Claude Code Works Better as Code Reviewer Than Generator
Better Workflow for Claude Code
The source describes an alternative approach to using Claude Code that focuses on code review rather than generation. Instead of describing features and getting code back, the author recommends flipping the interaction to make Claude react to existing code.
Key Practices from the Source
The author outlines three specific practices that changed their day-to-day workflow:
- Start with existing code: Begin sessions by pasting current implementations and asking questions like "here's my function that does X, what are the edge cases I haven't handled" rather than "write me a function that does X." This produces more grounded output that catches genuinely missed issues.
- Maintain project context files: Keep a plain text file per project with 3-5 lines covering stack, constraints, and conventions settled on. Paste this at the start of each session since Claude has no memory between sessions. Without this context, you spend the first 20 messages re-establishing ground rules.
- Restart sessions when responses degrade: When something goes wrong mid-session and responses get weird or circular, start a new session with a clean summary instead of trying to course-correct. Long context drift is real, and a fresh session with a tight problem statement almost always outperforms trying to rescue a conversation that's gone sideways.
The author notes that the typical workflow of describing a feature, getting code back, copy-pasting it, and moving on produces okay results but leaves a lot on the table. The real value shows up when you make Claude react to your existing code instead of generating from scratch.
📖 Read the full source: r/ClaudeAI
👀 See Also
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Why Most Claude Pipeline Failures Trace Back to Prompts, Not Models — and How to Fix with Skills
A Reddit post argues that the root cause of pipeline failures in Claude workflows is treating prompts like skills. The fix: define input contracts, output schemas, and a learnings file — making a skill what you promote to v1.
Tell AI to Define Its Own Terms from First Principles for Better Outputs and Auditable Reasoning
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3 weeks of OpenClaw: token costs, loops, and compaction — lessons from the trenches
After burning tokens on heartbeat checks with Opus, fighting agent loops, and losing context to compaction, a Reddit user shares the hard-won fixes: use cheaper models for trivial tasks, write anti-loop rules, and save decision logs.