By pillip
Run a PRD-driven AI development team inside Claude Code — brainstorm, spec, design, implement, review, test, and ship features with agents that enforce safety guards, git worktree isolation, and automatic PR creation.
Plan and execute migrations — DB schema changes, library upgrades, Python version transitions.
WCAG 2.1 AA accessibility audit — check color contrast, keyboard nav, screen reader support, touch targets, motion safety, and semantic structure.
Design pragmatic software architecture from PRD and requirements. Make explicit tradeoffs, not theoretical diagrams.
Interactive brainstorming agent — guides users from vague ideas to concrete problem definitions and solution directions through Socratic dialogue.
Interactive business analysis agent — validates business viability of ideas through market research, competitive analysis, and strategic critique before PRD creation.
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Activates destructive command warnings for the current session. Use when working in sensitive environments.
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Matches all tools
Hooks run on every tool call, not just specific ones
Executes bash commands
Hook triggers when Bash tool is used
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Modifies files
Hook triggers on file write and edit operations
Modifies files
Hook triggers on file write and edit operations
Uses power tools
Uses Bash, Write, or Edit tools
Uses power tools
Uses Bash, Write, or Edit tools
Turn a PRD into shipped code. 33 engineering agents + 23 skills (default install) handle the entire development lifecycle — from PRD to code review to deployment — so you can focus on what to build, not how. An optional sales pack (5 agents + 5 skills, opt-in via --pack=sales) covers account / discovery / proposal workflows for teams sharing a repo with engineering.
Positioning: trustworthy code in collaboration → AI dev team control plane. The kit's bet is that AI productivity is bottlenecked not by the model but by what surrounds it — concurrency, state, guardrails, decision capture, learning. Adding more agents doesn't close that gap; structuring the work does.
Claude Code is powerful on its own, but without structure it produces inconsistent results — skipped tests, forgotten reviews, PRs that drift from requirements. claude-kit solves this by giving Claude Code a repeatable process:
/spec → docs/specs/SPEC-NNN.md) — Problem / Options ≥2 / Trade-offs / Decision / Rollback. Sprint mode auto-runs it; non-sprint mode HOLDs for review.sprint_state.md. Crash or timeout? Just re-run /sprint to pick up where you left off.In short: claude-kit turns Claude Code from a smart assistant into a development team that follows engineering best practices.
claude-kit takes a PRD (Product Requirements Document) as input and orchestrates AI agents to support the entire development lifecycle — from requirements analysis to code review and deployment.
Core Principles:
issues.md as SSOT: Progress and completion are tracked by Status in this file/prd → /kickoff → /uiux → /sprint
# (optional pre-PRD: /brainstorm → /bizanalysis → /prd)
Start with an idea, validate it, write a PRD, generate planning docs, design the UI, and let the team-lead auto-implement everything.
/implement ISSUE-001 → /review ISSUE-001 → /ship
Pick an issue from issues.md, implement it with TDD, review with security audit, and merge.
/sprint
Team-lead picks up all ready issues, implements them in parallel, reviews each PR, ships merged code, and auto-creates follow-up issues from review findings.
/diagnose "TypeError in auth.py line 42" → /review → /ship
Trace the root cause, apply a minimal fix with regression test, review, and ship.
/testgen src/auth/
Scan for missing or hollow tests, generate unit/integration/E2E tests, and create a PR.
/scan
Reverse-engineer docs from code: architecture, requirements, test plan, and improvement issues — then feed into /sprint or /implement.
/issue "Add Stripe payment processing with subscription tiers, billing page, and invoice history"
Auto-detects that this spans multiple screens/modules, estimates issue count, updates planning and design docs incrementally (append-only), and creates a batch of implementation issues with dependencies.
/refactor src/legacy_module.py # Improve code structure
/migrate "Django 5.0" # Upgrade dependencies
/devops "github-actions" # Set up CI/CD
New project:
/prd → /kickoff → /uiux → /sprint
# (optional pre-PRD: /brainstorm → /bizanalysis → /prd)
Autonomous development methodology: PRD interviews → agent execution → automated review
A curated set of skills for each stage of development — propose, spec, design, plan, implement, ship.
Implementation planning, execution, and PR creation workflows with multi-agent collaboration
Helder's personal SDLC toolbelt for AI coding agents — from PRD to ship. Bundles the tracer-bullet workflow alongside TDD, code review, audits, and shipping skills.
Long-running agent harness with 5-layer memory architecture, GitHub integration, autonomous batch processing, Agent Teams with ATDD, 9 hooks (safety, quality gates, team coordination), and 6 Agent Skills
Autonomous GitHub Project Management workflow with orchestrator-agent coordination for parallel task execution via git worktrees