Use with AI Agents

Connect AI coding assistants to the Staking Rewards API using a ready-made skill file.

Give your AI coding assistant full knowledge of the Staking Rewards API by adding a single skill file. The agent will be able to query staking data, fetch ratings, and help you write integration code — no manual API docs reading required.

What is SKILL.md?

A SKILL.md file is a structured reference document that teaches AI agents how to use an API. It contains endpoints, authentication details, query patterns, and common pitfalls — everything the agent needs to make correct API calls on your behalf.

Supported Tools

The skill follows the Agent Skills open standard and works with any compatible agent:

Claude Code — native skill support
Claude.ai — enable via Settings → Capabilities (Pro/Max/Team/Enterprise)
Gemini CLI — native skill support
OpenAI Codex — native skill support
Cursor — add as project rules
Windsurf — add as project rules
GitHub Copilot — add as repository instructions

Setup

Get your API key

If you don't have one yet, get an API key.

Set it as an environment variable:

export STAKING_REWARDS_API_KEY=your_key_here

Install the skill

The fastest way to install is via skills.sh:

npx skills add stakingrewards/skills --skill staking-rewards-api

This automatically downloads and places the skill file for your AI tool.

Manual installation from GitHub

The skill includes SKILL.md and a references/ folder with additional context files. Clone the repository at github.com/stakingrewards/skills to get everything:

git clone https://github.com/stakingrewards/skills.git

Then follow the tool-specific steps below to place the files in the right location.

Configure your AI tool

If you used npx skills add, the skill is already installed.

For manual installation from the cloned repo:

# Install skill globally for Claude Code
mkdir -p ~/.claude/skills/staking-rewards-api
cp -r skills/ ~/.claude/skills/staking-rewards-api/

To scope it to a single project instead, install into the project's skills directory:

mkdir -p .claude/skills/staking-rewards-api
cp -r skills/ .claude/skills/staking-rewards-api/

If you used npx skills add, the skill is already installed.

For manual installation using the native Gemini CLI command:

gemini skills install https://github.com/stakingrewards/skills

Or copy from the cloned repo:

# Install globally
mkdir -p ~/.gemini/skills/staking-rewards-api
cp -r skills/ ~/.gemini/skills/staking-rewards-api/

# Or scope to a single project
mkdir -p .gemini/skills/staking-rewards-api
cp -r skills/ .gemini/skills/staking-rewards-api/

If you used npx skills add, the skill is already installed.

For manual installation using the native Codex command:

$skill-installer stakingrewards/skills

Or copy from the cloned repo:

# Install globally
mkdir -p ~/.agents/skills/staking-rewards-api
cp -r skills/ ~/.agents/skills/staking-rewards-api/

# Or scope to a single project
mkdir -p .agents/skills/staking-rewards-api
cp -r skills/ .agents/skills/staking-rewards-api/

Add the skill as a Cursor rule:

mkdir -p .cursor/rules
cp skills/SKILL.md .cursor/rules/staking-rewards-api.mdc

Alternatively, paste the contents of SKILL.md under Cursor Settings → Rules.

Add the skill as a Windsurf rule:

mkdir -p .windsurf/rules
cp skills/SKILL.md .windsurf/rules/staking-rewards-api.md

Alternatively, paste the contents of SKILL.md under Windsurf Settings → Rules.

Add as repository instructions:

mkdir -p .github
cp skills/SKILL.md .github/copilot-instructions.md

Start querying

Ask your AI assistant natural language questions:

"Get the current reward rate for Ethereum"
"Show me the top 10 staking providers by AUM"
"What are the best-rated DeFi protocols?"
"Fetch historical staking data for Cosmos over the last 30 days"
"Compare validator commission rates for Polkadot"

The agent will construct the correct API calls, handle authentication, and format the results.

What the Agent Can Do

With the skill file, your AI agent knows how to:

Query staking data — assets, providers, validators, reward options, and metrics via GraphQL
Fetch ratings — DeFi and infrastructure ratings via REST endpoints
Filter and sort — by metric values, date ranges, asset types, and more
Handle historical data — with date filters and interval aggregation
Write integration code — generate JavaScript, Python, or any language for your use case
Debug errors — recognizes common error codes and suggests fixes

Example Session

You: What's the current reward rate for ETH?

Agent: Let me query the Staking Rewards API for that.

The current ETH reward rate is 3.42%.

Data from Staking Rewards.

You: Show me the top 5 DeFi protocols by rating

Agent: Here are the top-rated DeFi protocols:

| Protocol          | Provider    | Rating | TVL      | APY   |
|-------------------|-------------|--------|----------|-------|
| Aave v3 WETH      | Aave        | A      | $5.99B   | 2.18% |
| Rocket Pool rETH  | Rocket Pool | BB     | $813M    | 2.13% |
| ...               | ...         | ...    | ...      | ...   |

Need help? Contact us at [email protected] or via Telegram @berlincrypto.

On this page