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Agent Skills utili, ordinati per utilizzo reale.
Confronta Skills verificati per funzione, contenuto del pacchetto, installazioni e affidabilità della fonte.
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- 10.772
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- ultimo aggiornamento
- 23 set 2026
sickn33data-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
sickn33database-migration
Master database schema and data migrations across ORMs (Sequelize, TypeORM, Prisma), including rollback strategies and zero-downtime deployments.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
sickn33distributed-tracing
Implement distributed tracing with Jaeger and Tempo for request flow visibility across microservices.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
sickn33e2e-testing-patterns
Build reliable, fast, and maintainable end-to-end test suites that provide confidence to ship code quickly and catch regressions before users do.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
sickn33fastapi-templates
Create production-ready FastAPI projects with async patterns, dependency injection, and comprehensive error handling. Use when building new FastAPI applications or setting up backend API projects.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
sickn33github-actions-templates
Production-ready GitHub Actions workflow patterns for testing, building, and deploying applications.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
sickn33ios-developer
Develop native iOS applications with Swift/SwiftUI. Masters iOS 18, SwiftUI, UIKit integration, Core Data, networking, and App Store optimization.
- installazioni
- 1
- GitHub Stars
- 46,6K
- Aggiornato
- 20 set
wshobsonaccessibility-compliance
Implement WCAG 2.2 compliant interfaces with mobile accessibility, inclusive design patterns, and assistive technology support. Use when auditing accessibility, implementing ARIA patterns, building for screen readers, or ensuring inclusive user experiences.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsonbazel-build-optimization
Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsonbinary-analysis-patterns
Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing static analysis on binaries.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsoncheckpoint-promotion
Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships, or when a promoted model needs re-gating against updated goldens.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsoncontext-driven-development
- Creates and maintains project context artifacts (product.md, tech-stack.md, workflow.md, tracks.md) in a conductor/ directory. Scaffolds new projects from scratch, extracts context from existing codebases, validates artifact consistency before implementation, and synchronizes documents as the project evolves. Use when setting up a project, creating or updating product docs, managing a tech stack file, defining development workflows, tracking work units, onboarding to an existing codebase, or running project scaffolding.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsondata-quality-frameworks
Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsondata-storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsondataset-curation
Prepare, format, and validate datasets for supervised fine-tuning and preference training. Use when converting raw data into training format, applying chat templates, configuring sequence packing, generating synthetic training data, or writing a dataset card before a run.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsondeployment-pipeline-design
Design multi-stage CI/CD pipelines with approval gates, security checks, and deployment orchestration. Use this skill when designing zero-downtime deployment pipelines, implementing canary rollout strategies, setting up multi-environment promotion workflows, or debugging failed deployment gates in CI/CD.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsonemployment-contract-templates
Create employment contracts, offer letters, and HR policy documents following legal best practices. Use when drafting employment agreements, creating HR policies, or standardizing employment documentation.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsoneval-harness-first
Build the evaluation harness that gates every fine-tuning run — golden sets, per-failure-mode graders, judge calibration, and base-model baselines. Use when starting a fine-tuning effort, when converting traces into an eval set, or when calibrating a judge against human labels.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsonfinetuning-method-selection
Decide whether to fine-tune at all, and route to the right method (SFT, DPO/ORPO/KTO, GRPO/RLVR, continued pretraining) and base model. Use when starting any fine-tuning effort, when unsure whether RAG or prompting would suffice, or when choosing between preference-optimization and reinforcement methods.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsongitlab-ci-patterns
Build GitLab CI/CD pipelines with multi-stage workflows, caching, and distributed runners for scalable automation. Use when implementing GitLab CI/CD, optimizing pipeline performance, or setting up automated testing and deployment.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsongo-concurrency-patterns
Master Go concurrency with goroutines, channels, sync primitives, and context. Use when building concurrent Go applications, implementing worker pools, or debugging race conditions.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsongrpo-rlvr-training
Train reasoning and verifiable-task behavior with GRPO and reinforcement learning from verifiable rewards (RLVR). Use when task success is algorithmically checkable (math, code, tool calls, structured output), when designing GRPO reward functions, or when a GRPO run diverges or reward-hacks.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsonhads
Use when writing technical documentation that needs to be readable by both humans and AI models, converting existing docs to HADS format, validating a HADS document, or optimizing documentation for token-efficient AI consumption.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set
wshobsonincident-runbook-templates
Create structured incident response runbooks with step-by-step procedures, escalation paths, and recovery actions. Use this skill when building a service outage runbook for a payment processing system; creating database incident procedures covering connection pool exhaustion, replication lag, and disk space alerts; onboarding new on-call engineers who need step-by-step recovery guides written for a 3 AM brain; or standardizing escalation matrices across multiple engineering teams.
- installazioni
- 1
- GitHub Stars
- 39,8K
- Aggiornato
- 19 set