Automated Claude Code Pipeline Cuts Token Usage from 78k to 15k Per Feature
What This Pipeline Does
This is an automated pipeline for Claude Code that addresses three common problems: Claude rebuilding existing code, high token costs (50-80k tokens for complex features), and excessive manual oversight. The pipeline runs through 12 phases automatically with one command: /auto-pipeline "add user dashboard with activity feed".
Key Features and Phases
- Pre-check phase: Searches your codebase and package.json before building anything. Example: When you request "Add authentication," it detects existing
next-authinstallations and recommendsEXTEND_EXISTINGinstead of building from scratch. - Requirements extraction: Minimal Q&A to determine actual needs
- Design phase: Creates technical specifications with citations
- Adversarial review: Attacks the design from three angles
- Planning phase: Creates deterministic steps with exact BEFORE/AFTER code
- Build phase: Executes the plan step-by-step
- QA pipeline: Runs linting, type checking, tests, documentation generation, and security scanning
Three Operational Profiles
--profile=yolo: Fast prototyping, skips most checks (~18k tokens)--profile=standard: Balanced approach with warnings on issues (~35k tokens)--profile=paranoid: Full oversight for production code (~50k tokens)
Token Savings Breakdown
A feature that previously cost ~78k tokens now runs in ~15k tokens with the yolo profile. Optimization strategies include:
- Slim agents (60-80% smaller prompts): 40-60% savings
- Caching (security scans, patterns, QA rules): 15-25% savings
- Phase skipping (yolo mode): 30-40% savings
Output-Based Validation System
Instead of relying on Claude's self-reported confidence scores, the pipeline uses objective grep-based validators. For example, in Phase 3 (Adversarial):
has_verdict→ grep "APPROVED|REVISE"no_high_severity→ ! grep "| HIGH |"no_consensus→ no issues from 2+ critics
The creator notes: "Can't game what you can't self-report."
Technical Details and Current Status
The pipeline is built for Next.js/TypeScript but structured to work with any stack. There's a full-workflow-legacy branch available for those who prefer the original manual pipeline with human checkpoints at every step. Caching currently includes security scans by lockfile hash, design patterns, and QA rules.
📖 Read the full source: r/ClaudeAI
👀 See Also
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OpenClaw CoreBrain Plugin: Persistent Memory for AI Coding Agents
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Practical Findings from 11 Multi-Agent Software Builds Without Programmatic Scaffolding
Analysis of 11 autonomous multi-agent builds shows scope enforcement works mechanically (20/20 success) not via prompts (0/20), orchestration costs are dominated by memory re-ingestion (~95% of input spend), and worker model capability creates 9.8x throughput gaps.