Pragmatic AI Labs
Official@paiml · Spain
The leading collection of graduate-level courses on Data Science, ML, Data Engineering, and Computer Science.
Agent Skills by Pragmatic AI Labs
Showing 8 vetted skills indexed across 2 GitHub repositories.
paimlcoverage-kaizen
Analyze Rust workspace coverage gaps and generate property-based tests.
paimlgateway-debug
Diagnose G0-G4 gateway failures using MQS zero-scores, evidence JSON, and stderr.
paimlmodel-certification
Guides HuggingFace model certification with tiered qualification and MQS scoring.
paimlTechnical Debt Tracking with PMAT
Detect SATD annotations, estimate debt hours, and generate trend reports.
paimlAutomated Refactoring with PMAT
Automate refactoring of multi-language codebases with PMAT and impact metrics.
paimlCode Quality Analysis with PMAT
Analyze code quality and technical debt with PMAT metrics.
paimlDeep Context Generation with PMAT
Generate compressed LLM-optimized codebase context with PMAT.
paimlMulti-Language Project Analysis with PMAT
Analyze polyglot codebases to detect languages, assess distribution, and generate per-language reports.
Frequently Asked Questions About Pragmatic AI Labs
FAQPage SchemaWhat specific tasks can engineers perform using PMAT?▼
Engineers can detect Self-Admitted Technical Debt (SATD) annotations, estimate remediation hours, generate trend reports, and perform deep context generation for codebase compression. Additionally, the system supports multi-language project analysis to assess distribution and identify specific language-based quality metrics.
Which technical personas benefit from these capabilities?▼
These capabilities are designed for software architects, lead developers, and quality assurance engineers responsible for maintaining large-scale polyglot codebases. It also serves data scientists and machine learning engineers requiring standardized certification and MQS scoring for model deployment readiness.
What are the prerequisites for diagnosing gateway failures?▼
To diagnose G0-G4 gateway failures, users must provide the associated MQS zero-scores, the generated evidence JSON files, and the corresponding stderr logs. These inputs allow the system to isolate failure points within the gateway architecture effectively.