Build with AI

The Melange CLI gives AI agents a structured way to discover models, compare device benchmarks, manage repositories, upload models, and produce credential-safe deployment code. It works with Claude Code, Cursor, Codex, or any coding agent.

Install the agent skill

One line on macOS or Linux — installs the CLI and the skill together:

curl -fsSL https://raw.githubusercontent.com/zetic-ai/melange-cli/main/script/install.sh | sh

Restart your agent afterward so it can discover the skill. Re-run the same line any time to update both. To choose a version or agents:

curl -fsSL https://raw.githubusercontent.com/zetic-ai/melange-cli/main/script/install.sh \
  | sh -s -- --version v1.2.3 --agent "universal claude-code codex"

The installer uses npx skills when a recent Node is available, and copies the skill directly into the agent directories otherwise. Additional options: --cli-only / MELANGE_SKIP_SKILL, --skill-only / MELANGE_SKIP_CLI, --install-dir / MELANGE_INSTALL_DIR, --require-signature / MELANGE_REQUIRE_SIGNATURE.

Or install the skill separately after a CLI-only install (Homebrew, npm, Go, manual):

npx skills add zetic-ai/melange-cli --skill melange-cli \
  --agent universal claude-code --global --yes

To use another coding agent, let the installer show its interactive agent selector:

npx skills add zetic-ai/melange-cli --skill melange-cli --global

Restart the agent after installation. Update the installed skill after a CLI release:

# if you used the one-liner, just re-run it:
curl -fsSL https://raw.githubusercontent.com/zetic-ai/melange-cli/main/script/install.sh | sh
# or if you installed the skill separately:
npx skills update melange-cli --global

Homebrew, npm, Go, and manual installs provide the CLI only — add the skill separately. The curl | sh installer installs and updates both together.

MCP server alternative

melange mcp serves the same operations as MCP tools (18 over stdio, 17 over HTTP — upload_model is stdio-only). When the MCP server is already connected, prefer its tools over shelling out. See the MCP server page for transports, per-request auth, and client setup. Every rule below applies unchanged whether data came from CLI or MCP.

What’s supported

With the Melange skill or MCP server, your agent can:

Search the public model library and inspect available model versions.
Compare real device benchmarks, targets, and report availability.
Create and manage repositories, imports, uploads, and model versions.
Track a conversion through its phases and report each transition as it lands.
Generate exact deployment guides for Android, iOS, and Flutter.
Check authentication, usage, quotas, and plan-specific availability.

The agent only sees data and actions available to your Melange account.

Example usages

After authenticating with melange auth login, describe the outcome you want. You can copy these prompts as-is or add your own device, platform, and performance constraints.

Find a small language model in the Melange public library, compare its real
device benchmark results, and give me the Android Kotlin deployment guide in
auto mode. Do not import or modify a model.

Compare LFM2.5_350M with another small language model available in
Melange. Use only benchmark values returned by Melange. Show throughput and peak memory for iPhone 16 and Galaxy S25 where available.
Explain missing or plan-limited data, then recommend a model and target. Do not
import or modify anything.

I want to upload model.pt2 with sample.npy to ACCOUNT/REPO. First verify my
authentication, repository context, and upload quota. Show me the dry-run
manifest and explain any missing inputs. Upload the model, monitor conversion, and report
the final status without retrying implicitly.

Best practices

Use plan mode for multi-step work

For imports, uploads, downloads, repository changes, or other consequential work, ask the agent to make a plan first. Review the resolved target and side effects before approving execution.

Describe the outcome. Include your target device, platform, inference mode, and latency or memory constraints when they matter.
Start with read-only discovery. Ask the agent to inspect existing models and reports before importing or uploading anything.
Require evidence. Ask for real Melange results, clear labels for unavailable or plan-limited data, and no estimated metrics. Expect the full report in the reply — every quantization or device, like the model page in the dashboard — rather than a summary or a single device's numbers.
Expect conversion to run in the background. An import or upload returns as soon as the model is registered, then converts and benchmarks server-side. The agent should tell you which phase is running and what remains instead of blocking, and note that the model becomes downloadable once conversion finishes, while benchmarking continues.
Approve changes explicitly. Keep confirmation enabled for billable, destructive, or external-side-effect actions.
Protect credentials. Let the CLI handle your personal access token; never paste it into a prompt or request it in generated code.
Keep the skill current. Update the skill after CLI releases, then restart your agent before starting a new workflow.