arthur-ai/integrations/claude-code-skills/arthur-onboard/arthur-onboard-prompts

Arthur Onboard — Step 6: Extract & Register Prompts

Read State

cat .arthur-engine.env 2>/dev/null || echo "(no state file)"

Parse ARTHUR_ENGINE_URL, ARTHUR_API_KEY, ARTHUR_TASK_ID.

Extract Prompts via Sub-agent

Delegate to a Task sub-agent (full claude agent) to find prompts in the repo:

Analyze the agentic application at: <REPO_PATH>

Use Read, Glob (find), and Grep to find all prompt definitions:
- System prompt strings assigned to variables (any language)
- User prompt templates with variables
- Multi-turn message arrays in OpenAI format ([{"role": "system", ...}])
- Prompt files (.txt, .md, .jinja2)
- Agent instruction strings passed to agent/chain initialization

Also detect the LLM model and provider used (from API call patterns, imports, env var names
like OPENAI_API_KEY, model= parameters, etc.).

Return ONLY a raw JSON object with no markdown, no explanation:
{
  "prompts": [
    {
      "name": "kebab-case-unique-name",
      "messages": [
        {"role": "system", "content": "..."},
        {"role": "user", "content": "..."}
      ],
      "model_name": "gpt-4o" | null,
      "model_provider": "openai" | "anthropic" | "gemini" | "bedrock" | "vertex_ai" | null
    }
  ],
  "detected_model_name": "<model>" | null,
  "detected_model_provider": "<provider>" | null
}

Rules:
- Only include prompts with substantive content (skip empty strings and test fixtures)
- Convert template variables to {{double_brace}} format regardless of source syntax
- Names: unique, lowercase, kebab-case, descriptive
- If nothing found: {"prompts": [], "detected_model_name": null, "detected_model_provider": null}

After Extraction

• No prompts found: tell the user, exit this skill
• Prompts found: show the list and ask for confirmation

For each confirmed prompt, register via:

curl -s -X POST \
  -H "Authorization: Bearer $ARTHUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d "$PROMPT_JSON" \
  "$ARTHUR_ENGINE_URL/api/v1/tasks/$ARTHUR_TASK_ID/prompts/$PROMPT_NAME"

Where $PROMPT_JSON = {"messages": [...], "model_name": "...", "model_provider": "..."}.

This step is non-blocking — log a warning and continue if it errors.