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analyzing-on-chain-data

'Process perform on-chain analysis including whale tracking, token flows,

Install / Use

npx skills add jeremylongshore/tons-of-skills-marketplace --skill analyzing-on-chain-data

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

85/100

Category

Operations

Supported Platforms

Claude Code

Our assessment of analyzing-on-chain-data

analyzing-on-chain-data scores 85/100 on our quality scale, 387th of 548 Operations skills we index.

Its SKILL.md is 5.7 KB long, well organised into 11 sections with 3 code examples: a solid amount of guidance for an agent.

With 2,785 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
18/20
Description
12/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 6 days ago, so analyzing-on-chain-data is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

analyzing-on-chain-data compared with similar skills

All 4 of these similar skills score higher than analyzing-on-chain-data; compare them before choosing.

SkillScoreStarsUpdatedFormat
analyzing-on-chain-data (this skill)by jeremylongshore852.8k6d agoSKILL.md
algorithmic-artby anthropics100177.9k7d agoSKILL.md
pptxby anthropics100177.9k7d agoSKILL.md
designby nextlevelbuilder100130.2k9d agoSKILL.md
ui-ux-pro-maxby nextlevelbuilder100130.2k9d agoSKILL.md

Frequently asked questions

How do I install analyzing-on-chain-data?
Run npx skills add jeremylongshore/tons-of-skills-marketplace --skill analyzing-on-chain-data. The install tabs above show the steps for each supported agent.
Which AI agents does analyzing-on-chain-data work with?
It is written for Claude Code, as a SKILL.md file. Other agents that read the same format can often use it too.
Is analyzing-on-chain-data safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is analyzing-on-chain-data still maintained?
The repository was last updated 6 days ago, so analyzing-on-chain-data is actively maintained.

name: analyzing-on-chain-data description: 'Process perform on-chain analysis including whale tracking, token flows, and network activity.

Use when performing crypto analysis.

Trigger with phrases like "analyze crypto", "check blockchain", or "monitor market".

' allowed-tools: Read, Write, Edit, Grep, Glob, Bash(crypto:onchain-*) version: 1.28.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT tags:

  • crypto
  • monitoring
  • analyzing-on compatibility: Designed for Claude Code

Analyzing On-Chain Data

Overview

Analyze DeFi protocol metrics, chain-level TVL, fee revenue, DEX volumes, yield opportunities, and stablecoin market caps using DeFiLlama as the primary data source. Designed for DeFi researchers, protocol analysts, and yield farmers who need programmatic access to on-chain analytics without writing custom subgraph queries.

Prerequisites

  • Python 3.8+ with requests library installed
  • DeFiLlama API access (free, no key required for most endpoints)
  • Optional: CoinGecko API key for supplementary token price data
  • onchain_analytics.py CLI script available in the plugin directory
  • data_fetcher.py and metrics_calculator.py modules for programmatic usage

Instructions

  1. Run python onchain_analytics.py protocols to retrieve the top DeFi protocols ranked by total value locked (TVL).
  2. Filter protocol results by category using --category lending, --category dex, or --category "liquid staking" to narrow the scope.
  3. Filter by chain with --chain ethereum or --chain arbitrum to isolate chain-specific protocol data.
  4. Sort results by alternative metrics using --sort market_share or --sort tvl_to_mcap to surface undervalued protocols.
  5. Run python onchain_analytics.py chains to retrieve chain-level TVL rankings across all tracked networks.
  6. Run python onchain_analytics.py fees --protocol aave to pull fee and revenue data for a specific protocol.
  7. Run python onchain_analytics.py dex --chain ethereum to analyze DEX trading volumes filtered by chain.
  8. Run python onchain_analytics.py yields --min-tvl 5000000 --chain ethereum to identify yield opportunities above a minimum TVL threshold.
  9. Run python onchain_analytics.py trends --threshold 5 to detect protocols with significant TVL changes (threshold is percentage).
  10. Export results in JSON or CSV format using --format json or --format csv and redirect to file for downstream analysis.

See ${CLAUDE_SKILL_DIR}/references/implementation.md for the full four-step implementation workflow.

Output

  • Protocol rankings table with name, TVL, market share percentage, and TVL-to-market-cap ratio
  • Chain TVL rankings showing aggregate locked value per network
  • Fee and revenue reports per protocol with daily/weekly/monthly breakdowns
  • DEX volume tables with per-chain and per-DEX breakdowns
  • Yield opportunity listings filtered by minimum TVL and chain, including APY and pool details
  • Trending protocol alerts showing TVL percentage changes above the configured threshold
  • Stablecoin market cap summaries
  • JSON (output.json) or CSV (output.csv) export files for programmatic consumption

Error Handling

| Error | Cause | Solution | |-------|-------|----------| | Request timeout | DeFiLlama API slow or unreachable | Wait and retry; check for outages; use cached data if available | | Protocol not found: invalid-name | Protocol slug does not match DeFiLlama database | Run python onchain_analytics.py protocols to find the exact slug; slugs are case-sensitive | | No data returned for query | Filter too restrictive or data unavailable | Remove filters and retry; verify the category or chain exists; try a broader time range | | TVL data unavailable for some protocols | New protocols or data collection gaps | Check DeFiLlama directly; data typically appears within 24 hours of listing | | Data may be stale (last updated: X hours ago) | Local cache not refreshed | Clear cache with rm ~/.onchain_analytics_cache.json; use --verbose to check cache status | | Showing top 50 of 1000+ protocols | Output truncated for readability | Use --limit to increase count or --format json for full untruncated data | | UnicodeEncodeError | Terminal encoding mismatch | Use --format json for safe output or set LANG=en_US.UTF-8 |

Examples

Daily DeFi Overview

python onchain_analytics.py protocols --limit 20
python onchain_analytics.py chains
python onchain_analytics.py trends

Produces a snapshot of the top 20 protocols by TVL, all chain rankings, and any protocols trending above the default threshold.

Research a Lending Protocol

python onchain_analytics.py protocols --category lending --sort tvl_to_mcap
python onchain_analytics.py fees --protocol aave

Ranks all lending protocols by TVL-to-market-cap ratio (identifying potentially undervalued protocols), then pulls detailed fee and revenue data for Aave.

Find High-TVL Yield Opportunities on Ethereum

python onchain_analytics.py yields --min-tvl 10000000 --chain ethereum --limit 50  # 10000000 = 10M limit

Returns up to 50 yield pools on Ethereum with at least $10M in TVL, sorted by APY. Export with --format csv > yields.csv for spreadsheet analysis.

Resources

  • DeFiLlama API Documentation -- primary data source for TVL, fees, yields, and DEX volumes
  • DeFiLlama Status Page -- check API availability and outage reports
  • CoinGecko API -- supplementary token price and market cap data
  • Dune Analytics -- custom SQL queries against on-chain data for deeper analysis
  • The Graph -- decentralized indexing protocol for querying blockchain data via GraphQL

Related Skills

View on GitHub
GitHub Stars2.8k
CategoryOperations
Updated6d ago
Forks404

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions