Connect candidates with jobs and employers with talent

WorkorAI

Overview

WorkorAI is a talent marketplace exposed to agents through an MCP server
(streamable HTTP at https://workorai.com/mcp, listed on the official MCP
Registry as io.github.work0r-ai/workorai). This skill routes requests by
intent across the dual-role tool surface: 9 candidate.* tools (job search,
job detail, applications, apply, invitations, saved jobs) and the
employer.* tools (job lifecycle, candidate discovery, invitations,
applicant review). Employer candidate discovery returns tiered rankings
(best/good/weak) with a white-box match explanation per candidate - fit
score, skills proven in interview, gaps, and a quotable rationale - instead
of a black-box score.

When to Use This Skill

• Use when a user asks to find a job, search vacancies, apply to a position,
  or track their applications ("find me a job").
• Use when an employer wants to post, publish, update, close, or archive a
  job on WorkorAI.
• Use when an employer asks to find, rank, compare, or evaluate candidates,
  or asks why a candidate matches a role.
• Use when a user needs to set up or troubleshoot the WorkorAI MCP
  connection and API key onboarding.

How It Works

Step 1: Connect the MCP server

Add the WorkorAI MCP server to your agent's MCP configuration. For Claude
Code:

claude mcp add --transport http workorai https://workorai.com/mcp

If the user has no API key yet, call the request_access tool and follow
the onboarding it returns.

Step 2: Route by role and intent

Detect whether the request is a candidate flow or an employer flow, then use
the matching tool group:

• Candidate: candidate.search_jobs, candidate.get_job,
  candidate.apply_to_job, candidate.get_applications,
  candidate.accept_invitation / candidate.decline_invitation,
  candidate.withdraw_application, candidate.set_saved_job,
  candidate.get_saved_jobs.
• Employer: employer.create_job → employer.publish_job →
  employer.close_job / employer.archive_job for the lifecycle;
  employer.search_candidates_for_job or
  employer.search_candidates_by_query for discovery;
  employer.invite_candidate, employer.list_applicants,
  employer.get_applicant_detail, employer.set_review_status for
  pipeline work.

Step 3: Explain matches with white-box data

When presenting employer search results, keep the tier structure
(best/good/weak) and surface each candidate's matchExplanation: fit score,
interview-proven skills, gaps, and rationale. For deeper comparison, fetch
per-candidate interview evidence with employer.get_candidate_evidence and
employer.get_applicant_transcript.

Examples

Example 1: Candidate job search

User: "Find me remote TypeScript jobs and apply to the best one."
Agent: candidate.search_jobs(query="TypeScript", remote=true)
       → present ranked results → candidate.get_job(id)
       → confirm with the user → candidate.apply_to_job(id)

Example 2: Employer candidate discovery

User: "Who are the best candidates for my Senior Backend role?"
Agent: employer.search_candidates_for_job(jobId)
       → report Best tier with each candidate's fit score, proven
         skills, and gaps → employer.invite_candidate on approval

Best Practices

• ✅ Confirm with the user before applying, inviting, or changing job
  status - these are visible, stateful marketplace actions.
• ✅ Quote the white-box match explanation when recommending a candidate,
  so the employer sees why, not just a score.
• ✅ Use request_access for key onboarding instead of asking users to
  paste credentials into chat.
• ❌ Don't fabricate fit scores or ranks - only report what the tools
  return.
• ❌ Don't apply to jobs or send invitations in bulk without explicit
  user approval.

Limitations

• Requires a WorkorAI account and API key; tools fail without a valid key.
• This skill does not replace environment-specific validation, testing, or
  expert review.
• Stop and ask for clarification if required inputs, permissions, or safety
  boundaries are missing.

Security & Safety Notes

• All operations go through the remote WorkorAI MCP server over HTTPS; the
  skill itself runs no shell commands.
• Mutating tools (apply, withdraw, invite, publish, close, delete) should
  be preceded by an explicit user confirmation.
• Treat API keys as secrets: store them in MCP client configuration, never
  in chat transcripts or committed files.

Additional Resources

• Source repository - full skill
  with reference files and agents (npm: @workorai/agent-kit)
• WorkorAI MCP endpoint