Resume Screening Shortlist
Resume Screening Shortlist converts candidate ID, resume section, education, employment dates into candidate matrix and shortlist with criterion evidence, gaps, ranking rationale, follow-up questions, and missing-information flags, with cited evidence, explicit rules, and unresolved exceptions. Use for resume screening shortlist, recurring review, and decision preparation.
Publicado 21 de ago de 2026 · Actualizado 26 de ago de 2026
Requisitos
Map the process identifiers, source columns, and row citation convention. Add the governing policy, rubric, schema, or reference tables to the scope. Define thresholds, status values, units, and exception severity.
Documento de la habilidad
El SKILL.md completo que tu agente lee y sigue.
Resume Screening Shortlist
Purpose
Resume Screening Shortlist turns the supplied records into candidate matrix and shortlist with criterion evidence, gaps, ranking rationale, follow-up questions, and missing-information flags. It keeps source facts, derived values, recommendations, and limitations separate so each conclusion can be checked.
Scope
Cover candidate ID, resume section, education, employment dates, role scope, required skill, years evidence, location, work authorization field, criterion. Preserve source identifiers, dates, units, and wording needed to trace every result.
Excluded for resume evidence and candidate triage: changing source records, taking external actions, making a human approval or employment decision, and asserting facts absent from the corpus.
Data basis
- Process documents and tables containing candidate ID, resume section, education, employment dates, role scope, required skill, years evidence, location, work authorization field, criterion.
- Company policy, rubric, schema, templates, and reference tables in scope.
- Run input for period, audience, entity, or threshold when it changes this run.
Result
Produce candidate matrix and shortlist with criterion evidence, gaps, ranking rationale, follow-up questions, and missing-information flags. Each material row and conclusion cites a document, section, page, row, field, or run input.
Quality criteria
- Every criterion-level evidence and hard-gate handling record is included or has an exclusion reason.
- Calculations state fields, units, denominator, and rule.
- Missing, contradictory, stale, and ambiguous values stay labelled rather than guessed.
- Facts, interpretations, and proposed next actions use separate fields.
- Supplied terminology, thresholds, and status values are used consistently.
- The final limitations section states what the corpus could not establish.
Instructions
Apply the role scorecard consistently. Cite page or section for each criterion. Missing resume evidence is unknown, not rejection; protected characteristics must not affect ranking. Keep hard gates separate. Preserve original values beside normalized values, cite every material finding, and use “not assessed” when required evidence or a rule is absent. When sources disagree, show both citations and explain the conflict. Keep row-level evidence in the supporting sheet and summarize only supported conclusions in the document.
Adapt before use
- Map the candidate-ID and resume-page mapping fields and row citation convention.
- Add the governing policy, rubric, schema, or reference tables to the scope.
- Define thresholds, status values, units, and exception severity.
Habilidades relacionadas
- 360 feedback synthesis
Synthesizes 360-degree survey responses into privacy-safe competency scores, rater-group themes, self-versus-other gaps, and a focused coaching agenda. Use for multi-rater reviews, leadership development, performance feedback, anonymized comment analysis, and competency calibration.
- Absence pattern report
Reports absence frequency, lost days, and recurring patterns from attendance records without inferring medical causes, and produces a cited HR report with a data-quality register. Use for leave analysis, attendance trends, capacity planning, absence reporting, or workforce reviews.
- Candidate Data Extraction
Extracts consistent candidate fields from CVs into a review-ready matrix, preserving source wording and marking absent or ambiguous information rather than inferring it. Use for resume parsing, applicant data normalization, recruitment data entry, or candidate profile preparation.
- Candidate Interview Debrief
Consolidates interview-loop notes into a candidate scorecard and decision memo, preserving question-level evidence, interviewer ratings, disagreement, and missing signals. Use after panel interviews, structured interview debriefs, hiring reviews, or candidate evaluation meetings.