By Richyboy170
Autonomous experiment loop that optimizes any file by a measurable metric. 5 slash commands, 8 evaluators, configurable loop intervals (10min to monthly).
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
Start an autonomous experiment loop with user-selected interval (10min, 1h, daily, weekly, monthly). Uses CronCreate for scheduling. Use when the user runs /ar:loop or asks to run an autoresearch experiment continuously on a schedule.
Resume a paused experiment. Checkout the experiment branch, read results history, continue iterating. Use when the user runs /ar:resume or asks to pick up a previously started autoresearch experiment.
Run a single experiment iteration. Edit the target file, evaluate, keep or discard. Use when the user runs /ar:run or asks for one manual autoresearch iteration.
Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:setup or asks to start optimizing a file with the autoresearch loop.
Uses power tools
Uses Bash, Write, or Edit tools
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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.
| 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 |
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 | 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 |
npx claudepluginhub richyboy170/agentic-sdlc-internship --plugin autoresearch-agentWorkflow-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).
End-to-end Kubernetes Operator discipline: CRD design, reconcile-loop patterns, and OperatorHub Capability Levels. Ships CRD validator, reconcile-loop linter, and capability auditor (3 stdlib Python tools), 4 references on the operator pattern + CRD design + reconcile patterns + framework comparison (controller-runtime/kubebuilder/operator-sdk/metacontroller/KOPF), CRD + Go controller skeletons, and /operator-audit slash command. NOT a generic k8s skill — specifically the Operator pattern.
Hypothesis testing, A/B experiment analysis, sample size calculation, and confidence intervals. 3 stdlib-only Python tools with Z-test, t-test, chi-square, effect sizes, power analysis, and Wilson score intervals.
Active coding discipline enforcer based on Karpathy's 4 principles: surface assumptions, keep it simple, make surgical changes, define verifiable goals. Ships 4 Python tools (complexity_checker, diff_surgeon, assumption_linter, goal_verifier), a review agent, /karpathy-check slash command, and a pre-commit hook. All tools stdlib-only.
End-to-end chaos engineering discipline: design experiments with hypothesis + steady-state metric + blast radius + abort criteria, calculate risk score against error budget, and generate blameless postmortems. 3 stdlib Python tools (experiment_designer, blast_radius_calculator, experiment_postmortem), 4 references on chaos principles + experiment design + 7-attack taxonomy + tooling landscape (Chaos Toolkit/Mesh/Litmus/Gremlin/AWS FIS/DIY), templates for plans + postmortems, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (chaos targets).
Ultra-compressed communication mode. Cuts ~75% of tokens while keeping full technical accuracy by speaking like a caveman.
Comprehensive UI/UX design plugin for mobile (iOS, Android, React Native) and web applications with design systems, accessibility, and modern patterns
Multi-model consensus engine integrating OpenAI Codex CLI, Gemini CLI, and Claude CLI for collaborative code review and problem-solving.
Curate auto-memory, promote learnings to CLAUDE.md and rules, extract proven patterns into reusable skills.