/talent-assess

Run a Talent-Augmenting OS onboarding assessment. Profile is saved locally in profiles/pro-{name}.md. The MCP server is the source of truth for the question bank and scoring.

First: greet the user before any tool calls

Before calling any tool, send a short greeting so the user sees activity:

"Hi! I'll guide you through a ~15 minute assessment to build your personalised Talent-Augmenting OS profile. Loading the question bank now: if the server has been idle, this first call can take 30–60 seconds to wake up. One moment."

Never go silent during setup.

Flow

1. Call the MCP tool talent_assess_start to fetch the full question bank, scoring anchors, and domain taxonomy as JSON. This is the single source of truth: do NOT try to read mcp-server/src/assessment.py from the filesystem (plugin installs don't ship that file).
2. If talent_assess_start fails or times out after ~90 seconds, tell the user:

"The TAOS server is taking a while to wake up. Open https://proworker-hosted.onrender.com in a browser tab, wait ~45 seconds for it to respond, then tell me 'retry' and I'll try again."

Do not proceed without the protocol.

1. Check profiles/pro-*.md or profiles/tal-*.md in the current directory. If one exists for this user, confirm they want to replace it before continuing.
2. Run the assessment conversationally, one section at a time (see sections below). For the expertise-domain step, call talent_suggest_domains to get the taxonomy.
3. Compute scores by calling talent_assess_score with the raw answers and domain ratings.
4. Save the profile by calling talent_assess_create_profile with the scores plus qualitative data: it generates the markdown and writes it. If that call fails, fall back: generate the markdown from the template returned by talent_assess_start and Write it to profiles/pro-{name}.md (lowercase, kebab-case) in the current directory.
5. Present the scores and ask: "Do these feel accurate? Anything you'd adjust?"

Assessment sections

• Identity: name, role, organisation, industry, context summary
• Section A (AI Dependency Risk): 5 questions, behavioural anchors 1-5
• Section B (Growth Potential): 5 questions, behavioural anchors 1-5
• Section D (AI Literacy): 4 questions, behavioural anchors 1-5
• Expertise Domains: call talent_suggest_domains for the taxonomy. Rate each 1-5 on the ESA scale with a behavioural example. Aim for 5-10 domains.
• Goals & Context: career goals, skills to develop/protect, task classification (automate/augment/coach/protect/hands-off), red lines, learning/feedback/communication style

Guidelines

• Be warm and conversational, not clinical
• Explain WHY you're asking each question briefly
• If the user gives a vague answer, probe with the behavioural anchors
• It's OK to adjust scores based on behavioural evidence (expert interviewer mode)
• The whole assessment should take 15-20 minutes
• If a profile already exists, note it and confirm they want to replace it