Building API endpoints with Claude: Practical prompt engineering lessons from a 70+ endpoint project

Practical prompt engineering for action-taking agents
A developer building a LinkedIn automation API with 70+ endpoints, server-side execution, and Chrome extension authentication used Claude to write approximately 80% of the code. The project revealed specific lessons about structuring prompts for agents that perform actions like API calls, data extraction, and decision trees versus agents that only chat.
The contract approach to prompts
The developer found that natural language instructions like "find relevant leads on LinkedIn and send them a personalized connection request" led to problems: Claude would hallucinate fields, skip validation, and chain unnecessary API calls. What worked was treating every agent prompt like a function signature with explicit constraints:
- INPUTS: Exactly what the agent receives (schema, types, edge cases)
- BOUNDARIES: What the agent must NOT do (no fallback guesses, no skipped fields, no invented data)
- OUTPUT CONTRACT: Exact shape of what comes back, including error states
- DECISION RULES: If X then Y, if Z then stop — no ambiguity
Specific lessons learned
"Be creative" is problematic for action agents: Every time room for interpretation was left in action-taking prompts, Claude would produce unexpected results. For agents that execute real actions, zero creativity is preferable, saving creativity for content generation tasks.
Error handling is essential: Approximately 40% of prompt tokens were dedicated to "what to do when things go wrong." Without this, Claude would either retry infinitely or silently swallow errors and return partial data as if everything was fine.
Chain-of-thought trade-offs: Forcing every agent to output a reasoning step before acting costs about 15% more tokens but saves debugging hours. When something breaks, the developer can read exactly why the agent decided to do what it did.
Pattern matching over persona prompts: Instead of using prompts like "you are a senior engineer," the developer pastes a real example of good output and says "match this pattern exactly." Claude performs better at pattern matching than role playing.
Model selection strategy: Simple extraction and formatting tasks are routed to Sonnet (or even Haiku), while only complex decision-making uses Opus. This approach reduced API costs from painful to manageable, as most agent tasks don't require Opus.
📖 Read the full source: r/ClaudeAI
👀 See Also

Fixing Claude Code's KV Cache Invalidation with Local Backends
Claude Code versions 2.1.36+ inject dynamic telemetry headers and git status updates into every request, breaking prefix matching and forcing full 20K+ token system prompt reprocessing on local backends like llama.cpp. A configuration fix in ~/.claude/settings.json can reduce processing from 60+ seconds to ~4 seconds.

Running OmniCoder-9B locally with llama.cpp configuration details
A developer achieved 96.7% average HumanEval score with OmniCoder-9B on mid-range hardware using specific llama.cpp flags including --reasoning-budget 0 to disable chain-of-thought output. The setup used a Q6_K quantized model running on an RTX 3080 with 10GB VRAM.

Troubleshooting OpenClaw: A Minimalist Reset Method
A Reddit user shares a five-step method to fix unstable OpenClaw setups by removing all skills, switching to Claude Sonnet, clearing sessions, simplifying SOUL.md, and testing with basic commands.

Mastering Backup: Safeguarding Your OpenClaw Agent
In an era dominated by automation and AI, ensuring the safety of your OpenClaw agent through robust backup strategies is paramount. Learn the essential steps to secure your digital assistant.