Prompts

Generate comprehensive acceptance criteria for user stories using Given-When-Then format, covering happy paths, edge cases, and error conditions.

Generate first-draft AIRB documentation sections from project inputs, including architecture, data flow, PIA, and monitoring plans.

Perform a comprehensive risk assessment for AI/LLM systems to determine AIRB tier classification and required governance controls.

Prepare a complete AIRB submission package and checklist for a UAIS/LLM project following RAI Development Guide v3.0 requirements.

Comprehensive analysis of existing testing infrastructure mapped to pipeline stages (left-to-right), identifying gaps, overlaps, and optimization opportunities

Interactive guide for creating new Ansible playbooks that execute in AWX, following Epic on Azure patterns for role integration, vault secrets, and testing workflows.

Guide for managing role versions in requirements.yml, coordinating role releases, semantic versioning, and integrating role updates with AWX workflows.

Interactive guide for creating new Ansible roles following Epic on Azure standards, including proper structure, Molecule testing, and requirements.yml integration.

Guide through creating a new AWX job template using the ansible_role_awx_cac CaC model, including all required fields and best practices.

Diagnostic and resolution guide for common AWX job failures, credential issues, project sync problems, and operational errors in Epic on Azure.

Guide testing a playbook change using AWX's scm_branch override without modifying the job template, following Epic on Azure safety patterns.

Guide coordinated testing of Ansible role changes using feature branches in both the role repo and playbooks repo, following Epic on Azure patterns.

Analyze Azure IaC and deployed resources to identify cost optimizations and produce an actionable remediation plan (optionally via GitHub issues).

Analyze an Azure resource’s health, diagnose issues using logs and telemetry, and produce a remediation plan for identified problems.

Facilitates backlog grooming sessions by analyzing issues for readiness, suggesting improvements to acceptance criteria, estimating complexity, and identifying dependencies before sprint planning.

Analyze bias/fairness test results and propose mitigations aligned with Optum RAI guidance for AIRB submission.

Generate burndown charts and forecast completion dates for sprints and epics based on current velocity, scope changes, and historical trends.

Automatically generate semantic, user-facing changelogs from merged PRs and closed issues, categorized by feature, fix, breaking change, and deprecation.

Create an AGENTS.md file for the current repository with secure and compliant Optum guidance.

Generate high-quality, megadoc-compliant documentation pages with proper front matter, Diátaxis categorization, and style guide adherence for any documentation need.

Create a concise and comprehensive README.md for the current repository.

Comprehensive standup preparation assistant that aggregates blockers, PR status, issue progress, and team updates into a concise, actionable format for daily standup meetings.

Map dependencies between issues and PRs to identify critical paths, bottlenecks, and risk areas for sprint and epic planning.

Assess deployment risks for releases based on change scope, system criticality, testing coverage, and historical incident patterns to inform go/no-go decisions.

Design a testing pipeline with progressive filtering, clear stage boundaries, optimized feedback loops, and minimal overlap between stages

Identify duplicate or overlapping pull requests and issues across repositories using similarity analysis, and recommend consolidation strategies to reduce redundant work.

Systematic triage of a Dynatrace-monitored Kubernetes service using DQL queries for entity discovery, JVM health, thread analysis, pod generation comparison, and Davis problem correlation. Produces structured root cause analysis with Splunk query handoffs for restricted log environments.

Generate comprehensive epic progress dashboards showing completion status, velocity trends, blocker analysis, and burn-down forecasts for multi-sprint initiatives and large features.

Review Terraform plan output to identify risks, validate best practices, and provide actionable feedback before apply.

Creates megadoc-compliant stub mkdocs.yml and initial docs/ structure for a repository that will be included as a submodule in ohemr-epic-megadoc.

Creates data flow diagrams showing how data moves through systems using Mermaid flowchart syntax

Creates deployment pipeline and workflow diagrams using Mermaid flowchart syntax with CI/CD focus

Creates C4 container or component diagrams from infrastructure code or system descriptions using Mermaid syntax

Implement a specific testing layer (unit, functional, integration, performance) with appropriate tooling, infrastructure, and best practices

Build comprehensive incident timelines from logs, metrics, and tickets. Produces structured chronological summaries for postmortems and RCAs.

Triage incoming issues and bugs using multi-factor scoring (severity, impact, effort) to recommend priority levels and sprint assignment.

Diagnose failing or unhealthy Kubernetes pods using cluster state, events, and logs. Produces structured root cause analysis with safe remediation recommendations.

Audit GitHub label usage across repositories to identify inconsistencies, redundancies, and missing labels, then recommend a standardized taxonomy.

Comprehensive pre-merge review checklist for pull requests with documentation changes, ensuring megadoc compliance, accuracy, completeness, and consistency before integration.

Create a Harmony-based example healthcare application that showcases eligibility, claims, and remittance concepts using current Harmony skills, instructions, navigation, forms, and components.

Use OTC AWESOME LLM tools to list, search, and download OTC assets (skills/instructions/prompts/agents) into this repo.

Generate comprehensive release readiness checklists covering code completion, testing, documentation, security, and operational readiness for production deployments.

Analyze sprint data to generate insights for retrospective discussions, identifying patterns in velocity, blockers, and team dynamics to drive process improvements.

Create and maintain operational runbooks following megadoc and Optum style guides. Produces structured, testable procedures with proper frontmatter and rollback steps.

Design a comprehensive shadow mode pilot plan for Tier 2/3 Optum AI/LLM systems with success criteria, monitoring, and go/no-go gates.

Diagnose Spring Boot container crashes in Kubernetes by correlating Dynatrace JVM telemetry, pod lifecycle events, and deployment state. Covers rolling deployment failures, OOM kills, thread exhaustion, startup failures, and major framework upgrades.

Facilitate sprint planning sessions by recommending issue prioritization, capacity allocation, and sprint goal formulation based on team velocity and backlog refinement data.

Analyze pull requests across repositories for age, review status, blockers, and patterns. Generates sprint hygiene report with actionable recommendations for standup discussions.

Deep analysis and bulk cleanup of stale pull requests and issues across repositories. Identifies candidates for closing, archiving, or escalating based on age, activity, and business value.

Generate and configure GitHub Super-Linter setup including workflow files, environment configuration, and pre-commit hooks for new or existing repositories.

Diagnostic and resolution guide for GitHub Super-Linter failures including ENV ordering, ESLint errors, CodeQL security findings, and configuration issues.

Calculate team capacity for upcoming sprints accounting for PTO, holidays, meetings, and other non-development time to enable realistic sprint commitments.

Diagnostic guide for resolving common megadoc integration problems including missing documentation, build failures, broken links, navigation issues, and monorepo plugin errors.

Walk through creating and configuring a United AI Studio (UAIS) project, including model selection, quota management, and initial risk tiering.

Comprehensive validation of megadoc-compliant documentation including stub mkdocs.yml correctness, front matter completeness, Diátaxis categorization, style guide adherence, and local build testing.

Analyze team velocity over time to identify trends, seasonal patterns, and capacity changes for more accurate sprint planning and forecasting.

Compose multiple specialized agents into a safe Wall-E workflow with proper MCP tool assignments, guardrails, and human-in-loop gates.

Recommend RAG chunking, embedding, and retrieval parameters for Wall-E contexts based on corpus characteristics and performance requirements.