ProfileClaw is a career context layer built for the age of AI agents. It converts short exploratory assessments — interests, work style, values, and skill confidence — into a structured profile that authorized agents can query over REST or MCP. Instead of re-explaining your career background to every assistant, you complete the assessments once and grant scoped access to the clients you choose.
What it offers
The platform bundles seven short questionnaires covering RIASEC interest patterns, Big Five work style signals, work values, self-reported skill confidence, True Colors temperament, DISC behavioral style, and work approach preferences. Each takes roughly three minutes, and together they feed a single career profile with dimension scores, top signals, and narrative summaries.
- Structured context API — agents read a versioned JSON context packet through
/api/v1/context, with tasks such as career and learning. - MCP and REST access — install once with a bearer key and connect clients like Claude Code, ChatGPT, Cursor, Gemini, DeepSeek, and OpenClaw.
- Consent-scoped sharing — each profile layer has its own visibility grant, and revocation is immediate.
- Audit trail — every agent read is logged so you can see who accessed what and when.
- Export options — preview the exact context an agent receives and export it as JSON or Markdown.
Who it is for
ProfileClaw suits people who use multiple AI assistants for career exploration, role matching, interview preparation, offer comparison, or learning plans and want consistent context across them. It is also relevant for anyone who wants a machine-readable complement to a traditional resume — one that captures how they work rather than only what they have done.
How it works
You complete the assessments, ProfileClaw structures the results into layered context with sources and confidence, and then you authorize agents to read specific layers. Raw answers stay private by default; agents see derived scores and summaries unless you explicitly allow more. The same profile is served to every connected client through its own scoped grant.
What sets it apart
Most career tests end at a report. ProfileClaw treats the result as a living data layer with boundaries enforced in code, so agents can use career context without overstepping what you have shared. Versioning, audit logging, and portable export keep the profile yours to move or withdraw.
