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AvelinLabs API Examples

Complete executable examples are maintained in the official AvelinLabs API examples repository.

These pages provide short explanations and links to public example resources. Full runnable scripts, payload libraries, sample response files, Postman assets, and integration templates are maintained in the examples repository.

If you are using examples to test fit in a product workflow, follow Evaluate AvelinLabs in your workflow alongside the sample payloads and responses.

For practical strong, weak, ambiguous, vague, and noisy input comparisons, see Input quality examples.

The examples are designed to show AvelinLabs as the workforce intelligence layer for AI-native hiring systems: structured occupation alignment, skill evidence, confidence and uncertainty signals, weak-signal analysis, ambiguity detection, review routing, and market context where available. They are not job-board examples, raw job-data feeds, ATS workflows, recruiting-agency playbooks, or generic AI prompts.

During beta, onboarding is API-first: register through the Platform API, verify your email, create a runtime API key, and use that key on product endpoints.

Current and planned public beta examples may cover:

  • platform onboarding
  • runtime API key usage
  • job analysis
  • job classification
  • occupation intelligence
  • occupation alignment
  • skill evidence
  • confidence, uncertainty, and review-routing signals
  • weak-signal and ambiguity handling
  • labor market intelligence
  • market context where available
  • runtime authentication patterns
  • API error handling
  • readiness checks
  • Meaning: Overall confidence signal for the top result.
  • Use it for: Routing and review, not as absolute truth.
  • Meaning: Trust-oriented score combining result strength and quality support.
  • Use it for: Deciding whether the selected output has enough evidence behind it.
  • Meaning: Combined uncertainty signal.
  • Use it for: Identifying inputs or matches that may need review.
  • Meaning: Routing label such as AUTO_ACCEPT, REVIEW, REJECT, or AMBIGUOUS.
  • Use it for: Deciding whether to display, review, reject, or request more input.
  • Meaning: Skill, tool, task, or domain signals detected from the input.
  • Use it for: Seeing what evidence the analysis used.
  • Meaning: Standard O*NET occupation code for a ranked candidate.
  • Use it for: Storing, comparing, or enriching the occupation consistently.
  • Meaning: Skills or capabilities supporting the selected match.
  • Use it for: Helping reviewers and client-facing teams explain why the result is relevant.
  • Meaning: Plain-English explanation for the candidate match.
  • Use it for: Reviewing and presenting the output in context.

For a fuller field-by-field walkthrough, read docs/annotated-job-analyze-response.md.

For practical field-by-field usage guidance in the public docs, see the Response Field Reference.

For practical batch and workflow measurements, see Metrics you can derive from AvelinLabs responses.

Some examples may be planned or upcoming while the beta evolves. Public docs should link only to files that exist. Until a specific example file is available, use the repository root:

https://github.com/AvelinLabs/avelinlabs-api-examples

Examples should not invent fields, endpoints, or response shapes. If an API area is early-stage or subject to change, the example should say so directly.