ScreenLane
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@ScreenLaneExplain the error shown on my screen"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
ScreenLane
Talk to your screen.
Open-source screen-aware voice command layer for AI agents. Capture what’s on your screen, take an instruction, build an agent-ready command, and send it to tools like Tera, Codra, GateLane, Codex, OpenCode, MCP, clipboard, or stdout.
Voice alone is not the product. Voice + screen context is the product.
Install
npm install -g @talocode/screenlane
# or
npx @talocode/screenlane@latest demoRequires Node.js ≥ 18.
Related MCP server: markupR MCP Server
Quickstart
screenlane init
screenlane doctor
screenlane demo
screenlane command \
--text "Fix this error" \
--context-file ./error.txt \
--target codra \
--out prompt
screenlane capture --source text --text "Stack trace here..." --save
screenlane dictate --text "Explain this page" --out text
screenlane serve # local API: http://127.0.0.1:3070
screenlane mcp # MCP over stdioAuth & cloud
One key. One cloud base.
Key |
|
Cloud API |
|
export TALOCODE_API_KEY=your_key
# Optional: gate the local HTTP API
export SCREENLANE_REQUIRE_AUTH=true
# Then clients send: Authorization: Bearer <TALOCODE_API_KEY>screenlane auth set --key your_key
screenlane auth status
screenlane auth clearLocal capture / command / demo work without a key.
Cloud send (Tera, Codra, GateLane, …) uses
TALOCODE_API_KEY+https://api.talocode.site.
CLI
Command | Purpose |
| Create |
| Screen / file / text / url / clipboard context |
| Instruction ( |
| Build agent-ready prompt |
| Route to a target |
| Local HTTP API (port 3070) |
| MCP server (stdio) |
| Diagnostics |
| Deterministic demo |
| Manage |
SDK
import { ScreenLaneClient } from "@talocode/screenlane";
// Local (no server)
const local = new ScreenLaneClient();
const cmd = await local.createCommand({
text: "Fix this error",
contextText: "TypeError: ...",
target: "codra",
});
// Talk to local server
const api = new ScreenLaneClient({
baseUrl: "http://127.0.0.1:3070",
apiKey: process.env.TALOCODE_API_KEY,
});Local HTTP API
screenlane serve → http://127.0.0.1:3070
GET /healthGET /v1/screenlane/doctorPOST /v1/screenlane/capture|dictate|command|send|demoGET /v1/screenlane/contexts·commands
When SCREENLANE_REQUIRE_AUTH=true:
Authorization: Bearer <TALOCODE_API_KEY>MCP
screenlane mcpTools: screenlane_capture, screenlane_dictate, screenlane_command, screenlane_send, screenlane_doctor, screenlane_demo, screenlane_list_contexts, screenlane_list_commands
How it works
Screen context + instruction
↓
Agent command (deterministic templates in v0.1)
↓
Tera / Codra / Codex / OpenCode / GateLane / MCP / clipboard / stdoutCloud path uses TALOCODE_API_KEY against https://api.talocode.site.
Skills
skills/screen-aware-command/skills/screen-aware-debugging/skills/screen-aware-writing/
Python
pip install talocode-screenlane
screenlane-py --helpNotes
Prefer
--textfor dictate (no bundled live mic in v0.1).Screenshots use OS tools when available; OCR via
tesseractwhen installed (--no-ocrto skip).Text / file / url / clipboard always work.
Demo is text-mode voice simulation (deterministic).
More docs: docs/ · Demo:
screenlane demo
Talocode ecosystem
Part of Talocode. Sibling projects:
Project | What it is |
Screen-aware voice commands (this repo) | |
AI chat & assistant | |
Local coding agent | |
MCP gateway | |
Context ingestion | |
Agent memory | |
X growth intelligence | |
X reply intelligence | |
Crawler intelligence | |
Web extraction | |
Search for agents | |
Builder platform | |
Trading intelligence | |
Browser automation | |
Shared agent skills |
More: github.com/talocode · talocode.site · docs.talocode.site
License
MIT © Talocode
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