99 lines
2.7 KiB
Markdown
99 lines
2.7 KiB
Markdown
---
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id: candidate-analysis
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name: Candidate Analysis
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description: Analyse the applicant pool's quality, and how much of it has actually been screened.
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category: hiring
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pages:
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- candidates
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- candidates-analysis
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status: active
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version: 1
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triggers:
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- candidate quality
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- quality of candidates
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- score band
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- score bands
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- screening coverage
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- how strong*candidates
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- how good*candidates
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owliver:
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enabled: true
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suggestions:
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- label: How strong is the candidate pool?
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capability: summary
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- label: Show candidates by score
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capability: table
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- label: Score bands
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capability: progress
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capabilities:
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- summary
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- stats
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- table
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- list
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- progress
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- insight
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responses:
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summary:
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title: Candidate quality
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source: candidates.quality
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stats:
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title: Candidate quality
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source: candidates.quality
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table:
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title: Candidates by score
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source: candidates.quality
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limit: 10
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list:
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title: Strongest candidates
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source: candidates.quality
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limit: 5
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progress:
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title: Candidate quality
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source: candidates.quality
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insight:
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title: Candidate quality
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source: candidates.quality
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---
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# Candidate Analysis
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## Purpose
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- Report how strong the applicant pool is.
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- Report how much of it anyone has actually looked at, beside the quality figure.
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- Rank candidates by score so a shortlist has a starting point.
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## Capabilities
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- Summarize pool size, screening coverage, average score and interview count.
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- List or tabulate candidates by score.
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- Break the scored pool into quality bands.
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## Data
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Reads `candidates.quality`, which counts `JobApplication` records and their
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`ai_score`, joined to `AIInterview` records for interview coverage.
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## Analysis
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Coverage is reported next to quality, always. An average score computed from a
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fifth of the pool is not the pool's average, and reporting the first without the
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second is how a hiring dashboard talks itself into confidence.
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Scored candidates are grouped into four bands — 80 and above, 70 to 79, 50 to 69,
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and below 50 — because a mean hides whether a pool is uniformly mediocre or
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split between strong and weak.
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## Output
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Pool size, share screened, average score across scored candidates only, and
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interview count. Then candidates ranked by score with their role and stage.
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## Limitations
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- Unscored candidates are excluded from the average rather than counted as zero.
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They are reported separately as the unscreened share.
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- A score is an AI screening score, not an interview outcome or a hiring decision.
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- Filtering by period counts applications by when they were received, not by when
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they were screened.
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