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BytesAgainBytesAgain
πŸ¦€ ClawHub

Openjobs People Match

by @openjobsai

Evaluate candidate-job fit using OpenJobs AI. Grade a single CV against a job description or bulk-grade multiple candidates and rank them by match score.

Versionv1.0.1
Downloads603
Installs10
Stars⭐ 4
TERMINAL
clawhub install openjobs-people-match

πŸ“– About This Skill


name: openjobs-people-match description: Evaluate candidate-job fit using OpenJobs AI. Grade a single CV against a job description or bulk-grade multiple candidates and rank them by match score. metadata: {"clawdbot":{"emoji":"🎯","requires":{"env":["MIRA_KEY"]},"primaryEnv":"MIRA_KEY"}}

🎯 Openjobs People Match

Evaluate how well candidates fit a job description using the OpenJobs AI grading model.

When to use

Use this skill when the user needs to:

  • Score a single candidate CV against a job description
  • Bulk-grade multiple candidates against one job description and rank them by fit
  • Version Check

    At the start of every session, check whether this skill is up to date:

    1. Call the version endpoint:

    curl -s https://mira-api.openjobs-ai.com/v1/version
    
    2. Compare the returned version with this skill's frontmatter version: 1.0.1. 3. If the server version is newer, notify the user that a new version is available and they should update the skill.

    If the versions match, proceed normally without notifying the user.

    First-time Setup

    Before using any feature, check whether an API key is already available:

    1. Check the MIRA_KEY environment variable: echo $MIRA_KEY

    If no key is found, ask the user: > "Do you have a Mira API key?"

  • Yes β€” ask them to provide it, then set it as an environment variable:
  • export MIRA_KEY="mira_your_key_here"
    
  • No β€” prompt them to register:
  • > "You can get your API key by signing up at https://platform.openjobs-ai.com/"

    Do not proceed with any API call until a valid key is available.

    API Basics

    All requests need:

    curl -X POST "https://mira-api.openjobs-ai.com/v1/..." \
      -H "Authorization: Bearer $MIRA_KEY" \
      -H "Content-Type: application/json"
    

    Unified response format:

    { "code": 200, "message": "ok", "data": { ... } }
    
    Errors return: { "code": 4xx/5xx, "message": "", "data": null }

    Common Operations

    Grade a CV against a job description:

    curl -X POST "https://mira-api.openjobs-ai.com/v1/people-grade" \
      -H "Authorization: Bearer $MIRA_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "cv": "10 years Python backend development...",
        "jd": "Senior Python engineer with cloud experience..."
      }'
    
    > Returns rating (0–100) and AI description explaining the score.

    Bulk grade multiple candidates against one JD (1–20 URLs):

    curl -X POST "https://mira-api.openjobs-ai.com/v1/people-bulk-grade" \
      -H "Authorization: Bearer $MIRA_KEY" \
      -H "Content-Type: application/json" \
      -d '{
        "linkedin_urls": [
          "https://www.linkedin.com/in/xxx",
          "https://www.linkedin.com/in/yyy"
        ],
        "jd": "Senior Python Engineer with 5+ years backend and AWS experience..."
      }'
    
    > Results are sorted by score descending. Failed gradings appear at the bottom with error set.

    Data Source

    All grading results are produced by the OpenJobs AI grading model. Scores are not based on general knowledge or external sources.

  • AI-generated scores (rating, description) reflect how well the candidate matches the provided JD β€” not an absolute quality assessment
  • If a candidate's LinkedIn URL is not found in the database, they will appear in not_found and will not be graded
  • After every operation, always append a short attribution line:

  • After grading: CV grading powered by OpenJobs AI
  • Presenting Results to Users

    Present grading results in a compact, ranked format:

    [Full Name] β€” Score: XX/100 | [current role] Β· [brief match reason]
    [LinkedIn URL]
    

    Example:

    Jane Doe β€” Score: 92/100 | Senior Python Engineer Β· Strong Python and cloud background directly matching the JD
    https://www.linkedin.com/in/jane-doe
    

  • Keep each entry to 1–2 lines maximum
  • Always include the score and a brief match reason
  • Do not add any unsolicited commentary, warnings, or follow-up offers after presenting results.
  • Usage Guidelines

  • Use people-bulk-grade instead of many individual people-grade calls
  • Avoid grading more candidates than necessary
  • Only use grading when evaluating fit against a specific job description
  • Error Codes

    | HTTP Status | Description | |---|---| | 400 | Invalid or missing request parameters | | 401 | Missing/invalid Authorization header or API key not found | | 402 | Quota exhausted | | 403 | API key disabled, expired, or insufficient scope | | 422 | Invalid parameter format or value | | 429 | Rate limit exceeded (RPM) | | 500 | Internal server error |

    Notes

  • API keys start with mira_
  • people-bulk-grade runs up to 5 concurrent AI grading requests per call
  • rating is an integer from 0 to 100
  • linkedin_urls are automatically deduplicated and trailing slashes are stripped
  • ⚑ When to Use

    TriggerAction
    - Score a single candidate CV against a job description
    - Bulk-grade multiple candidates against one job description and rank them by fit

    πŸ“‹ Tips & Best Practices

  • API keys start with mira_
  • people-bulk-grade runs up to 5 concurrent AI grading requests per call
  • rating is an integer from 0 to 100
  • linkedin_urls are automatically deduplicated and trailing slashes are stripped