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jbaruch/face-recognition-calibration-djl

Empirical calibration for DJL face_feature (ArcFace/FaceNet 512-d) embeddings: cosine distance bands, piecewise confidence formula, enrollment quality targets. Replaces the dlib-based jbaruch/face-recognition-calibration tile for Kotlin/JVM pipelines.

74

Quality

93%

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Overview
Quality
Evals
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Files

Quality

Content

82%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is highly actionable with complete executable Kotlin/DJL code and strong diagnostic feedback loops, organized into clear sections. It loses a little on conciseness due to duplicated formula/table presentations and on workflow clarity because the main pipeline lacks explicit prose validation checkpoints.

Suggestions

De-duplicate the confidenceOf formula and the distance→confidence tables: keep the comparison table in the teaching section and reference it from the pipeline rather than restating both.

Add one explicit validation checkpoint in the pipeline prose (e.g., 'verify embeddings are L2-normalized before calling cosineDistance') to match the feedback-loop rigor already present in the diagnostic section.

Consider moving the enrollment-averaging and threshold recipes into a short reference block or section pointer if the file grows, to keep the core calibration overview lean.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence (no explaining cosine distance or L2 normalization), but the confidenceOf formula and the distance→confidence tables each appear twice (teaching section and full pipeline), which is minor redundancy that could be trimmed.

4 / 5

Actionability

Fully executable, copy-paste-ready Kotlin/DJL code covers the common cases — translator, model loading, cosineDistance, confidenceOf, enrollment averaging, thresholding, and a diagnostic print — with no pseudocode gaps.

5 / 5

Workflow Clarity

The pipeline sequence is clear in code and the diagnostic section provides a 'Look for ✓' checklist plus feedback loops ('If true=0.55, others=0.58 → re-enroll tighter'), but the happy path lacks explicit prose validation checkpoints, leaving a minor gap.

4 / 5

Progressive Disclosure

No bundle files exist; the skill is self-contained with well-organized, clearly headed sections and easy navigation. It scores 4 rather than 5 because the 5 anchor centers on one-level-deep external references, and the doc exceeds the under-50-line simple-skill exception.

4 / 5

Total

17

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20

Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is exemplary: third-person, concrete, and comprehensive, clearly stating both what the skill does and when to use it with natural trigger phrasing. It avoids fluff and over-claims while staying tightly scoped to the DJL face_feature confidence-calibration niche.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Compute perceptually-correct confidence… using piecewise mapping', 'enrollment averaging', 'L2 normalization', and diagnosing — with comprehensive coverage and no vague filler, matching the 5 anchor.

5 / 5

Completeness

Explicitly answers 'what' (compute confidence via piecewise mapping with enrollment averaging and L2 normalization) and 'when' via a concrete 'Use when…' clause with multiple trigger scenarios.

5 / 5

Trigger Term Quality

Natural trigger terms include 'FaceNet/ArcFace cosine distance', 'confidence display (semaphore, progress bar, gauge)', and the diagnostic phrase 'strong-looking recognition still reads as yellow or weak', with synonyms and natural user phrasing.

5 / 5

Distinctiveness Conflict Risk

Highly specific to 'DJL face_feature cosine distances' with concrete thresholds (0.30/0.65), carving a clear niche with minimal overlap risk against other skills.

5 / 5

Total

20

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20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Reviewed

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