Agent skill

On-Chain Data Analysis

by HKUDS in HKUDS/Vibe-Trading

Reads public blockchain data (active addresses, whale activity, TVL, DEX liquidity) and valuation metrics such as MVRV, NVT and SOPR to interpret conditions and generate signals.

MITAuto-check passedBusiness, Finance & HR

Install On-Chain Data Analysis

skills CLI
$ npx skills add HKUDS/Vibe-Trading --skill onchain-analysis -a claude-code

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

GitHub CLI
$ gh skill install HKUDS/Vibe-Trading onchain-analysis --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/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/onchain-analysis .claude/skills/onchain-analysis && 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
onchain-analysis
GitHub stars
35k
Token cost
~2.5k tokens
SKILL.md length
468 words
Files
1
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Reads public blockchain data (active addresses, whale activity, TVL, DEX liquidity) and valuation metrics such as MVRV, NVT and SOPR to interpret conditions and generate signals.

  • Works in 7 steps: Data-source limitations: on-chain data… → Entity identification is difficult: one… → UTXO vs account model: BTC (UTXO)… → …
  • Judging whether a rally is backed by growing network activity
  • SKILL.md covers Overview, Network Activity Metrics, Whale Tracking and DeFi Liquidity Analysis, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

The skill uses transparent blockchain data for crypto analysis in four areas: network activity, whale behavior, DeFi liquidity and on-chain valuation. A metrics table covers active addresses, new addresses, transaction count, transfer value and an activity-to-price ratio, each with a bullish and a bearish reading, and a short framework contrasts a bull market driven by real demand with one where price rises without matching activity.

Whale tracking defines holder tiers for BTC and ETH, lists behavior signals such as withdrawals from exchanges and a rising count of whale wallets, and sets large transfer thresholds to watch. A historical table gives reference ranges for BTC active addresses across market phases, and the stated scope also includes TVL, DEX liquidity and the MVRV, NVT and SOPR valuation metrics.

When your agent uses it

  • Judging whether a rally is backed by growing network activity
  • Tracking whale wallets and large exchange withdrawals
  • Reading MVRV, NVT or SOPR as valuation signals
  • Assessing DEX liquidity and TVL for a protocol

Example prompts

  • “Are active addresses confirming the current BTC price rise?”
  • “What do large whale withdrawals from exchanges suggest?”
  • “Explain how to read MVRV and SOPR together.”
  • “Set alert thresholds for large BTC and ETH transfers.”

Workflow steps

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

  1. Data-source limitations: on-chain data is most reliable for BTC and ETH; data quality for other chains is uneven
  2. Entity identification is difficult: one entity can control multiple addresses, so whale analysis contains error
  3. UTXO vs account model: BTC (UTXO) analysis methods cannot be directly applied to ETH (account model)
  4. Missing Layer-2 data: many transactions occur on L2s (Arbitrum / Optimism), so L1 data is incomplete
  5. On-chain data lag: block confirmation takes time, so this is not suitable for short-term trading decisions
  6. Data acquisition: the built-in OKX data source provides candles / trade data, but on-chain data requires extra APIs (Glassnode / Nansen)
  7. Metric desensitization: as market structure changes (ETFization / institutionalization), historical thresholds may need adjustment

What it can do on your machine

Read from SKILL.md and the folder at commit e532650. 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

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    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

On-Chain Data Analysis loads about 2.5k tokens when it runs. Until then it costs about 49 tokens; SKILL.md has 468 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/Vibe-Trading at commit e532650, republished under its MIT licence (© HKUDS). 468 words, ~2,452 tokens.

Download SKILL.mdSave it as .claude/skills/onchain-analysis/SKILL.md (or your agent's skills folder).
name
onchain-analysis
description
On-chain data analysis — active addresses / whale tracking / TVL / DEX liquidity, interpretation and signal generation using on-chain valuation metrics such as MVRV / NVT / SOPR.
category
crypto

On-Chain Data Analysis

Overview

Use transparent public blockchain data for analysis, covering network activity, whale-behavior tracking, DeFi liquidity analysis, and on-chain valuation metrics. This provides crypto investing with data dimensions unavailable in traditional finance.

Network Activity Metrics

Core Activity Indicators
MetricMeaningBullish SignalBearish Signal
Active addressesUnique addresses interacting with the network each daySustained increase (network adoption rising)Sustained decline
New addressesAddresses appearing for the first time each dayAccelerating growth (new users entering)Shrinkage
Transaction countNumber of on-chain transactions per daySteady growthSharp decline
Transfer valueTotal value transferred on-chain per dayLarge-value transfer activityInactive
Activity / price ratioGrowth in active addresses vs growth in priceActivity growth outpaces price growthPrice rises but activity does not
Activity Analysis Framework
Healthy bull market:
  Price↑ + active addresses↑ + new addresses↑ = driven by real demand

Bubble signal:
  Price↑ + active addresses↓ or flat = capital-driven, not user-driven

Bottoming signal:
  Price↓ + active addresses bottom out and stabilize = speculators exit, real users remain
Historical Reference for BTC Active Addresses
PhaseActive Addresses (daily avg)BTC PriceMeaning
2020 bear-to-bull transition0.8-1.0 million$10k-$20kBottom stabilization
2021 bull market1.0-1.3 million$30k-$69kHealthy growth
2022 bear market0.8-0.9 million$16k-$30kPulled back but did not collapse
2024 bull market0.9-1.2 million$40k-$100kInstitution-driven

Whale Tracking

Whale Definition
TierBTC HoldingsETH HoldingsEstimated Count
Super whales>10,000 BTC>100,000 ETH~100
Large whales1,000-10,000 BTC10,000-100,000 ETH~2,000
Mid whales100-1,000 BTC1,000-10,000 ETH~15,000
Small whales10-100 BTC100-1,000 ETH~150,000
Whale Behavior Signals
Bullish signals:
1. Whales withdraw from exchanges -> intent to hold long term
2. Whale-wallet count rises -> institutions / large holders accumulating
3. Exchange BTC balance keeps falling -> lower available supply
4. Long-term holder (LTH) balances rise -> smart money buying

Bearish signals:
1. Large whale transfers into exchanges -> preparing to sell
2. Dormant addresses wake up and transfer -> early holders taking profits
3. Exchange balances surge -> sell pressure is about to be released
4. Miner balances fall and move to exchanges -> miner capitulation / profit taking
Large Transfer Monitoring
Thresholds to watch:
- BTC: single transfer > 500 BTC (about $50M)
- ETH: single transfer > 10,000 ETH (about $30M)
- USDT: single transfer > $50M

Transfer-direction analysis:
- Wallet -> exchange: potential selling (bearish)
- Exchange -> wallet: withdrawal to hold (bullish)
- Exchange -> exchange: arbitrage transfer (neutral)
- Wallet -> wallet: OTC trade (watch follow-up behavior)

DeFi Liquidity Analysis

TVL (Total Value Locked)
TVL = total value of assets locked in DeFi protocols

TVL analysis dimensions:
1. Total TVL trend: rising = capital flowing into the DeFi ecosystem
2. Cross-chain TVL: market-share changes among ETH vs Solana vs Arbitrum
3. Protocol TVL ranking: leading protocols such as Aave / Lido / Maker
4. TVL / market cap ratio: similar to asset-utilization rate in traditional finance
DEX Liquidity Metrics
MetricMeaningFocus
DEX volumeDaily decentralized-exchange volumeTrend in DEX / CEX ratio
Liquidity depthSize of AMM liquidity poolsBigger pools = lower slippage = better
LP yieldAnnualized return for liquidity providersAbnormally high = unsustainable
Impermanent lossOpportunity cost for LPsMore severe when volatility is higher
Stablecoin Liquidity
Stablecoin inflows = "dry powder" for the crypto market

Metrics to watch:
1. Changes in total USDT / USDC supply
2. Stablecoin balances on exchanges
3. Stablecoin mint / burn activity (Tether / Circle)
4. Stablecoin market-cap share (lower = higher risk appetite)

Signals:
- Heavy stablecoin minting -> capital preparing to enter
- Exchange stablecoin balances up -> buying power is accumulating
- Stablecoin share rising quickly -> risk-off market (capital rotating from coins into stablecoins)
Show full SKILL.md (187 more words)Show less

On-Chain Valuation Metrics

MVRV (Market Value to Realized Value)
MVRV = market cap / realized cap

Realized cap = Σ(each UTXO × price at last movement)
             = sum of all holders' cost basis

| MVRV | Meaning | Historical Signal |
|------|------|---------|
| > 3.5 | Severely overvalued, large unrealized profits across holders | Historical top zone |
| 2.0-3.5 | Richly valued, most holders in profit | Mid-to-late bull market |
| 1.0-2.0 | Reasonable range | Normal market or early bull market |
| < 1.0 | Undervalued, most holders underwater | Bear-market bottom zone |

Signal logic: MVRV > 3.5 -> most holders have large unrealized gains -> strong incentive to sell -> potential top
              MVRV < 1.0 -> most holders are losing money -> unwilling to sell -> potential bottom
NVT (Network Value to Transactions)
NVT = market cap / daily on-chain transfer value

Similar to a PE ratio in traditional finance:
- High NVT: market cap is high relative to on-chain activity (overvalued or optimistic on future growth)
- Low NVT: market cap is low relative to on-chain activity (undervalued or value-like)

NVT Signal (improved):
NVT_Signal = market cap / MA(90, daily on-chain transfer value)
Use a 90-day moving average to smooth noise

| NVT Signal | Meaning |
|-----------|------|
| > 150 | Severely overvalued |
| 65-150 | Normal range |
| < 65 | Undervalued |
SOPR (Spent Output Profit Ratio)
SOPR = realized value of spent outputs / creation value of spent outputs

Simply put: on average, are the coins sold today being sold at a profit or a loss?

| SOPR | Meaning | Signal |
|------|------|------|
| > 1.05 | Sellers are realizing 5%+ profit on average | Profit-taking pressure |
| 1.0-1.05 | Small-profit selling | Normal |
| = 1.0 | Break-even | Key support / resistance |
| < 1.0 | Selling at a loss | Panic selling (bottom signal) |

In bull markets: a pullback to SOPR = 1.0 is a buy opportunity (cost-basis support)
In bear markets: a rebound to SOPR = 1.0 is a sell opportunity (cost-basis resistance)
Other On-Chain Valuation Metrics
MetricFormulaPurpose
Puell MultipleDaily miner revenue / MA(365, daily miner revenue)Miner-income cycle
Stock-to-Flowstock / annual productionBTC scarcity model
Reserve RiskHODL Bank / priceHolder confidence
Exchange balancetotal BTC held on exchangesSupply-side pressure

Analysis Framework

Composite On-Chain Score
Score each indicator from 1 to 5 and combine with weights:

| Dimension | Weight | Indicators |
|------|------|------|
| Valuation | 30% | MVRV, NVT |
| Activity | 25% | active addresses, new addresses |
| Capital flow | 25% | exchange balances, stablecoins |
| Whale behavior | 20% | whale holdings, large transfers |

Total score > 4.0: strongly bullish
Total score 3.0-4.0: bullish bias
Total score 2.0-3.0: neutral
Total score < 2.0: bearish / leaning bearish

Output Format

markdown
## On-Chain Analysis Report: BTC

### On-Chain Snapshot
| Metric | Current Value | Historical Percentile | Signal |
|------|--------|---------|------|
| MVRV | 2.1 | 65% | Elevated but not at a top |
| NVT Signal | 85 | 50% | Fair |
| SOPR(7d avg) | 1.02 | 55% | Slight profit-taking state |
| Active addresses | 950k/day | 45% | Relatively low |
| Exchange balance | 2.30M BTC | 30% | Low level (bullish) |

### Whale Activity
- Last 7 days: net withdrawal of +15,000 BTC (bullish)
- Large transfers: 3 transfers >1000 BTC into cold wallets
- Miners: holdings stable, no major outflows observed

### Composite Score: 3.5/5 (bullish bias)

### Conclusion
On-chain data is broadly bullish. MVRV is not yet in an extreme zone, exchange balances are low,
and whales continue to accumulate. But active addresses remain soft, so monitor whether this is an
"institution-driven bull market without retail participation". Maintain long exposure, but keep
position sizing controlled and avoid leverage.

Notes

  1. Data-source limitations: on-chain data is most reliable for BTC and ETH; data quality for other chains is uneven
  2. Entity identification is difficult: one entity can control multiple addresses, so whale analysis contains error
  3. UTXO vs account model: BTC (UTXO) analysis methods cannot be directly applied to ETH (account model)
  4. Missing Layer-2 data: many transactions occur on L2s (Arbitrum / Optimism), so L1 data is incomplete
  5. On-chain data lag: block confirmation takes time, so this is not suitable for short-term trading decisions
  6. Data acquisition: the built-in OKX data source provides candles / trade data, but on-chain data requires extra APIs (Glassnode / Nansen)
  7. Metric desensitization: as market structure changes (ETFization / institutionalization), historical thresholds may need adjustment

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in agent/src/skills/onchain-analysis of HKUDS/Vibe-Trading.

Open the folder on GitHubat commit e532650

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Questions about On-Chain Data Analysis

What does On-Chain Data Analysis do?

Reads public blockchain data (active addresses, whale activity, TVL, DEX liquidity) and valuation metrics such as MVRV, NVT and SOPR to interpret conditions and generate signals. The skill uses transparent blockchain data for crypto analysis in four areas: network activity, whale behavior, DeFi liquidity and on-chain valuation. A metrics table covers active addresses, new addresses, transaction count, transfer value and an activity-to-price ratio, each with a bullish and a bearish reading, and a short framework contrasts a bull market driven by real demand with one where price rises without matching activity.

When should I use On-Chain Data Analysis?

On-Chain Data Analysis fits situations like: judging whether a rally is backed by growing network activity; tracking whale wallets and large exchange withdrawals; reading MVRV, NVT or SOPR as valuation signals; assessing DEX liquidity and TVL for a protocol.

How do I install On-Chain Data Analysis in Claude Code?

Run `npx skills add HKUDS/Vibe-Trading --skill onchain-analysis -a claude-code`. Or copy the skill folder (agent/src/skills/onchain-analysis in HKUDS/Vibe-Trading) into .claude/skills/onchain-analysis in your project. Claude Code loads it when a task matches its description.

How do I install On-Chain Data Analysis in Codex?

Run `npx skills add HKUDS/Vibe-Trading --skill onchain-analysis -a codex`. Or copy the skill folder (agent/src/skills/onchain-analysis in HKUDS/Vibe-Trading) into .agents/skills/onchain-analysis in your project. Codex loads it when a task matches its description.

Can I use On-Chain Data Analysis 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 HKUDS/Vibe-Trading --skill onchain-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/onchain-analysis, .gemini/skills/onchain-analysis, .github/skills/onchain-analysis and .opencode/skills/onchain-analysis in your project.

What does On-Chain Data Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: On-Chain Data Analysis is instructions for the agent only.

Does On-Chain Data Analysis 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 On-Chain Data Analysis 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. Review the folder before installing.

What licence does On-Chain Data Analysis use?

On-Chain Data Analysis 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 On-Chain Data Analysis use?

About 2.5k tokens (SKILL.md is roughly 9.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to On-Chain Data Analysis?

Skills that share tags, products or a category with On-Chain Data Analysis: Surf Crypto Data API (BlockRunAI/blockrun-mcp, 391 stars), Web3 Data Explorer (internet-court/internet-court-skill, 6.5k stars), Technical Analyst (tradermonty/claude-trading-skills, 3k stars) and Predexon Prediction Market Data (BlockRunAI/ClawRouter, 6.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains On-Chain Data Analysis?

HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 35,043 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 8, 2026.

Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.