Results for “software-classification”

Provides HIPAA-specific decision gates and guardrails for healthcare software, directing users to implementation and review skills for PHI handling, audit logging, and compliance.

Manage software complexity through deep modules, information hiding, and strategic programming, with a scoring framework for evaluating design quality.

Cardinality and model family jointly determine the encoding.

Helps software engineers with discrete job search tasks: job description analysis, CV tailoring, cover letter writing, offer evaluation, and follow-up emails.

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI, Microsoft Agent Framework, GitHub Copilot SDK, ONNX Runtime, and OllamaSharp.

Train or fine-tune sentence-transformers models for retrieval, similarity, clustering, classification, and reranking, with support for bi-encoders, cross-encoders, and sparse encoders.

Selects and scores marketing ideas for SaaS products using a feasibility scoring system to prioritize high-impact, low-effort opportunities.

Multi-dimensional lead qualification scoring. Evaluates leads against BANT criteria, firmographic fit, behavioral signals, and intent indicators. Outputs qualified/disqualified verdict with detailed reasoning.

Defines ideal customer profile filters, scores inbound and outbound leads, and builds a lightweight qualification rubric to sharpen pipeline focus for founder-led sales.

Naming Craft

Assess software supply chain security by generating SBOMs, scanning dependencies, auditing CI/CD pipelines, analyzing container images, and verifying vulnerability reachability.

Classifies HTML pages, forms, and fields using machine learning to detect page types, form types, and field types from HTML content or URLs.

Systematically diagnose and fix software bugs by analyzing error messages, stack traces, logs, and runtime behavior across multiple languages. Use when the user requests debugging or provides relevant inputs for this workflow.

Identify underspecified areas in the current feature spec by asking targeted clarification questions and encoding answers back into the spec.

Guides open source license selection, comparison, compliance review, and drafting of LICENSE/NOTICE files using canonical texts.

Compare tradeoffs and recommend architectural patterns — dependency injection, service-oriented architecture, repository, domain events, circuit breaker, and anti-corruption layer. Use when choosing between design patterns, planning microservices boundaries, evaluating system design alternatives, or asking 'which pattern should I use' for a specific coupling or resilience problem.

Demystifying CLIP Data

Reduce complexity of over-engineered code. Identify unnecessary abstractions, remove dead code, flatten deep nesting, and simplify logic while preserving behavior.

Guides coding workflows with planning, implementation, verification, and testing, storing user preferences locally on request.

Classifies ECC components (skills, commands, rules, hooks) into DAILY and LIBRARY buckets based on evidence from the actual codebase, producing an install plan and verification report.

Automates the collection of suspicious files from endpoints and email gateways, submission to sandbox and multi-engine scanners, and generation of verdicts with IOCs for SIEM integration.

Enforces system design, project structure, dependency management, error handling, testing, and documentation standards at principal-engineer level.

Enforces complexity analysis, correct data structure selection, vectorization, and algorithm implementation discipline at principal-engineer level.

Use to design or audit complaint identification and classification in customer support, especially in regulated sectors. Trigger for "are we identifying complaints correctly", "complaint detection", regulatory complaint definition, FCA or CFPB complaint handling, vulnerable customer identification, root cause categorisation for complaints, or building an AI classifier for complaints.

Code review automation for TypeScript, JavaScript, Python, Go, Swift, Kotlin. Analyzes PRs for complexity and risk, checks code quality for SOLID violations and code smells, generates review reports. Use when reviewing pull requests, analyzing code quality, identifying issues, generating review checklists.

Refactoring Analyst

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Scores competitors across nine weighted dimensions with explicit 1–5 rubrics and a tension plot, producing comparable profile cards for competitive analysis.

Discover and reorganize the skill inventory into hierarchical sub-skill bundles. Use when the user asks to review, group, or consolidate skills into a parent bundle.

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Automates multi-source enrichment of IPs, domains, URLs, and file hashes using VirusTotal, AbuseIPDB, Shodan, GreyNoise, URLScan.io, and MISP to provide contextual risk scoring and disposition recommendations for SOC analysts.

Scans a codebase for Martin Fowler's catalog of code smells, reporting each with location, severity, and refactoring recommendations, and ranks files to build a prioritized refactoring queue.

Use only when the request concerns standards, responsibility, complexity, smells, naming, refactoring catalogs, or task-plan rules; not routine work.

Type design analysis — encapsulation, invariant expression, usefulness, and enforcement.