WorkorAI

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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.

Category: AI & Intelligent Agents
Repo: antigravity-awesome-skills
Path: skills/workorai/SKILL.md
Updated: 7/5/2026, 4:58:46 PM
Open on GitHubHow to Install

AI Summary

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. It is useful for LLM applications, agent orchestration, RAG pipelines, AI evaluation, and multi-agent workflows. Source: antigravity-awesome-skills (skills/workorai/SKILL.md).

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", "ищу работу").
  • 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

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