Skylit / Your research building blocks

Agent Kits.

Your agent.
Your first working result.

Give your coding agent a starting point. Run an offline demo, see the output, then adapt a research workflow to the question you want to answer.

Get the starter prompt Explore the workflows

Internal pilot · GitHub repository access is currently required. Ask about access. A Skylit API key alone does not grant repository access.

YOUR FIRST OUTPUTOFFLINE DEMO
Fictional SPY gamma replay showing fixed strikes 100 and 105 over time, with gaps left unconnected.
Follow the strike. Keep the gaps.Actual output from the Kit’s node-tracker example. Synthetic data, zero API calls.

Copy. Paste. Start.

Let your agent
help you get going.

Use a coding agent that can run local commands. It can check your setup, run the node-tracker demo and help you choose what to build next.

Repository access, Git and Python 3.11+ are required. The first demo needs no Skylit key, model API key or Python packages. Your coding agent may have its own costs.

Guided setup (GitHub, repository access required, opens in a new tab)
Paste into your coding agent
Get me started with https://github.com/SkylitAI/skylit-agent-kit. In a repository-scoped session, read AGENTS.md and docs/start-here.md before running commands. Handle setup, run the offline node-tracker demo, and show me the chart. Use docs/capabilities.md to help me choose and build my next workflow. Keep private vaults out and never request credentials in chat. Use live calls only within my authorized budget; publish only with my authorization.

Four runnable workflows

Start with a useful question.

Each recipe defaults to fictional fixtures, no network and zero API credits. Read the report, change an input, then build on it.

01

Fixed-strike replay

Follow the same strikes through signed exposure changes. See the gaps instead of filling them in.

use-case node-tracker
02

Prices beside levels

Put dated OHLCV bars beside separately timed exposure levels. Keep their source times distinct.

use-case price-levels
03

Flow investigation

Inspect a returned trade sample alongside strike rollups for an explicit time window.

use-case flow-investigator
04

Volatility context

Review returned volatility fields with freshness and coverage notes.

use-case volatility-context
Commands and workflow guides (GitHub, repository access required, opens in a new tab)Explore Lab experiments

Under the hood

The pieces to
make it your own.

Agent Kits is built around the Skylit Agent Kit repository: maintained examples, shared helpers and agent setup guides. Run Python directly or have your agent help.

Browse the repository (GitHub, repository access required, opens in a new tab)
77 endpoint previews
Synthetic request and response examples across the public API. Explore contract shapes before connecting an account.
A bounded watchlist runner
Plan a GEX, VEX and recent-flow report first. Review requests and documented credits before an explicit live run.
Codex and Claude guides
Setup guidance for local workflows and optional MCP connections. Host certification is still pending.
Reports with context
Keep source timestamps, missing observations and coverage limits beside the result.

When you’re ready for live data

See the plan.
Then connect.

The offline examples are ready to explore. Live paths are implemented and tested with synthetic responses; authenticated service certification remains pending.

  1. 01

    Check your account access

    Skylit service access and API credits depend on your account. Open the Developer page.

  2. 02

    Review the request budget

    Start with a dry run. Live mode is explicit; the runner checks the plan against its request and credit caps. Direct MCP calls do not inherit those caps.

  3. 03

    Keep your key local

    Use the secure environment or hidden terminal prompt. Never paste credentials into agent chat.

Read the public API docs

Before you start

A few useful answers.

Do I need a Skylit subscription to try the demo?

The offline examples do not call Skylit services and need no Skylit account or API key. You do need access to the internal repository. Agent subscriptions may have their own costs.

Is this a hosted agent or an app inside Skylit?

It is a repository of local tools, examples and guides you can run and adapt. For Skylit’s in-app AI analyst, see Talon.

Can I use MCP with my agent?

Optional MCP setup is covered in the agent guides. Direct MCP calls have their own access and costs and do not inherit the local runner’s limits. Start with the public API documentation and the repository’s host-specific guide.

Where should I share a new workflow?

Start an experiment in Agent Lab. Reusable work can graduate into the maintained Kit through review. Code licensing does not include service access or third-party data rights.

Make the workflow your own

Start with a kit. Take it further.

Ask about pilot access Explore Agent Lab

Research and education. These workflows do not place trades.