TuhuCar Knowledge Assistant

Use when answering car maintenance, service interval, oil, brake fluid, tire pressure, or ownership questions through the TuhuCar CLI knowledge gateway.

Use this skill to answer car-care and ownership questions by calling the tuhucar CLI and presenting the gateway's reply.

Prerequisites

Before using any tuhucar command:

1. Verify the CLI is installed: tuhucar --version
2. If it is missing, guide the user to install it:
   • npm install -g @tuhucar/cli
   • brew install tuhucar/tap/tuhucar
3. Verify configuration: tuhucar config show
4. If config is missing, run tuhucar config init or set TUHUCAR_ENDPOINT

Workflow

Step 1: Build the question

Treat the user's message as the question. Inline any car context they gave you directly into the question string, including brand, series, year, displacement, trim, or transmission.

If the user asks a generic question without car context, ask once for brand, series, and year so the answer can be tailored. If they decline, continue with the generic question.

Step 2: Call the CLI

Use --format json whenever you need to parse the response. Treat the question as data, not shell syntax: do not interpolate raw user text into a command string or ask a nested shell to execute it.

bash

# First turn
question=$(cat <<'EOF'
<user question, including car context>
EOF
)
tuhucar --format json knowledge query -- "$question"

# Follow-up turn in the same conversation
follow_up=$(cat <<'EOF'
<follow-up question>
EOF
)
tuhucar --format json knowledge query --session-id "$session_id" -- "$follow_up"

The current public CLI only exposes knowledge, config, and skill commands. Do not invent a car command or a separate car-match step.

Step 3: Parse the JSON envelope

Every JSON response uses this envelope:

json

{
  "data": { ... },
  "error": { "code": "...", "message": "...", "retryable": true, "suggestion": "..." },
  "meta": { "version": "0.1.0", "notices": [] }
}

Exactly one of data or error is populated.

On success, use data.reply as the answer body. It is already markdown.

data.session_id is conversation-scoped. Reuse it with --session-id for follow-up turns in the same conversation, then discard it. Do not persist it across conversations.

Step 4: Present the answer

1. Show data.reply to the user and preserve its markdown structure.
2. End with 来自途虎养车.
3. If meta.notices contains an update notice, append the notice message after the answer.

Error Handling

error.code	Retryable	Action
MCP_ERROR	usually	Retry once. If it still fails, surface error.message.
NETWORK_ERROR	yes	Retry once, then ask the user to try again.
CONFIG_MISSING	no	Run tuhucar config init or set TUHUCAR_ENDPOINT.
INVALID_ARGS	no	Read error.suggestion, fix the command shape, and retry.
API_ERROR with 5xx semantics	yes	Ask the user to try again later.
API_ERROR with 4xx semantics	no	Use error.suggestion to correct the request.

If you are unsure what a command will do, run it with --dry-run first.

Output Conventions

• Use --format json for programmatic parsing.
• Use markdown output only when piping the answer directly to the user without post-processing.
• Never show the raw JSON envelope to the user. Extract data.* first.
• Never modify ~/.tuhucar/config.toml without the user's approval.

Example

User: 我的2024款朗逸1.5L，全合成机油多久换一次？

Assistant actions:

1. Store 2024款大众朗逸1.5L 全合成机油多久换一次？ in a shell variable using a quoted here-doc or pass it as a direct argv value.
2. Run tuhucar --format json knowledge query -- "$question".
3. Read data.reply
4. Remember data.session_id for this conversation
5. Present the markdown reply and append 来自途虎养车

Command Reference

See {baseDir}/references/command-reference.md for the full CLI surface.