By pipecrew-ai
Orchestrates end-to-end feature delivery across multiple repositories: discovers project structure, generates code for backend/frontend/infra, reviews implementations, and assesses cross-repo integration. Supports Spring Boot, React, Next.js, NestJS, FastAPI, Flask, Django, Python workers, AWS CDK, and Terraform stacks.
Reads a list of repo paths and infers cross-repo topology from the code itself — OpenAPI specs, HTTP client calls, event publish/consume, DB connections, IAM/cross-stack references. Produces two Mermaid diagrams (high-level + detailed) WITHOUT requiring a pre-existing platform.md or config.json. Use when no workspace metadata exists, when a workspace was onboarded with an older plugin version, or when you want a code-grounded second opinion against an existing platform.md. Inputs the caller must provide: - repo_paths: list of absolute repo paths to scan - workspace_name (optional): label for the diagram title — defaults to the parent directory name of the first repo - output_mode: 'canonical' (emit both architecture-overview.mmd + architecture.mmd) | 'topic' (emit a single focused diagram) | 'audit' (read-only staleness report against existing diagrams) - topic (required when output_mode = topic): name of the focused concern (e.g., 'auth-flow', 'event-flow') - existing_diagrams (required when output_mode = audit): paths to existing .mmd files to compare against
Reviews AWS CDK (TypeScript / Python) stack implementations for INFRASTRUCTURE_IMPACT compliance, security defaults (encryption, IAM least-privilege, removalPolicy), cross-stack references, tagging, and test coverage. Produces a structured report with findings grouped by severity. Read-only — `cdk synth` / `cdk diff` output is consumed as a verification artifact alongside the source diff.
Implements new AWS CDK stacks in a TypeScript CDK project (S3 buckets, SQS queues, event notifications, IAM, CORS, Lambda functions, CloudFront, etc.). Reads the target repo's CLAUDE.md (and any context files it points to) plus existing stacks for naming conventions, stage/region handling, and resource patterns. Use for any TypeScript CDK repo. Inputs the caller must provide: - repo_path: absolute path to the CDK repo worktree - stack_name: the new stack's canonical name pattern (e.g., my-feature-{stage}{regionSuffix}) - requirements: what resources the stack must contain, with cross-references to other stacks/services - fix_list (optional): file:line targets with exact changes
Manages agent-facing context files (CLAUDE.md, agent-context/). Five modes: full (agent-context/ + CLAUDE.md, role-dispatched), claude-only (CLAUDE.md standalone), init (legacy — agent-context/ only), refresh (update after a feature ships), audit (staleness report, no writes).
Implements HTTP endpoints, views, serializers, models, migrations, and tests in a Django / Python service. Supports both API-first (OpenAPI spec present, typically via DRF + drf-spectacular) and code-first (no spec — architect inlines the endpoint contract) modes. Reads the target repo's CLAUDE.md for conventions, reads the existing code for patterns, then implements end-to-end. Inputs the caller must provide: - repo_path: absolute path to the target repo worktree - spec_policy: 'api-first' | 'code-first' - spec_file: relative path to the OpenAPI spec (only when spec_policy=api-first) - inline_contract: for code-first, the endpoint contract (method, path, request/response shape, status codes) extracted from the architect's API_DESIGN - feature_summary: one paragraph - requirements: FR/EC list - endpoints_to_implement: list of endpoint paths + methods - fix_list (optional): file:line targets for fix rounds
Standalone cross-repo assessment. Checks a feature branch across all repos for cross-repo integration: wire-shape agreement, requirement enforcement symmetry, event/infra wiring. Use for verifying manually-implemented features or pre-merge validation.
Audit or refresh PipeCrew context docs at three scopes: a single repo (its agent-context/, CLAUDE.md, and DESIGN_SYSTEM.md if frontend), the workspace (platform.md), or everything (workspace + every repo). Audit mode reports staleness only; refresh mode updates docs to match current code.
PipeCrew delivers a feature end-to-end across all workspace repos. Orchestrates: requirements → architecture → spec editing → parallel implementation (backend + frontend + mock + infra) → cross-repo assessment. Usage: /deliver <feature description>. Reads workspace config from {workspace_root}/{workspace}/config.json.
Discover a new project for the PipeCrew. Inspects repos, interrogates the domain, generates workspace config + platform.md + CLAUDE.md files + domain-specific agents. Run once per project, then use /deliver to ship features.
Generate or refresh architecture diagrams for a workspace. Default mode regenerates the two canonical Mermaid files (architecture-overview.mmd + architecture.mmd) for an onboarded workspace by re-dispatching the solution-architect in discovery mode. Optional --topic flag produces a focused diagram on a specific concern (auth flow, event flow, deploy topology, etc.) without touching the canonical files. Reuses the same conventions documented in rules/discovery-diagrams.md that /discover uses. Standalone — does not run the rest of /discover.
Uses power tools
Uses Bash, Write, or Edit tools
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
██████ ██████ ██████ ██████ █████ ██████ ██████ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██ ██████ ██ ██████ █████ ██ ██████ █████ ██ █ ██ ██ ██ ██ ██ ██ ██ ██ ██ ███████ ██ ██████ ██ ██████ █████ ██ ██ ██████ ██ ██
A self-learning, multi-repo agent crew for Claude Code. Hand it one feature; it ships across every repo that feature touches — engineering its own context and learning your platform, so every run starts smarter than the last.
Website · Install · Quick start · Skills · Agents · Supported stacks
Not a faster one-shot agent — a crew that fans out across your repos, engineers its own context, and gets sharper every run. One feature in, PRs across every repo out.
Nothing a one-shot agent learns survives the session. Your platform's conventions, the gotchas, the way you always do it — re-explained every run, to every agent, like onboarding a new hire on a loop. And the moment a feature spans more than one repo, the agent that "finished" the backend has no idea the frontend and the contract drifted out from under it.
Describe a feature in plain language. PipeCrew figures out which repositories it touches — and how — then runs a crew of stack-specialized agents that take it from requirements to merged PRs in every affected repo, and feeds the result back so the next run is smarter.
one feature ─► product-owner ─► architect ─► contracts/specs ─► [ backend │ frontend │ mock │ infra ]
(parallel, one specialist per repo)
─► per-repo review ─► cross-repo assess ─► PRs in every repo ─┐
│
└──────────────── learns from the merged PR ◄────────────────┘
You stay the director, approving at gates. The orchestrator job moves to PipeCrew.
Each repo draws an implementer that knows its stack — and a reviewer that knows it too. Every agent works in its own git worktree, all building against the same shared contract at once, so a feature spanning five repos moves like one. They never talk — they only read the contract.
Per-repo reviewers run in parallel; a workspace-level assessor reads every diff together and catches the API/consumer mismatch no single per-repo reviewer can see — before PRs land, not on Monday.
State lives in files, not the chat. Each agent loads only the slice it needs into its task window, does the work, emits a machine-readable result — and the window is gone when the task ends.
Every run makes the next one sharper.
Feed a merged PR (or a run, or a diff) back, and PipeCrew proposes tier-classified updates to its durable layer — repo, workspace, or plugin memory — which you approve per finding. The durable layer lives in a shared, GitHub-backed repo, so everyone's crew benefits from what anyone's run learned. Run #2 beats run #1.
claude plugin install https://github.com/pipecrew-ai/pipecrew
PipeCrew ships new versions as GitHub Releases. To pull the latest:
/plugin marketplace update pipecrew # refresh the catalog from GitHub
/plugin install pipecrew@pipecrew # re-fetch the plugin at the new version
/reload-plugins # activate it in the running session
npx claudepluginhub pipecrew-ai/pipecrew --plugin pipecrewFull-stack agents — frontend, backend, API, DevOps architects
Opinionated engineering agent team with progressive-disclosure specs for Python / TypeScript / Go monorepos. Ships PA / SWE / Tester / PR Reviewer / On-Call sub-agents, /squid-plan / /squid-implement-task / /squid-implement-night pipelines, and a library of language/framework/infra specs that agents load on-demand.
Multi-agent orchestration for code that matters.
Scaffold new projects and add features with best-practice templates
Engineering discipline layer for Claude Code — 5 workflows, 69 commands, 21 rules, 29 skills, 9 agents organized in 12 packs
Zenith (Forerunner) - Complete full-stack development and infrastructure automation. FastAPI, React/Vite, Ansible, Terraform, Kubernetes with production-ready templates and CI/CD pipelines.