SkillSight: Automated Skill Assessment

• SkillSight is a suite of systems that objectively infers and assesses skills using multimodal signals such as text, video, and gaze.
• It employs advanced transformer-based models and synthetic data augmentation to achieve high precision in skill extraction and ranking, improving metrics by up to 25 points.
• The platform supports real-time, low-power skill assessment across various domains including HR analytics, surgical training, and AR/VR applications.

SkillSight is a class of systems and computational methodologies for large-scale, automated, objective skill inference, skill requirement mapping, and skill assessment based on signals from text, video, gaze, and other behavioral or physiological data streams. These platforms leverage NLP, semantic embedding models, computer vision, attention modeling, and data mining pipelines to detect, rank, and analyze skills in domains ranging from labor market analytics and human resource management to surgical training and first-person wearable assessment. Core functionalities include skill extraction from unstructured documents, standardized skill mapping (often to taxonomies such as ESCO), context-aware skill ranking, cross-entity skill matching, personal skill gap diagnostics, and real-time or low-power skill assessment in physical tasks.

1. Automated Skill Extraction and Mapping

SkillSight platforms employ advanced NLP architectures to extract and normalize skill mentions—both explicit and implicit—from diverse free-text sources such as job postings, curricula vitae, policy documents, and educational records. Skill extraction typically uses transformer-based LLMs (e.g., BERT, SentenceTransformer, LaBSE) fine-tuned for extreme multi-label classification or trained with synthetic supervision rooted in large ontologies like ESCO.

Synthetic data augmentation is central: for each skill, LLMs generate multiple job-ad-style sentences that mention or imply the target skill, enabling construction of fully synthetic labeled datasets with coverage across tens of thousands of skills. These datasets, validated to achieve ~94% precision in manual checks, are used to train bi-encoder models via contrastive loss, where positive sample pairs are skill-sentence and negative pairs are mismatched (Decorte et al., 2023). Models then operate as efficient, scalable, approximate nearest neighbor searchers in embedding space, substantially outperforming baseline distant supervision methods (up to +25 points in R-Precision@5).

For job-posting to skill matching, multi-stage pipelines combine logistic regression classifiers trained on embeddings, similarity-based retrievers for both skill labels and synthetic sentences, and optionally LLM-based re-ranking stages for fine-grained candidate selection. Reranking using models such as GPT-4 via prompt engineering increases RP@10 up to 22 points over prior methods (Clavié et al., 2023).

2. Personalized and Contextual Skill Ranking

In skill ranking for specific job titles, SkillSight systems utilize weak supervision: similar job titles are identified in a high-dimensional embedding space, and skill frequencies among these neighbors are used as soft importance labels. Language-agnostic encoders (e.g., LaBSE) are fine-tuned on this neighbor-induced label set, and a job- or context-specific inverse document frequency (IDF) re-weighting scheme is applied to down-weight generic skills and highlight specialized ones. This approach yields high mean average precision (MAP@20 = 0.722) and robust cross-lingual performance (Anand et al., 2022).

User-facing applications compute the importance score for each skill as

$$\text{score}(T,s) = p(T,s) \times \log\left(\frac{N_{\text{total}}}{f_s}\right)$$

where $p(T,s)$ is the model’s probability for skill $s$ and $f_s$ is the frequency of $s$ in the training pool.

In production-ready systems, these models enable real-time ranking of skills for any job or candidate profile within 100 ms, scalable to tens of thousands of skills, and transferable across over 100 languages.

3. Skill Assessment Beyond Text: Video, Gaze, and Behavioral Streams

SkillSight approaches have been extended to domains requiring skill inference from behavioral signals beyond text, particularly in first-person video/gaze settings or specialized task environments such as surgery. In egocentric skill assessment, SkillSight deploys hybrid teacher–student architectures:

• SkillSight-Teacher jointly models egocentric video (via TimeSformer with gaze-induced attention), gaze-crop sequences (per-frame image patches centered on gaze), and raw gaze trajectories using transformer backbones. Skill labels (e.g., novice/expert) are predicted by fusing these modalities via a multi-layer perceptron (Wu et al., 24 Nov 2025).
• SkillSight-Student applies knowledge distillation to yield an ultra-light gaze-only student model. With a four-layer transformer over normalized 3D gaze streams, this student achieves up to 44.4% accuracy on complex skill recognition tasks (e.g., on Ego-Exo4D sports, music, and cooking), maintaining 73× lower power consumption than video-based baselines. Gaze-only inference leverages cognition findings: experts display distinct anticipatory gaze strategies compared to novices.

In surgical domains, SkillSight frameworks like PRINTNet combine DeepLabV3-based semantic segmentation (instrument pixel-level detection) with StrongSORT object tracking, producing real-time metrics such as the ratio of total procedure time to instrument visible time—a feature shown to strongly correlate with human-rated expertise scores (Das et al., 25 Sep 2024). Downstream, multilayer perceptrons predict skill level (novice/expert) with up to 87% accuracy using motion and usage patterns extracted from instrument tracks.

4. SkillSight in Benchmarking and Model Evaluation

SkillSight principles have been employed in machine learning evaluation to parse the latent skills required by instances in composite benchmarks. Rather than treat overall accuracy as the only metric, SkillSight automatically derives structured rationales for each sample using LLM prompting, extracts the invoked skills by step, and groups instances into homogeneous “skill-slices” (Moayeri et al., 17 Oct 2024). This enables:

• Quantifying slice-level accuracies for each model, revealing up to 20% differentials in specific competencies hidden by near-equal aggregate scores.
• Facilitating instance-level routing in model ensembles: for every instance, select the model with top proficiency on its required skills; this increases overall accuracy by 3.2% over any individual model.
• Curating prototype test sets targeting bespoke skill profiles, enabling focused evaluations not achievable via surface attributes.

This paradigm instrumentalizes “skills as evaluation axes,” identifying and routing on fine-grained competencies across diverse AI benchmarks.

5. Skill Assessment of Personal, Professional, and Non-Cognitive Skills

In non-technical domains (e.g., personal or professional competencies), SkillSight leverages LLMs to map open-ended text responses (such as SJT answers) to construct-valid skill features (e.g., interpretation, justification, perspective-taking). The system assigns one feature label per prompt via zero-shot or prompt-engineered LLM invocation, with Cohen’s weighted κ agreement up to 0.60 for certain features—at or near human–human agreement levels for sufficiency and justification (Walsh et al., 18 Jul 2025).

Operationally, SkillSight can compose modular pipelines in which every targeted competency is mapped to a proxy feature, extracted via per-feature or ensemble LLM prompting. Systematic monitoring, periodic recalibration, and explainability (retaining model “reasoning” for feedback) are integral to robust deployment.

6. System Architectures and Ecosystem Integration

SkillSight platforms integrate multi-stage computational pipelines involving:

• Preprocessing and cleaning: Document ingestion, normalization (e.g., lowercasing, tokenization), and semantic chunking.
• Embedding and indexing: Semantic embedding of chunks using transformer models (e.g., all-MiniLM-L6-v2), normalized for inner product similarity, and indexed in ANN frameworks (FAISS, Elasticsearch).
• Candidate retrieval and re-ranking: Skill candidates are aggregated via combined logistic, similarity, and LLM stages; optional ensemble voting improves retrieval robustness at scale (Clavié et al., 2023, Koundouri et al., 13 Mar 2025).
• Ontology mapping: Extraction results are mapped to standardized occupations or course catalogs via intersection and cosine similarity scores for dynamic, contextual learning-pathway recommendations.
• Visualization: Dash or React-based front ends support network graphs, bar/donut charts, and force-directed layouts to facilitate skill–occupation–course exploration, with real-time user interaction (Koundouri et al., 13 Mar 2025).

A summary table of technical components in leading SkillSight system variants:

System Aspect	Key Models/Algorithms	Typical Metrics/Outcomes
Skill extraction (text)	LLM synthetic data, bi-encoders	RP@5, MAP@20, F1, Precision/Recall (≥0.96)
Skill ranking	LaBSE/transformers + IDF	Mean avg. precision, validated skill scores
Benchmark slicing	LLM rationale parsing, skill-slice grp.	Up to 20% diff in per-skill accuracy
Behavioral skill infer	Video+gaze transformers, gaze-only	Accuracy, mIoU, MOTP/MOTA, power (9–943 mW)
Job/CV matching	BERT+GRU, NER, TFIDF match ratios	MRR (≥0.93), NDCG@k, Recall@k
Visualization	Dash, Plotly, ECharts network graphs	Real-time, interactive skill maps

7. Practical Applications, Challenges, and Extensions

SkillSight is deployed in employment analytics, personalized job matching, resume/CV parsing, real-time skill assessment in AR/VR environments, automated feedback for surgical training, and large-scale education/testing evaluation. Adaptations exist for policy analysis, HR talent mapping, course recommendation, and regulatory monitoring.

Current operational challenges include:

• Handling rare or ambiguous skills (mitigated by IDF weighting and feedback loops)
• Cross-lingual generalizability (solved via multilingual encoders and synthetic supervision)
• Power constraints on wearables (addressed by gaze-only, distilled models)
• Maintaining alignment and avoiding drift in automated non-cognitive skill assessment (mitigated by recalibration and explainability provisions)
• Real-world accuracy in complex, high-noise environments (requires continuous retraining and dataset expansion)

Ongoing extensions target domain adaptation to healthcare, law, and manufacturing, surfacing bias in skill recommendations, collaborative annotation tools, and dynamic re-indexing as ontologies or course offerings expand.

In sum, SkillSight encompasses a suite of architectures and algorithms for multifaceted, efficient, and objective skill assessment and guidance, unifying advances from NLP, computer vision, attention modeling, and human–computer interaction for applications across the modern workforce, education, and embodied skill learning (Wu et al., 24 Nov 2025, Moayeri et al., 17 Oct 2024, Clavié et al., 2023, Anand et al., 2022, Walsh et al., 18 Jul 2025, Das et al., 25 Sep 2024, Koundouri et al., 13 Mar 2025, Wu, 2023).