Small context documents that give AI agents enough information to build their own skills.
A seedling is a tiny markdown file — a few API endpoints, a lookup table, a code snippet — just the stuff an agent can't figure out on its own. The agent reads it and grows the rest: working code, error handling, tests, everything.
Follows the Agent Skills spec.
You write this:
---
name: weather-fetcher
description: Get current weather and forecasts for any location using the free Open-Meteo API.
---
## API: Open-Meteo (free, no key)
Geocode: GET https://geocoding-api.open-meteo.com/v1/search?name={city}&count=1
Current: GET https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}¤t=temperature_2m,weather_code,wind_speed_10m
## Weather Codes (WMO)
0=Clear, 1-3=Cloudy, 45=Fog, 51-55=Drizzle, 61-65=Rain, 71-75=Snow, 95=ThunderstormThe agent grows it into a full working skill.
sprout search "get the weather" # find a seedling
sprout plant weather-fetcher # install it as a local skill
sprout plant weather-fetcher -g # install globally
sprout list # see what you've plantedSeedlings are installed as Agent Skills:
- Local:
./agents/skills/<name>/SKILL.md - Global:
~/.agents/skills/<name>/SKILL.md
Each seedling is a folder with a SKILL.md and optional references:
seedlings/
├── weather-fetcher/
│ └── SKILL.md
├── csv-to-chart/
│ └── SKILL.md
├── stock-price/
│ └── SKILL.md
└── qr-code-generator/
└── SKILL.md
A good seedling is as short as possible. Only include what an agent genuinely cannot infer:
| Include | Skip |
|---|---|
| API endpoints and params | Error handling patterns |
| Auth method (key, OAuth, none) | Code structure |
| Lookup tables and code mappings | File I/O boilerplate |
| Library name and install command | Logging and validation |
| Response format quirks | Test scaffolding |
- Create
seedlings/your-skill/SKILL.md - Add frontmatter with
name(must match folder name) anddescription - Write only what an agent can't infer
- Open a PR