bulletproof

name bulletproof description Use when building a feature, refactoring, fixing a complex bug, changing architecture, or starting any non-trivial coding task. 12-stage verified dev workflow from research to deploy. Bulletproof — Adaptive Development Workflow Author: Artemiy Miller ( @artemiimillier ) · Telegram · who.ismillerr@gmail.com · TG Channel Version: 5.0 · March 2026 License: MIT Compatible: Claude Code, Codex, Gemini CLI, Cursor, Windsurf, OpenCode Core Principle Code to solve problems, not code for code's sake. Before EVERY change ask: "Does this actually solve our problem? Is this the most efficient solution?" If the answer isn't clear — stop, research alternatives, pick the best one. Pick Your Mode Not every task needs the full pipeline. Size Examples Mode Stages S Bug fix, small edit, 1-2 files Lightweight 1 → 4 → 5 → 6 → 7 → Gates (skip spec/plan) M New feature, module refactor, 3-10 files Standard Stages 1-10 L Architecture change, new service, 10+ files Full Stages 1-12 (all) How stages relate: Stages 5-6-7 (Self-Audit, Verification, Impact) run inside each implementation phase as an inner loop. Stages 8-12 run once after all phases complete as an outer loop. Context Management (ALWAYS applies) The 40% Rule Code quality degrades when context fills beyond 40% ("Dumb Zone"). Rules: Stay within 40-60% of the context window Manual /compact at 50% — don't wait for auto If overloaded: save progress → /clear → fresh start Fresh Context Between Stages Every major stage = clean context window: Save stage artifact (research / spec / plan / handoff) /clear Start new stage pointing the agent to the artifact path Handoff Protocol Before /clear always create progress/<task>-handoff.md . See templates/handoff.md for format. Progressive Disclosure Don't dump the entire codebase into context: Research: sub-agents → compact summary Planning: summary + key interfaces only Implementation: only files for current phase In CLAUDE.md: "For details, see path/to/docs.md" (not @file) Stage 1: Deep Research Mode: Read-Only. No code. No changes. Launch parallel Explore agents (1 per area: structure, patterns, deps, tests) WebSearch: Who has already solved this problem? How did they solve it? What is the most efficient known solution? Don't reinvent — find the best existing approach first. Analyze all findings and make a conclusion: which solution is the BEST and why. The research artifact must end with a clear recommendation, not just a list of options. Save to thoughts/research/YYYY-MM-DD-<task>.md (see templates/research.md for format) → /clear Stage 2: Spec / PRD Mode: Read + Write only in specs/. No code. Spec = WHAT and WHY. Not how. Spec = contract. Read Research Artifact from thoughts/research/ Create specs/YYYY-MM-DD-<name>.md (see templates/spec.md for format) Key sections: Problem, Goal, Scope, Acceptance Criteria, Constraints, Non-Goals Skip for size S tasks. → /clear Stage 3: Planning + Questions Mode: Read + Write only in plans/. No code yet. Read both Spec ( specs/ ) and Research ( thoughts/research/ ) Launch Plan agents to check the approach Find gaps: what's unthought? What edge cases? What could break? Be creative and proactive: anticipate ALL possible problems BEFORE writing code. Think several steps ahead. What could go wrong in a week? A month? Under load? With unexpected user behavior? Solve problems before they exist. WebSearch: How have others solved this exact problem? What libraries/patterns exist? What's the proven best practice? Choose the most efficient solution, not the first one that comes to mind. After Plan agents verify the approach — rewrite the plan into an improved version incorporating all findings, edge cases, and research results. Not just patch it — rewrite it better. Challenge Loop (mandatory before finalizing plan) Before finalizing the plan, answer 3 questions: 1. DOES THIS SOLVE THE PROBLEM? Compare every plan item against acceptance criteria from spec. If any criterion is uncovered — the plan is incomplete. 2. IS THIS THE MOST EFFICIENT SOLUTION? Search: who has already solved this problem? What approach did they use? Name 2-3 alternative approaches (including ones found via research). For each: pros, cons, effort. Justify why the chosen approach is better than all alternatives. 3. IS THERE "CODE FOR CODE'S SAKE"? Every change must directly serve acceptance criteria. If a change isn't tied to solving the problem — remove it. Drive-by refactoring = separate task, not part of this one. Annotation Cycle Claude drafts the plan Ctrl+G — plan opens in editor User adds > NOTE: annotations Claude: "Address all notes, don't implement yet" Repeat until no notes remain Questions for User Only for real forks where there's a genuine decision to make Use AskUserQuestion with options For each question: recommend which option you think is best and why Don't ask the obvious Final Plan Create plans/YYYY-MM-DD-<name>.md (see templates/plan.md for full template with Challenge Log, phases, prompts) → /clear Stage 4: Phased Implementation Each phase = separate session, fresh context, feature branch. Phases can be run in parallel via separate Claude Code sessions/terminals when they don't depend on each other. Check the plan for dependencies before parallelizing. Guard phrase to start coding: Only begin implementation after the plan is finalized and all annotation notes are addressed. The trigger: "Implement Phase N according to plan." Order within each phase: Create/switch to feature branch: feature/<task> Update status → in_progress TDD : tests FIRST (red) Implement : code to make tests pass (green) Refactor (if needed) Self-Audit (Stage 5) Verification (Stage 6) Impact Analysis (Stage 7) Gates (see Gates section) Commit (checkpoint) Status → completed , write to Changelog Handoff → /clear Stage 5: Self-Audit (after each phase) Mandatory BEFORE marking completed : Check the phase implementation: 1. SPEC COMPLIANCE Open spec. Walk through every acceptance criterion. For each: implemented? Where exactly in code? If any not covered — finish it. 2. CHALLENGE THE SOLUTION Look at the written code with fresh eyes. Does this actually solve the problem from spec? Is there a simpler/more efficient way? Any "code for code's sake" — changes unrelated to the task? Stage 6: Verification — Deep Bug Hunt Not just linting. Thoughtful review with false-positive filtering. Step 1: Find errors Check ALL code from this phase for: - Logic errors (wrong conditions, off-by-one, race conditions) - Data handling (null/undefined, type mismatches) - Security (injection, auth bypass, exposed secrets) - Performance (N+1 queries, memory leaks, unnecessary re-renders) Step 2: Verify bugs are REAL For EACH found bug: 1. Is this a REAL bug or a false positive? 2. Can you prove this bug is reproducible? 3. If you can't prove it — it's NOT a bug. Don't touch it. RULE: Don't fix code "for beauty" or "just in case". Fix ONLY proven bugs that actually affect functionality. Every "fix" without proof = risk of introducing a new bug. Step 3: Logic and efficiency check Final code cleanliness check: - Logic: is the data flow correct from input to output? - Efficiency: any redundant operations? - Readability: is the code understandable without comments? BUT: don't refactor "for beauty". Only if it affects correctness. Stage 7: Impact Analysis — "Did we break anything?" The most underestimated stage. 75% of AI agents break previously working code. MANDATORY CHECK BEFORE MERGE: 1. REGRESSION What other modules/functions depend on changed files? Run ALL project tests (not just current phase). If anything broke — this is priority #1. 2. SIDE EFFECTS Did any contracts/interfaces change (API, props, types)? If yes — who uses them? Are all consumers updated? 3. THINK AHEAD What problems could these changes cause in a week/month? Edge cases we haven't tested? What happens with: zero data? Huge data? Concurrent requests? What if the user does something unexpected? 4. COMPATIBILITY Backward compatibility preserved? Data migrations needed? Feature flags needed for gradual rollout? Stage 8: Integration Check All phases completed → run gates across entire project Explore agents for audit: everything from spec implemented? Every acceptance criterion → fulfilled? Stage 9: Code Review (fresh context) New session. No implementation bias. Launch @code-reviewer agent (see agents/code-reviewer.md ) Checklist: edge cases, race conditions, backward compat, security, error handling, performance If possible: cross-model review (different model checks Claude's work) Warning : AI reviewing AI has shared blind spots. For critical code — human review is mandatory. Stage 10: Security Scan (for M and L) semgrep --config=auto . # or /security-review # built into Claude Code Stage 11: Fixes + Re-verification If review/scan found issues: Fix (only proven bugs — rule from Stage 6) Re-run gates Repeat Impact Analysis (Stage 7) — fixes didn't break anything else? Re-review if major changes were made Stage 12: Cleanup + Deploy Archive plan: mv plans/<file> plans/archive/ Keep spec as documentation Squash merge → main Deploy — ONLY on explicit user request Deterministic Gates A phase CANNOT be completed without passing ALL required gates. Tier 1: Required (block the phase) # Frontend cd frontend && npx tsc --noEmit # 0 type errors cd frontend && npm run lint # 0 lint errors cd frontend && npm test # all tests green # Backend cd backend && python -m py_compile app/main.py cd backend && pytest --tb=short -q cd backend && ruff check . Tier 2: Recommended (for M and L) npx madge --circular src/ # circular dependencies npm audit --audit-level=high # dependency vulnerabilities pip-audit Tier 3: Deep Security (for Security Scan stage) semgrep --config=auto . # or /security-review If a gate fails — fix and re-run. Never skip. Hooks Add to .claude/settings.json : { "hooks" : { "PreToolUse" : [ { "matcher" : "Bash" , "hooks" : [ { "type" : "command" , "command" : "bash -c \"CMD=$(echo $TOOL_INPUT | jq -r '.command // empty'); echo \\\"$CMD\\\" | grep -qE '(git push.*(main|master)|rm -rf /|DROP TABLE)' && echo 'BLOCKED: Use feature branch / safe alternative.' >&2 && exit 2 || exit 0\"" } ] } ] , "Stop" : [ { "hooks" : [ { "type" : "prompt" , "prompt" : "You are a JSON-only evaluator. Respond ONLY with raw JSON, no markdown.\n\nReview the assistant's final response. Reject if:\n- Rationalizing incomplete work ('pre-existing', 'out of scope', 'follow-up')\n- Listing problems without fixing them\n- Skipping test/lint failures with excuses\n- Making changes unrelated to the stated problem ('code for code's sake')\n- Claiming completion without running verification gates\n\nRespond: {\"ok\": false, \"reason\": \"[issue]. Go back and finish.\"}\nor: {\"ok\": true}" } ] } ] } } Git Discipline Each task = feature/<task> branch Commit after each passed gate (checkpoint for rollback) NEVER push to main directly (hook blocks it) Squash merge on completion Optional Enhancements PostToolUse: Auto-format after every file write { "matcher" : "Write|Edit" , "hooks" : [ { "type" : "command" , "command" : "npx prettier --write \"$FILE_PATH\" 2>/dev/null || true" } ] } Claude generates well-formatted code; the hook handles the last 10% to avoid CI failures. PreToolUse: Block hardcoded secrets on file write { "matcher" : "Write|Edit" , "hooks" : [ { "type" : "command" , "command" : "bash -c \"CONTENT=$(echo $TOOL_INPUT | jq -r '.content // empty'); echo \\\"$CONTENT\\\" | grep -qiP '(api.?key|secret|password)\\s*=\\s*[\\x27\\\"][^\\x27\\\"]{10,}' && echo 'BLOCKED: Hardcoded secret. Use env vars.' >&2 && exit 2 || exit 0\"" } ] } Fragile (regex-based) but catches obvious mistakes. For production, use semgrep or /security-review instead. Model Recommendations Stage Model Why