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engineering-skills

Name: engineering-skills
Author: richyboy170

By Richyboy170

32 production-ready engineering skills: architecture, frontend, backend, fullstack, QA, DevOps, security, AI/ML, data engineering, Playwright, self-improving agent, security suite (adversarial-reviewer, ai-security, cloud-security, incident-response, red-team, threat-detection), Stripe integration, TDD guide, Google Workspace CLI, a11y audit, Snowflake development, and more. v2.8.1 augments senior-fullstack / senior-frontend / senior-backend with karpathy-coder + Matt Pocock discipline: each ships a 7-question forcing-question library, 4 customization profiles (JSON, swappable), a deterministic decision engine, a composition map into POWERFUL specialists, plus cs-fullstack-engineer / cs-frontend-engineer / cs-backend-engineer orchestrator agents (context: fork) invokable by other agents via /cs:fullstack-review, /cs:frontend-review, /cs:backend-review, /cs:engineer-grill. Agent skill and plugin for Claude Code, Codex, Gemini CLI, Cursor, OpenClaw.

npx claudepluginhub richyboy170/agentic-sdlc-internship --plugin engineering-skills

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What's Inside

Skills32

adversarial-reviewer

/adversarial-reviewer

Adversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.

ai-security

/ai-security

Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

aws-solution-architect

/aws-solution-architect

Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, and cost optimization.

azure-cloud-architect

/azure-cloud-architect

Design Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, and cost optimization.

cloud-security

/cloud-security

Use when assessing cloud infrastructure for security misconfigurations, IAM privilege escalation paths, S3 public exposure, open security group rules, or IaC security gaps. Covers AWS, Azure, and GCP posture assessment with MITRE ATT&CK mapping.

README

BMAD-Method Agentic SDLC

Spec-driven, artifact-first software delivery for deep agents.

BMAD-Method Agentic SDLC is a layered software development lifecycle for agentic delivery. It uses Claude Code agents, specialist personas, BMAD skills, commands, and optional deterministic workflows to take software work from idea discovery through PRD, architecture, evaluation, story implementation, QA review, and delivery.

This repository is not primarily a website or app. Generated apps, app/, and sandbox/ are working surfaces and experiments. The core product is the agentic SDLC system.

At A Glance

Layer	Role	Examples
Agent teams	Own lifecycle phases and specialist perspectives	`cs-engineering-lead`, `cs-planning-lead`, `code-reviewer`
Skills	Execute repeatable methods and quality gates	`bmad-prd`, `bmad-dev-story`, `bmad-testarch-nfr`
Commands	Provide user-facing entry points	`/cs:workflow-build`
Artifacts	Carry context across phase boundaries	`prd.md`, `.decision-log.md`, `sprint-status.yaml`
Workflow automation	Makes repeatable multi-agent runs deterministic	`.claude/workflows/*.js`

Lifecycle

flowchart TD
  U[User goal, raw idea, or artifact paths] --> L[cs-engineering-lead]

  L --> R[Phase 1: Research and discovery]
  R --> P[Phase 2: PRD and requirements planning]
  P --> A[Phase 3: Architecture, stories, readiness]
  A --> E[Phase 4: Story implementation]
  E --> Q[Phase 5: Independent QA review]
  Q --> D[Final delivery digest]

  R -. owns .-> RL[cs-brainstorm-research-lead]
  P -. owns .-> PL[cs-planning-lead]
  A -. owns .-> PL
  E -. routed by .-> L
  Q -. dispatched by .-> L

  D --> NX[Next handoff or release decision]

The lifecycle is intentionally gated. Agents do not just chat toward code; they create artifacts, hand them off, verify them, and keep implementation tied to specs and acceptance criteria.

Phase Map

Phase	Coordinator	Specialists	Primary Skills	Output Artifacts
1. Research and discovery	`cs-brainstorm-research-lead`	`cs-market-researcher`, `cs-tech-researcher`, `cs-problem-solver`, `cs-innovation-strategist`, `cs-visual-researcher`, `cs-concept-synthesizer`, plus `cs-design-thinker` / `cs-ideation-strategist` when interactive facilitation is needed	`bmad-brainstorming`, `bmad-market-research`, `bmad-domain-research`, `bmad-technical-research`, `bmad-prfaq`, `bmad-product-brief`	Locked problem and ICP, alternatives, evidence, wedge, risks, assumptions, validation test, visual manifest when applicable
2. PRD and requirements planning	`cs-planning-lead`	`cs-concept-to-prd-planner`, `cs-requirements-architect`, `cs-prd-work-planner`, `cs-evaluation-architect`, `cs-prd-quality-reviewer`, `cs-epic-story-planner`	`bmad-prd`, `bmad-ux`, `bmad-testarch-test-design`, `bmad-testarch-nfr`, `bmad-review-edge-case-hunter`	`prd.md`, `addendum.md`, `.decision-log.md`, FR/NFR/UJ IDs, evaluation spine, open questions
3. Architecture, stories, readiness	`cs-planning-lead`	Planning specialists plus architecture and story workflows	`bmad-create-architecture`, `bmad-agent-architect`, `bmad-create-epics-and-stories`, `bmad-create-story`, `bmad-story-automator-review`, `bmad-check-implementation-readiness`, `bmad-sprint-planning`, `bmad-sprint-status`	Architecture artifacts, ADR/design decisions, epics, ready-for-dev stories, `sprint-status.yaml`, readiness verdict
4. Story implementation	`cs-engineering-lead`	`cs-frontend-engineer`, `cs-backend-engineer`, `cs-fullstack-engineer`, `cs-senior-engineer`, `cs-karpathy-reviewer`	`bmad-dev-story`, `bmad-code-review`, `bmad-quick-dev`, `bmad-testarch-atdd`, `bmad-testarch-trace`, `bmad-testarch-test-review`, `bmad-checkpoint-preview`	Changed files, tests/checks run, implementation notes, file list, story status moved to review
5. Independent QA review	`cs-engineering-lead`	`code-reviewer`, `security-auditor`, `test-engineer`, `web-performance-auditor`	`code-review-and-quality`, `security-and-hardening`, `test-driven-development`, `performance-optimization`, `browser-testing-with-devtools`	Findings by severity, coverage gaps, security risks, performance risks, approve/request-changes verdict

Detailed Flow

Phase 1: Research and discovery

View full README on GitHub

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More by Richyboy170

autoresearch-agent

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Autonomous experiment loop that optimizes any file by a measurable metric. 5 slash commands, 8 evaluators, configurable loop intervals (10min to monthly).

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Workflow-builder skill: design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool (CLAUDE_CODE_WORKFLOWS=1, /workflows). Every session opens with an intake question set; when the user is vague, a stdlib recommendation engine infers and proposes a topology with rationale instead of stalling. Ships 3 stdlib Python tools (intake recommendation engine, .js validator enforcing the pure-literal-meta / no-non-determinism / guarded-loop / parallel-thunk rules, topology scaffolder), 3 references citing 7-8 authoritative sources each (full API surface, orchestration patterns, decision + intake guide), templates + a runnable example, cs-workflow-architect persona agent + /cs:workflow-build slash command. Use when building, scaffolding, or running a custom Claude Code workflow or orchestrating sub-agents (fan-out, pipeline, loop, judge-panel).

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Richyboy170

engineering-skills

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What's Inside

README