setup

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name: setup description: One-time setup for the TinyML Agent Skill. Run this immediately after installing the plugin. Configures update mode (pinned vs auto-update), discovers SCRIPTS_DIR, verifies tinyml-tensorlab installation, sets up virtual environment, and saves all variables to .env. Must complete before using tinyml-workflow-agent. Always trigger when: user just installed the tinyml plugin, says "set up tinyml", "configure the tinyml skill", "first time setup", or encounters the message "Run /tinyml-agent-skills:setup first".

TinyML Agent Skill — Setup

Run this once after installing the plugin. Re-run any time you move or reinstall tinyml-tensorlab, or want to change your update mode.

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Step 1: Discover script paths

Two separate runner.py files exist — one for this setup skill, one for the main tinyml-workflow-agent skill. Find both:

Main skill scripts (SCRIPTS_DIR) — used for tinyml-tensorlab operations during this session only (not stored in .env):

find ~/.claude -name "runner.py" 2>/dev/null | grep "tinyml-workflow-agent" | head -1

Set SCRIPTS_DIR from result (strip /runner.py, keep the directory).

Setup skill scripts (SETUP_SCRIPTS_DIR) — used only during this setup:

find ~/.claude -name "runner.py" 2>/dev/null | grep "setup/scripts" | head -1

Set SETUP_SCRIPTS_DIR from result (strip /runner.py, keep the directory).

If either is not found, ask the user:

"Where is the tinyml-agent-skills plugin installed?"

Verify both runners exist:

ls "$SCRIPTS_DIR/runner.py"
ls "$SETUP_SCRIPTS_DIR/runner.py"

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Step 2: Choose update mode

Ask the user:

"How would you like to manage updates for this skill?

1. Pinned — stay on the current version, no automatic updates
2. Auto-update — check for newer versions at the start of each session"

NOTE: Current version can be found in plugins/tinyml-agent-skills/.claude-plugin/plugin.json,

Call the setup runner (not the main skill runner) with their choice:

# Pinned:
UPDATE_RESPONSE=$(python3 "$SETUP_SCRIPTS_DIR/runner.py" set_update_mode '{"mode": "pinned"}')

# Auto-update:
UPDATE_RESPONSE=$(python3 "$SETUP_SCRIPTS_DIR/runner.py" set_update_mode '{"mode": "auto"}')

Confirm success: true from UPDATE_RESPONSE before proceeding.

Extract and store for Step 7:

UPDATE_MODE=$(echo "$UPDATE_RESPONSE" | python3 -c "import sys, json; print(json.load(sys.stdin).get('mode'))")
UPDATE_PINNED_VERSION=$(echo "$UPDATE_RESPONSE" | python3 -c "import sys, json; print(json.load(sys.stdin).get('pinned_version') or '')")

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Step 3: Confirm tinyml-tensorlab path

Ask user: "What is the full path to your tinyml-tensorlab directory?" (e.g. /home/username/tinyml-tensorlab)

TINYML_BASE_PATH=<user-provided path>

python3 "$SETUP_SCRIPTS_DIR/runner.py" check_installation \
  "{\"tinyml_base_path\": \"$TINYML_BASE_PATH\"}"

If success: false: show errors and hint, ask user to correct path. Repeat until success: true.

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Step 4: Verify packages and virtual environment

First check if a venv exists:

ls "$TINYML_BASE_PATH/tinyml-modelmaker/venv/bin/activate" 2>/dev/null && echo "venv found" || echo "no venv"

• venv found — activate it: source "$TINYML_BASE_PATH/tinyml-modelmaker/venv/bin/activate"
• no venv — the user may have installed packages globally or via pyenv/conda. Do NOT force a venv. Proceed with the current Python environment.

Either way, verify all packages import correctly:

import tinyml_modelmaker
import tinyml_tinyverse
import tinyml_torchmodelopt
import tinyml_modelzoo

print(f"TI Tiny ML ModelMaker: {tinyml_modelmaker.__version__}")
print(f"TI Tiny ML Tinyverse: {tinyml_tinyverse.__version__}")
print(f"TI Tiny ML Model Optimization toolkit: {tinyml_torchmodelopt.__version__}")
print(f"TI Tiny ML Model Zoo: {tinyml_modelzoo.__version__}")

If imports succeed: skip to Step 5.

If imports fail: ask the user how they installed the packages — venv, pyenv, conda, or global pip. If user has not installed the requisite packages, follow references/setup_guide.md to set up the environment. Return here once done.

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Step 5: Run verification training (first-time only)

On re-runs: if ~/.tinyml-agent-skills/.env already exists with IS_REPO_SETUP=1, and the user is only updating update-mode or fixing paths (not re-verifying training), skip this step.

Tell the user: "Running a one-time verification to confirm training and compilation work correctly. This will take a few minutes."

Linux:

cd "$TINYML_BASE_PATH/tinyml-modelzoo"
./run_tinyml_modelzoo.sh examples/generic_timeseries_classification/config.yaml

Windows:

cd "$TINYML_BASE_PATH\tinyml-modelzoo"
run_tinyml_modelzoo.bat examples\generic_timeseries_classification\config.yaml

Stream output to user.

If compilation fails due to missing compiler path: search for the compiler automatically, set the required environment variables, and re-run. If still not found, refer user to: https://software-dl.ti.com/C2000/esd/mcu_ai/01_03_00/user_guide/installation/environment_variables.html#

If verification passes: proceed to Step 6.

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Step 6: Set docs path

TINYML_TENSORLAB_DOCS_PATH="$TINYML_BASE_PATH/docs/source/"

Validate it contains RST subdirectories:

ls "$TINYML_TENSORLAB_DOCS_PATH/getting_started/" 2>/dev/null | head -3

If no output or error: path is wrong. Correct value is $TINYML_BASE_PATH/docs/source/. Fix before continuing.

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Step 7: Write .env file

Config is written to ~/.tinyml-agent-skills/.env — user-global, survives plugin updates, reinstalls, and cache clears. Works on all platforms (Linux, macOS, Windows).

CRITICAL: Inform the user of the location:

"Configuration will be saved to: ~/.tinyml-agent-skills/.env" "This persists across all sessions and plugin versions."

Call the setup runner to write the file (cross-platform — no bash file operations):

python3 "$SETUP_SCRIPTS_DIR/runner.py" save_config \
  "{\"tinyml_base_path\": \"$TINYML_BASE_PATH\", \"docs_path\": \"$TINYML_TENSORLAB_DOCS_PATH\", \"update_mode\": \"$UPDATE_MODE\", \"pinned_version\": \"$UPDATE_PINNED_VERSION\"}"

Check success: true. On success, env_file in the response will show the exact path created.

Note: SCRIPTS_DIR is NOT stored in .env — it is derived automatically from the installed plugin location at the start of each session.

If success: false: show errors, do not proceed until resolved.

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Setup Complete

Tell the user:

"✓ Setup complete. You can now use /tinyml-agent-skills:tinyml-workflow-agent to start building TinyML models."

"Configuration saved to: ~/.tinyml-agent-skills/.env" "This file is loaded automatically by tinyml-workflow-agent on every session start and survives plugin updates." "If you move tinyml-tensorlab or change your setup, re-run this setup skill."

Re-run this setup skill any time you:

• Move or reinstall tinyml-tensorlab
• Switch to a different tinyml-tensorlab version
• Want to change pinned vs auto-update mode
• Encounter: "Run /tinyml-agent-skills:setup first" (means ~/.tinyml-agent-skills/.env is missing or incomplete)