Token A (selling) → SOL → Token B (buying)

If multiple whales rotate from A to B:
  - Bearish for Token A (smart money exiting)
  - Bullish for Token B (smart money entering)

Data Sources

Whale tracking on Solana uses multiple data sources. See references/data_sources.md for complete details.

SourceUse CaseAuth Required
HeliusTransaction history, webhooksYes (free tier available)
SolanaTrackerTop traders, wallet PnLYes
BirdeyeToken holder rankingsYes (free tier available)
Solana RPCToken accounts, signaturesNo (public endpoints)
Helius Webhooks for Real-Time Tracking

Helius webhooks enable real-time whale alerts without polling:

python
# Webhook configuration for whale wallet monitoring
webhook_config = {
    "webhookURL": "https://your-server.com/whale-alerts",
    "transactionTypes": ["SWAP", "TRANSFER"],
    "accountAddresses": [
        "WhaLe1...",  # Tracked whale wallets
        "WhaLe2...",
    ],
    "webhookType": "enhanced",  # Parsed transaction data
}

Signal Generation

Convert whale activity into trading signals. Whale signals are one input to a broader decision framework, not standalone trading triggers.

Signal Types

Whale Buy Signal:

  • Whale buys > 100 SOL of a token
  • Confidence increases with: whale track record, buy size, number of whales buying
  • Weaken signal if: token is very new (< 24h), whale is known flipper, high concentration risk

Whale Sell Signal:

  • Whale sells > 25% of position or > 100 SOL
  • Confidence increases with: multiple whales selling, transfers to exchanges, increasing sell frequency
  • Weaken signal if: whale is taking partial profit after large gain, whale is known rebalancer

Accumulation Signal:

  • Whale accumulation score >= 4 (see scoring above)
  • Strongest when: multiple whales accumulating same token, accumulation during price downtrend
  • Time horizon: days to weeks (accumulation is a slow signal)

Distribution Signal:

  • Whale distribution score >= 4
  • Strongest when: team/insider wallets distributing, post-unlock distribution, increasing sell pace
  • Time horizon: hours to days (distribution can accelerate quickly)
Signal Scoring
python
def compute_whale_signal(whale_activity: dict) -> dict:
    """Combine whale activity indicators into a composite signal."""
    score = 0.0

    # Trade direction: +1 for buy, -1 for sell
    direction = 1 if whale_activity["is_buy"] else -1

    # Size factor: larger trades = stronger signal
    size_sol = whale_activity["size_sol"]
    if size_sol > 500:
        score += direction * 3
    elif size_sol > 100:
        score += direction * 2
    else:
        score += direction * 1

    # Whale quality: better track record = stronger signal
    win_rate = whale_activity["whale_win_rate"]
    score *= (0.5 + win_rate)  # 0.5x to 1.5x multiplier

    # Convergence: multiple whales = stronger signal
    whale_count = whale_activity["concurrent_whale_count"]
    score *= (1 + 0.3 * (whale_count - 1))

    return {
        "score": round(score, 2),
        "direction": "bullish" if score > 0 else "bearish",
        "confidence": "high" if abs(score) > 5 else "medium" if abs(score) > 2 else "low",
    }

Integration with Other Skills

Whale tracking works best when combined with other analysis:

SkillIntegration
token-holder-analysisIdentify concentration risk, insider wallets
liquidity-analysisEstimate price impact of whale trades
solana-onchainWallet profiling, transaction history
slippage-modelingPredict slippage for whale-sized trades
risk-managementFactor whale concentration into position sizing
helius-apiTransaction fetching, webhook setup

Files

References
  • references/detection_methods.md — Accumulation/distribution detection algorithms, whale classification, alert thresholds and scoring systems
  • references/data_sources.md — Complete guide to Helius, SolanaTracker, Birdeye, and on-chain data sources for whale tracking
Scripts
  • scripts/track_whales.py — Fetches top holders for a token, classifies whale activity as accumulating/distributing/holding, prints a whale report. Run with --demo for synthetic data mode.
  • scripts/whale_alerts.py — Monitors a watchlist of whale wallets for new large transactions, classifies trades, and prints alerts. Run with --demo for simulated whale trades.

Limitations and Caveats

  • Privacy wallets: Whales using multiple wallets or mixers can evade tracking
  • Misleading signals: Whales may intentionally create visible accumulation to attract followers, then dump
  • Latency: By the time you detect a whale buy, the price impact may already be priced in
  • False positives: Internal transfers between a whale's own wallets look like buys/sells
  • Exchange wallets: Centralized exchange hot wallets show massive flows that are not individual whale activity
  • Not financial advice: Whale activity is informational input for analysis, not a standalone trading recommendation

© 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/whale-tracking of agiprolabs/claude-trading-skills.

  • SKILL.md
  • references/data_sources.md
  • references/detection_methods.md
  • scripts/track_whales.py
  • scripts/whale_alerts.py

Open the folder on GitHubat commit 981e1d7

Compare with similar skills

Whale Tracking 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.

Whale Tracking compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Whale Tracking this skillagiprolabs/claude-trading-skills410—~3kAutomated safety check: PassMIT
Solana Devsolana-foundation/solana-dev-skill573—~3.8kAutomated safety check: PassMIT
RadarAuditware/radar154—~2.1kAutomated safety check: PassGPL-3.0
Safe Solana BuilderFrankcastleauditor/safe-solana-builder145—~3.6kAutomated safety check: PassNone
Wiremock TestOpenZeppelin/openzeppelin-relayer154—~1.6kAutomated safety check: NotesAGPL-3.0
Solana Token Extensionssolanabr/ai-kit109—~3.5kAutomated safety check: PassMIT

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Works with

Categories

Questions about Whale Tracking

What does Whale Tracking do?

Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens. Whale Tracking is an agent skill from agiprolabs/claude-trading-skills.

When should I use Whale Tracking?

Whale Tracking fits situations like: backend & APIs work in your project.

How do I install Whale Tracking in Claude Code?

Run `npx skills add agiprolabs/claude-trading-skills --skill whale-tracking -a claude-code`. Or copy the skill folder (skills/whale-tracking in agiprolabs/claude-trading-skills) into .claude/skills/whale-tracking in your project. Claude Code loads it when a task matches its description.

How do I install Whale Tracking in Codex?

Run `npx skills add agiprolabs/claude-trading-skills --skill whale-tracking -a codex`. Or copy the skill folder (skills/whale-tracking in agiprolabs/claude-trading-skills) into .agents/skills/whale-tracking in your project. Codex loads it when a task matches its description.

Can I use Whale Tracking 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 whale-tracking -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/whale-tracking, .gemini/skills/whale-tracking, .github/skills/whale-tracking and .opencode/skills/whale-tracking in your project.

What does Whale Tracking need to run?

Going by SKILL.md and its folder, Whale Tracking needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Whale Tracking access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Whale Tracking 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 Whale Tracking use?

Whale Tracking 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 Whale Tracking use?

About 3k tokens (SKILL.md is roughly 12k 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 3.8k tokens, read only when the agent opens those files.

What are the alternatives to Whale Tracking?

Skills that share tags, products or a category with Whale Tracking: Solana Dev (solana-foundation/solana-dev-skill, 573 stars), Radar (Auditware/radar, 154 stars), Safe Solana Builder (Frankcastleauditor/safe-solana-builder, 145 stars) and Wiremock Test (OpenZeppelin/openzeppelin-relayer, 154 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Whale Tracking?

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.