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Evidence-Bound CV Shortlist Pack
by Kuan Ee Lau
Turn a weighted hiring rubric and redacted CV text into a locator-backed evidence matrix, unresolved-evidence flags, and mandatory human-review queue.
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You say
{ "role_id": "automation-operations-specialist", "role_title": "Automation Operations Specialist", "redaction_attestation": true, "requirements": [ { "requirement_id": "incident-troubleshooting", "label": "incident troubleshooting", "kind": "criterion", "weight": 100, "verification_question": "Walk through one failure you resolved." } ], "candidates": [ { "candidate_id": "B", "source_type": "text", "sources": [ { "locator": "cv:B:1", "text": "Resolved a retry storm and added idempotency." } ], "evidence": [ { "requirement_id": "incident-troubleshooting", "status": "supported", "locators": [ "cv:B:1" ], "note": "Failure and fix are explicit." } ] } ] }
Your agent does
Review rank: 1 Candidate: B Weighted evidence score: 100 Evidence: cv:B:1 Must-have status: all_supported Boundary: human review required; no interview or hiring decision is made.
What you get
About this skill
Shortlisting breaks down when concrete examples, generic claims, missing facts, and contradictions are blended into one confident score. This local pack makes the evidence boundary visible. It validates a buyer-normalized rubric and redacted text, preserves exact source locators, applies the supplied weights, flags must-have gaps, creates candidate-specific verification questions, and records deterministic scoring and tie rules.
Only direct supported evidence receives criterion weight. Missing, unverifiable, or contradictory evidence remains unresolved. The ordered output is a review queue, not a candidate recommendation. An accountable human must verify the evidence and make every interview and hiring decision.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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