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Arthur onboarding sub-skill — Step 3: Set up an Arthur Task (create or select). Reads/writes .arthur-engine.env.
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Basado en la clasificación ocupacional SOC
| name | arthur-onboard-task |
| description | Arthur onboarding sub-skill — Step 3: Set up an Arthur Task (create or select). Reads/writes .arthur-engine.env. |
Goal: Establish ARTHUR_TASK_ID in .arthur-engine.env.
cat .arthur-engine.env 2>/dev/null || echo "(no state file)"
Parse ARTHUR_ENGINE_URL, ARTHUR_API_KEY, and ARTHUR_TASK_ID from the output.
State write helper:
STATE_FILE=".arthur-engine.env"
grep -v '^ARTHUR_TASK_ID=' "$STATE_FILE" 2>/dev/null > /tmp/ae_env_tmp && mv /tmp/ae_env_tmp "$STATE_FILE" || true
echo "ARTHUR_TASK_ID=$TASK_ID" >> "$STATE_FILE"
Confirm reuse with user and exit this skill (task step done).
List existing tasks:
curl -s \
-H "Authorization: Bearer $ARTHUR_API_KEY" \
"$ARTHUR_ENGINE_URL/api/v2/tasks" | \
python3 -c "
import sys, json
d = json.load(sys.stdin)
tasks = d.get('tasks') or d.get('data') or []
tasks = [t for t in tasks if not t.get('is_system_task')]
for t in tasks:
print(f' {t[\"id\"]}: {t[\"name\"]}')
"
Show the list and ask: "Select an existing task, or create a new one?"
To create a task:
TASK_ID=$(curl -s -X POST \
-H "Authorization: Bearer $ARTHUR_API_KEY" \
-H "Content-Type: application/json" \
-d "{\"name\": \"$TASK_NAME\"}" \
"$ARTHUR_ENGINE_URL/api/v2/tasks" | \
python3 -c "import sys,json; print(json.load(sys.stdin).get('id',''))")
echo "TASK_ID=$TASK_ID"
Save the task ID to the state file.
Arthur onboarding sub-skill — Step 4: Analyze the target repository (language, framework, existing instrumentation). Writes detection results to .arthur-engine.env.
Arthur onboarding sub-skill — Step 8: Configure an LLM provider for continuous evals (OpenAI, Anthropic, Gemini, Bedrock, or Vertex AI). Reads/writes .arthur-engine.env.
Arthur onboarding sub-skill — Step 9: Recommend and configure continuous LLM evals for the Arthur task. Reads credentials and eval provider from .arthur-engine.env.
Arthur onboarding sub-skill — Step 5: Instrument the target repository with Arthur tracing (Python SDK, Mastra TS, or OpenInference). Reads detection results from .arthur-engine.env.
Arthur onboarding sub-skill — Step 2: Ensure Arthur GenAI Engine is available (local install or remote). Reads/writes .arthur-engine.env.
Onboard an agentic application to Arthur GenAI Engine. Guides through engine connection, task setup, code instrumentation, trace verification, and eval configuration. Invoke from any agentic application repository.