claude-sdlc-wizard
Health Warn
- No license — Repository has no license file
- Description — Repository has a description
- Active repo — Last push 0 days ago
- Community trust — 10 GitHub stars
Code Fail
- rm -rf — Recursive force deletion command in .github/workflows/weekly-update.yml
Permissions Pass
- Permissions — No dangerous permissions requested
This agent automates software development life cycle (SDLC) enforcement for Claude Code. It forces the AI to plan before coding, write tests first, and self-review its work.
Security Assessment
The tool executes shell commands and makes network requests to fetch updates and run installations. No hardcoded secrets were detected. However, there is a significant security concern: a recursive force deletion command (`rm -rf`) was found inside an automated GitHub workflow (`weekly-update.yml`). The installation process also relies heavily on curling remote scripts and piping them directly to bash (`curl ... | bash`), which is inherently risky if the repository is ever compromised.
Overall Risk Rating: Medium.
Quality Assessment
The project is actively maintained, with its most recent push happening today. It has a small but present level of community trust with 10 GitHub stars, and the repository includes a clear description. On the downside, the project completely lacks a standard open-source license. This means that legally, default copyright laws apply, and the software cannot be safely modified or redistributed for commercial or even open-source use without explicit permission from the author.
Verdict
Use with caution — the core tool is actively maintained and useful, but the missing license is a legal blocker for most professional projects, and the `rm -rf` command in automated workflows combined with remote bash execution requires careful manual review before integrating.
SDLC enforcement for Claude Code — hooks, skills, and wizard setup in one command. TDD, planning, self-review, CI shepherd.
Claude Code SDLC Wizard
A self-evolving Software Development Life Cycle (SDLC) enforcement system for AI coding agents. Makes Claude plan before coding, test before shipping, and ask when uncertain. Measures itself getting better over time.
Built on 15+ years of software engineering and founding engineering experience — battle-tested patterns from real production systems, baked into an AI agent that follows tried-and-true software quality practices so you don't have to enforce them manually.
Install
Requires Claude Code (Anthropic's CLI for Claude).
Run from your terminal or from inside Claude Code (! prefix):
npx agentic-sdlc-wizard init
Then start (or restart) Claude Code — type /exit then claude to reload hooks. Setup auto-invokes on first prompt — Claude reads the wizard doc, scans your project, and generates bespoke CLAUDE.md, SDLC.md, TESTING.md, and ARCHITECTURE.md. No manual commands needed.
curl (no npm install needed):
curl -fsSL https://raw.githubusercontent.com/BaseInfinity/claude-sdlc-wizard/main/install.sh | bash
Homebrew:
brew install BaseInfinity/sdlc-wizard/sdlc-wizard
sdlc-wizard init
GitHub CLI extension:
gh extension install BaseInfinity/gh-sdlc-wizard
gh sdlc-wizard init
From GitHub (no npm registry needed):
npx github:BaseInfinity/claude-sdlc-wizard init
Install CLI globally:
npm install -g agentic-sdlc-wizard
sdlc-wizard init
Manual: Download CLAUDE_CODE_SDLC_WIZARD.md to your project and tell Claude Run the SDLC wizard setup.
npx agentic-sdlc-wizard check # Human-readable
npx agentic-sdlc-wizard check --json # Machine-readable (CI-friendly)
Reports MATCH / CUSTOMIZED / MISSING / DRIFT for every installed file. Exits non-zero on MISSING or DRIFT — use in CI to catch setup regressions.
Check for content updates: Tell Claude Check if the SDLC wizard has updates — it reads CHANGELOG.md, shows what's new, and offers to apply changes.
Why Use This
You want Claude Code to follow engineering discipline automatically:
- Plan before coding (not guess-and-check)
- Write tests first (TDD enforced via hooks)
- State confidence (LOW = ask user, don't guess)
- Track work visibly (TaskCreate)
- Self-review before presenting
- Prove it's better (use native features unless you prove custom wins)
The wizard auto-detects your stack (package.json, test framework, deployment targets) and generates bespoke hooks + skills + docs. CI validates the generated assets; cross-stack setup-path E2E is on the roadmap.
What This Actually Is
Five layers working together:
Layer 5: SELF-IMPROVEMENT
Weekly/monthly workflows detect changes, test them
statistically, create PRs. Baselines evolve organically.
Layer 4: STATISTICAL VALIDATION
E2E scoring with 95% CI (5 trials, t-distribution).
SDP normalizes for model quality. CUSUM catches drift.
Layer 3: SCORING ENGINE
Multi-criteria scoring, 10/11 points. Claude evaluates Claude.
Before/after wizard A/B comparison in CI.
Layer 2: ENFORCEMENT
Hooks fire every interaction (~100 tokens).
PreToolUse reminds Claude to write tests first.
Layer 1: PHILOSOPHY
The wizard document. KISS. TDD. Confidence levels.
Copy it, run setup, get a bespoke SDLC.
What Makes This Different
| Capability | What It Does |
|---|---|
| E2E scoring in CI | Every PR gets an automated SDLC compliance score (0-10) — measures whether Claude actually planned, tested, and reviewed |
| Before/after A/B testing | Compares wizard changes against a baseline with 95% confidence intervals to prove improvements aren't noise |
| SDP normalization | Separates "the model had a bad day" from "our SDLC broke" by cross-referencing external benchmarks |
| CUSUM drift detection | Catches gradual quality decay over time — borrowed from manufacturing quality control |
| Pre-tool TDD hooks | Before source edits, a hook reminds Claude to write tests first. CI scoring checks whether it actually followed TDD |
| Self-evolving loop | Weekly/monthly external research + local CI shepherd loop — you approve, the system gets better |
Optional: Cross-Model Review (Codex)
Claude can't grade its own homework. Have a different AI from a different company review Claude's work — different training, different blind spots, different biases. We use OpenAI's Codex CLI, and it's three commands to set up:
npm i -g @openai/codex
export OPENAI_API_KEY=sk-...
codex --version # confirm ready
That's it. Codex picks up your OpenAI account's best available model automatically — if you have GPT-5.5, it uses GPT-5.5; otherwise GPT-5.4. No model config needed.
How to use it: after Claude's self-review passes, write a one-file mission brief and run:
codex exec -c 'model_reasoning_effort="xhigh"' -s danger-full-access \
-o .reviews/latest-review.md \
"Read .reviews/handoff.json and review per the checklist. Output findings + CERTIFIED or NOT CERTIFIED."
xhigh reasoning is non-negotiable — lower settings miss subtle bugs. See CLAUDE_CODE_SDLC_WIZARD.md for the full protocol (handoff format, round-2 dialogue loop, preflight docs). Real-world: this catches P0/P1 issues in 2-3 out of 10 reviews that Claude's self-review rated as clean.
How It Works
Think Iron Man: Jarvis is nothing without Tony Stark. Tony Stark is still Tony Stark. But together? They make Iron Man. This SDLC is your suit - you build it over time, improve it for your needs, and it makes you both better.
The dream: Mold an ever-evolving SDLC to your needs. Replace my components with native Claude Code features as they ship — and one day, delete this repo entirely because Claude Code has them all built in. That's the goal.
WIZARD FILE (CLAUDE_CODE_SDLC_WIZARD.md)
- Setup guide, used once
- Lives on GitHub, fetched when needed
|
| generates
v
GENERATED FILES (in your repo)
- .claude/hooks/*.sh
- .claude/skills/*/SKILL.md
- .claude/settings.json
- CLAUDE.md, SDLC.md, TESTING.md, ARCHITECTURE.md
|
| validated by
v
CI/CD PIPELINE
- E2E: simulate SDLC task -> score 0-10
- Before/after: main vs PR wizard
- Statistical: 5x trials, 95% CI
- Model-aware: SDP adjusts for external conditions
Self-Evolving System
| Cadence | Source | Action |
|---|---|---|
| Weekly | Claude Code releases | PR with analysis + E2E test |
| Weekly | Community (Reddit, HN) | Issue digest |
| Monthly | Deep research, papers | Trend report |
Every update: regression tested -> AI reviewed -> human approved.
E2E Scoring
Like evaluating scientific method adherence - we measure process compliance:
| Criterion | Points | Type |
|---|---|---|
| TodoWrite/TaskCreate | 1 | Deterministic |
| Confidence stated | 1 | Deterministic |
| Plan mode | 2 | AI-judge |
| TDD RED | 2 | Deterministic |
| TDD GREEN | 2 | AI-judge |
| Self-review | 1 | AI-judge |
| Clean code | 1 | AI-judge |
40% deterministic + 60% AI-judged. 5 trials handle variance.
Model-Adjusted Scoring (SDP)
| Metric | Meaning |
|---|---|
| Raw | Actual score (Layer 2: SDLC compliance) |
| SDP | Adjusted for model conditions |
| Robustness | How well SDLC holds up vs model changes |
- Robustness < 1.0 = SDLC is resilient (good!)
- Robustness > 1.0 = SDLC is sensitive (investigate)
Tests Are The Building Blocks
Tests aren't just validation - they're the foundation everything else builds on.
- Tests >= App Code - Critique tests as hard (or harder) than implementation
- Tests prove correctness - Without them, you're just hoping
- Tests enable fearless change - Refactor confidently
Official Plugin Integration
| Plugin | Purpose | Scope |
|---|---|---|
claude-md-management |
Required - CLAUDE.md maintenance | CLAUDE.md only |
claude-code-setup |
Recommends automations | Recommendations |
code-review |
Local self-review and PR review (optional) | Local + PRs |
Prove It's Better
Don't reinvent the wheel. Use native/built-in features UNLESS you prove your custom version is better. If you can't prove it, delete yours.
- Test the native solution — measure quality, speed, reliability
- Test your custom solution — same scenario, same metrics
- Compare side-by-side
- Native >= custom? Use native. Delete yours.
- Custom > native? Keep yours. Document WHY. Re-evaluate when native improves.
This applies to everything: native commands vs custom skills, framework utilities vs hand-rolled code, library functions vs custom implementations.
How This Compares
This isn't the only Claude Code SDLC tool. Here's an honest comparison:
| Aspect | SDLC Wizard | everything-claude-code | claude-sdlc |
|---|---|---|---|
| Focus | SDLC enforcement + measurement | Agent performance optimization | Plugin marketplace |
| Hooks | 3 (SDLC, TDD, instructions) | 12+ (dev blocker, prettier, etc.) | Webhook watcher |
| Skills | 4 (/sdlc, /setup, /update, /feedback) | 80+ domain-specific | 13 slash commands |
| Evaluation | 95% CI, CUSUM, SDP, Tier 1/2 | Configuration testing | skilltest framework |
| CI Shepherd | Local CI fix loop | No | No |
| Auto-updates | Weekly CC + community scan | No | No |
| Install | npx agentic-sdlc-wizard init |
npm install | npm install |
| Philosophy | Lightweight, prove-it-or-delete | Scale and optimization | Documentation-first |
Our unique strengths: Statistical rigor (CUSUM + 95% CI), SDP scoring (model quality vs SDLC compliance), CI shepherd loop, Prove-It A/B pipeline, comprehensive automated test suite, dogfooding enforcement.
Where others are stronger: everything-claude-code has broader language/framework coverage. claude-sdlc has webhook-driven automation. Both have npm distribution.
The spirit: Open source — we learn from each other. See COMPETITIVE_AUDIT.md for details.
Documentation
| Document | What It Covers |
|---|---|
| ARCHITECTURE.md | System design, 5-layer diagram, data flows, file structure |
| CI_CD.md | All workflows, E2E scoring, tier system, SDP, integrity checks |
| SDLC.md | Version tracking, enforcement rules, SDLC configuration |
| TESTING.md | Testing philosophy, test diamond, TDD approach |
| CHANGELOG.md | Version history, what changed and when |
| CONTRIBUTING.md | How to contribute, evaluation methodology |
Community
Automation Station — a community Discord packed with software engineers bringing 40+ years of combined experience across every area of the stack.
Frontend · Backend · Infra · Embedded · Data · QA · DevOps
Share patterns, ask questions, compare notes on AI agents, automation, and SDLC tooling.
Contributing
PRs welcome. See CONTRIBUTING.md for evaluation methodology and testing.
Feedback
Three ways to report bugs, request features, or ask questions:
- In-session: run
/feedbackinside any Claude Code session using this wizard — auto-fills context and redacts secrets before filing - Issue templates: bug report, feature request, question
- Discussions: open-ended conversations at github.com/BaseInfinity/claude-sdlc-wizard/discussions
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