krow_backend/skills/candidate-analysis.md

id, name, description, category, pages, status, version, triggers, owliver

id	name	description	category	pages	status	version	triggers	owliver
candidate-analysis	Candidate Analysis	Analyse the applicant pool's quality, and how much of it has actually been screened.	hiring	candidates candidates-analysis	active	1	candidate quality quality of candidates score band score bands screening coverage how strong*candidates how good*candidates	enabled suggestions capabilities responses true label capability How strong is the candidate pool? summary label capability Show candidates by score table label capability Score bands progress summary stats table list progress insight summary stats table list progress insight title source Candidate quality candidates.quality title source Candidate quality candidates.quality title source limit Candidates by score candidates.quality 10 title source limit Strongest candidates candidates.quality 5 title source Candidate quality candidates.quality title source Candidate quality candidates.quality

Candidate Analysis

Purpose

• Report how strong the applicant pool is.
• Report how much of it anyone has actually looked at, beside the quality figure.
• Rank candidates by score so a shortlist has a starting point.

Capabilities

• Summarize pool size, screening coverage, average score and interview count.
• List or tabulate candidates by score.
• Break the scored pool into quality bands.

Data

Reads candidates.quality, which counts JobApplication records and their ai_score, joined to AIInterview records for interview coverage.

Analysis

Coverage is reported next to quality, always. An average score computed from a fifth of the pool is not the pool's average, and reporting the first without the second is how a hiring dashboard talks itself into confidence.

Scored candidates are grouped into four bands — 80 and above, 70 to 79, 50 to 69, and below 50 — because a mean hides whether a pool is uniformly mediocre or split between strong and weak.

Output

Pool size, share screened, average score across scored candidates only, and interview count. Then candidates ranked by score with their role and stage.

Limitations

• Unscored candidates are excluded from the average rather than counted as zero. They are reported separately as the unscreened share.
• A score is an AI screening score, not an interview outcome or a hiring decision.
• Filtering by period counts applications by when they were received, not by when they were screened.