Claude Skills by paulasilvatech

Author current GitHub Copilot agents, instructions, and VS Code prompts in this repository. Use when asked to create or update a known primitive type with repository governance, dated evidence, canonical sources, templates, synchronization, and type-specific validation.

Create, audit, repair, and improve GitHub Copilot Agent Skills for VS Code, GitHub Copilot CLI, and GitHub Copilot cloud agent. Use when a user asks to create a skill, generate a SKILL.md, convert a workflow into a reusable skill, audit an existing skill, fix skill loading issues, optimize a skill description, validate frontmatter, or add references/scripts/assets to a skill package.

Author current GitHub Copilot agents, instructions, and VS Code prompts in this repository. Use when asked to create or update a known primitive type with repository governance, dated evidence, canonical sources, templates, synchronization, and type-specific validation.

Create, audit, repair, and improve GitHub Copilot Agent Skills for VS Code, GitHub Copilot CLI, and GitHub Copilot cloud agent. Use when a user asks to create a skill, generate a SKILL.md, convert a workflow into a reusable skill, audit an existing skill, fix skill loading issues, optimize a skill description, validate frontmatter, or add references/scripts/assets to a skill package.

Use when validating requirements against EARS notation patterns. Triggers include \"EARS\", \"requirement review\", \"requirement quality\", \"shall statement\", and \"REQ-ID\".

Use when reviewing Terraform, Bicep, or CloudFormation, checking drift, or hardening infrastructure code. Triggers include \"review terraform\", \"review bicep\", \"IaC review\", \"drift detection\", and \"state file\".

Review existing PostgreSQL SQL, schema, and functions for PostgreSQL-specific anti-patterns, quality, and security — JSONB operations, array usage, custom types, schema design, function optimization, and Row Level Security (RLS). Use when the user asks to review, audit, or critique existing PostgreSQL code or a migration. To author or optimize new PostgreSQL features, use postgresql-optimization.

Create, audit, repair, and improve GitHub Copilot Agent Skills for VS Code, GitHub Copilot CLI, and GitHub Copilot cloud agent. Use when a user asks to create a skill, generate a SKILL.md, convert a workflow into a reusable skill, audit an existing skill, fix skill loading issues, optimize a skill description, validate frontmatter, or add references/scripts/assets to a skill package.

Scaffold a new GitHub Copilot Agent Skill using the skill-creator skill workflow.

Run the AgentRC AI-readiness assessment for the current repository, optionally apply a policy, and produce a self-contained HTML dashboard at reports/index.html. Use when asked to assess, audit, score, or report how AI-ready a repo is.

Generate tailored AI agent instruction files with the AgentRC instructions command, including .github/copilot-instructions.md, AGENTS.md, scoped .github/instructions/*.instructions.md files with applyTo globs, and optional CLAUDE.md output. Use after /acreadiness-assess to close AI Tooling gaps or when the user wants to create, regenerate, refresh, preview, or choose output options for custom instructions.

Help the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.

Design governance, safety, policy enforcement, trust scoring, and audit controls for AI agent systems. Use when building agents with external tools, policy-based tool access, semantic intent classification, dangerous prompt detection, multi-agent trust workflows, rate limits, content filters, or audit trails across PydanticAI, CrewAI, OpenAI Agents, LangChain, or AutoGen.

Evaluate AI agent systems against OWASP Agentic Security Initiative Top 10 controls. Use when asked whether an agent is OWASP ASI compliant, to check ASI compliance, run an agentic security audit, map controls to ASI-01 through ASI-10, or generate a compliance report before production deployment.

Verify supply chain integrity for AI agent plugins, MCP servers, tools, and dependencies by generating SHA-256 manifests, verifying installed files, auditing pinned versions, and enforcing promotion gates. Use when asked to verify plugin integrity, generate INTEGRITY.json, check supply chain, detect tampering, audit dependency pinning, sign this plugin, or promote dev → staging → production.

Enforce AppSec audit integrity for security analysis, code review, threat modeling, SAST, SCA, and quality scan agents. Use this skill when running a post-analysis quality gate, preventing rationalization, retrying failed evidence collection, running a second-pass self-critique, scoring output from 1-10 with a ≥8 threshold, or recording governed lessons or memories.

Bootstrap and run a lightweight multi-agent development team. Use when starting or adopting a project, planning multi-step work, coordinating implementation with optional QA, brainstorming with distinct perspectives, or preserving PROJECT_BRIEF context across sessions.

Configure and run CodeQL code scanning with GitHub Actions workflows, default or advanced setup, CodeQL CLI databases, SARIF uploads, custom query packs, monorepo categories, build modes, and alert triage. Use this skill when the user asks to create or customize codeql.yml, choose CodeQL setup, configure a language matrix, run codeql database create or database analyze, upload SARIF, troubleshoot CodeQL builds, or interpret code scanning alerts.

Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast...

Apply GDPR-compliant engineering practices across code, APIs, data models, authentication, logging, retention, deletion jobs, cloud infrastructure, and pull requests. Use this skill when handling personal data, user accounts, cookies, analytics, emails, audit logs, encryption, pseudonymization, anonymization, data exports, breach response, CI/CD pipelines with real data, or questions asking whether a design is GDPR-compliant.

Configure and manage GitHub secret scanning, push protection, custom patterns, exclusions, alert triage, remediation, bypass workflows, and pre-commit secret scans through the Advanced Security plugin. Use this skill when enabling secret scanning, resolving blocked pushes, writing secret_scanning.yml, creating regex patterns, triaging alerts, or scanning code before committing.

Scan codebases and files for exploitable security vulnerabilities by tracing data flows, dependencies, secrets, authentication, authorization, injection, cryptography, and business logic issues. Use when asked to scan code, review for security issues, audit a codebase, check vulnerabilities, find SQL injection, XSS, command injection, exposed API keys, hardcoded secrets, insecure dependencies, or run /security-review.

Produce full or incremental STRIDE-A threat models for repositories and systems, including architecture overviews, DFD diagrams, findings, STRIDE heatmaps, and executive assessment. Use when the user asks to threat model a repo, refresh an existing threat-model-* report, compare commits or reports, identify trust boundary risks, or map findings to CVSS 4.0, CWE, and OWASP.

Creates valid Microsoft Threat Modeling Tool (.tm7) files compatible with the Microsoft Threat Modeling Tool v7.3+. Use when asked to create, generate, or modify a .tm7 threat model file, or when performing STRIDE threat modeling that should output a .tm7 file that opens cleanly in the Microsoft Threat Modeling Tool.

Two related workflows for a locally-cloned codebase, in one skill. Use this skill when enforced*** — i.e. whether any workflow is a **required status check /; branch-protection rule** that actually blocks merges, versus one that merely; runs — since that distinction is a manual, human-configured setting that.

Create, list, inspect, update, and delete Arize AI integrations that store LLM provider credentials for evaluators and Arize features. Use this skill when connecting OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM, or custom providers to Arize; managing AI integration credentials; listing integrations; rotating keys; deleting integrations; or finding integration IDs.

Create, inspect, update, and use Arize annotation configs and annotation queues, then bulk-apply human labels to spans with the Python SDK. Use when asked for "annotation config", "annotation queue", "label schema", "human feedback", "bulk annotate spans", "update_annotations", "labeling queue", "annotate record", or "human review".

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

Create, update, and run Arize LLM-as-judge evaluators and tasks for spans, traces, sessions, projects, datasets, and experiments. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or evaluator prompt improvement.

Create, run, export, compare, delete, and troubleshoot Arize experiments with the ax CLI, including real model inference, run files, evaluations, result analysis, and dataset-linked experiment workflows. Use this skill when the user asks to create experiment, run experiment, compare models, evaluate AI, benchmark prompts, A/B test models, export experiment results, measure accuracy, or inspect experiment runs in Arize.

Adds Arize AX tracing to an LLM application for the first time. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or OpenInference, or get started with Arize tracing.

Generate Arize UI deep links for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs using base64 org and space IDs, resource IDs, and trace/session time windows. Use when the user wants to link to, open, share, or debug an Arize trace, span, session, dataset, queue, evaluator, or annotation config.

Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.

Download, export, inspect, and root-cause existing Arize traces, spans, sessions, errors, prompts, retrieval documents, model calls, and behavior regressions with the ax CLI. Use when asked to look at existing trace data, export traces by trace ID, export spans by span ID, download a session, investigate LLM app runtime issues, or analyze Arize behavior regressions.

Analyze a screen recording of a repetitive manual workflow, extract frames and optional narration, reconstruct the process, and produce tested automation scripts. Use this skill when the user asks to automate a recorded task, turn a screen recording into a script, replace repetitive browser, spreadsheet, email, file-management, terminal, or macOS GUI steps, or create dry-run automation.

Suggest relevant GitHub Copilot custom agent files from the github/awesome-copilot repository by comparing repository context with available agents, detecting already installed or outdated local .github/agents/*.agent.md files, and presenting a no-install table. Use when the user asks to suggest awesome GitHub Copilot custom agents, find agents to add, compare local agents with awesome-copilot, or update outdated custom agents.

Suggest relevant GitHub Copilot instruction files from the awesome-copilot repository by comparing repository context, chat needs, local .github/instructions files, and remote versions. Use this skill when the user asks what Copilot instructions to add, whether local instructions are outdated, or how to install/update awesome-copilot instructions without duplicates.

Suggest relevant GitHub Copilot Agent Skills from the awesome-copilot repository by comparing remote skills with local repository skills, detecting missing or outdated skills, bundled assets, duplicates, and repository-fit gaps. Use when the user asks which awesome-copilot skills to install, update, compare, or recommend for the current repository.

Analyze AWS resources used in the app (IaC files and/or resources in a target account/region) and optimize costs - creating GitHub issues for identified optimizations. Use this skill when the user asks for prerequisites.

Diagnose AWS resource health with AWS CLI, CloudWatch metrics, CloudWatch Logs Insights, Performance Insights, CloudTrail correlation, severity classification, root cause analysis, and remediation plans. Use when the user asks for AWS resource health, issue diagnosis, CloudWatch troubleshooting, or remediation for EC2, Lambda, RDS, ECS, ALB, DynamoDB, SQS, or API Gateway.

Answer natural-language questions about AWS resources by running strictly read-only AWS CLI queries. Use when asking about EC2, S3, RDS, Lambda, ECS, EKS, Secrets Manager, IAM, VPC, networking, messaging, `aws lambda list-event-source-mappings --query 'EventSourceMappings[].[FunctionArn,EventSourceArn,State,BatchSize]' --output table`, or current-state inventory.

Review AWS workloads against the AWS Well-Architected Framework across Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. Use when the user asks for an AWS well-architected review, IaC architecture assessment, WAF findings, risk classification, or GitHub issue remediation plan.

Design evaluator-optimizer, reflection, rubric, LLM-as-judge, and test-driven refinement loops for AI agent outputs. Use when implementing self-critique, iterative improvement, quality gates, agent response evaluation, code refinement, or rubric-based scoring for generated code, reports, or analysis.

Design Azure API Management as the runtime AI gateway for model and tool traffic, including token-per-minute controls, token limits, quotas, multi-backend load-balanced backend pools, circuit breakers, semantic caching, token metrics, managed identity, and content safety. Use when fronting model deployments, enforcing token budgets, adding semantic cache, load balancing LLM endpoints, or governing tool API calls at runtime.

Provides a decision framework for production multi-agent and AI-native systems on GitHub and Azure AI Foundry. Use this skill when designing model routing, prompt caching, semantic caching, memory, context curation, RAG, tools, MCP, identity, guardrails, evaluation, observability, or cost controls.

Build on Azure AI services including AI Search, Speech, Azure OpenAI, and Document Intelligence, covering keyword, vector, hybrid, and semantic search, speech-to-text, text-to-speech, transcription, and OCR. Use when the user asks about AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcription, OCR, or converting text to speech.

Design Azure API Center as the enterprise inventory and governance plane for APIs, agent tools, OpenAPI definitions, environments, deployments, metadata, linting, and MCP server discovery. Use when building an API or tool catalog, enforcing API standards, registering MCP servers, or making approved tools discoverable to agent builders.

Use when designing or provisioning Azure Managed Redis for cache, semantic cache, vector memory, session store, or agent memory in AI-native systems; produces SKU guidance, network and identity controls, Bicep deployment steps, and integration recommendations. DO NOT USE FOR: general agent architecture (use agentic-architecture-patterns), Foundry agent runtime design (use foundry-agent-blueprint), or general Azure infrastructure (use azure-infrastructure). Triggers include "design Redis seman...

Create Agent Users in Microsoft Entra ID from Agent Identities, enabling AI agents to act as digital workers with user identity capabilities in Microsoft 365 and Azure environments. Use this skill when the user asks for skill: creating agent users in microsoft entra agent id.

Create, register, deploy, update, and synchronize prompt-based Azure AI Foundry agents from a local JSON manifest using the Agent Service REST API. Use when users ask to sync Foundry agents, create agents in Foundry, push agents, update agent instructions, scaffold a foundry-agents.json manifest, or add sync scripts.