Opus 4.7 Broke 40% of Prompts; Fix Was Structuring CLAUDE.md and Skills

When Opus 4.7 dropped in April, about 40% of the prompts across 6 mid-market company setups broke overnight. Token burn went up, outputs became weirdly literal — 4.6 had been filling in ambiguous instructions, but 4.7 didn't. The fix wasn't rewriting prompts; it was finally taking CLAUDE.md and Skill files seriously.
What broke and why
Prompts written for 4.6 assumed the model would be charitable about vague instructions. 4.7 interpreted them literally, causing outputs that needed 3-4 turns to correct. Prompts that survived were those baked into Skill files with explicit output formats, length caps, and worked examples.
The rebuild approach
Across the 6 setups, three structural changes were made:
- Skills replaced standalone prompts — anything done more than 3 times got a Skill file (50–200 lines) with audience, output format, length, and a 2-3 sentence worked example. Skills are loaded on demand instead of bloating context.
- Hierarchical CLAUDE.md — one global file for user identity, business, voice rules; a project-level CLAUDE.md per engagement; session-level instructions for one-offs. Model reads in order and builds a mental model that survives across sessions.
- Memory files broken out — kept CLAUDE.md under 400 lines; detailed institutional knowledge lives in separate files that CLAUDE.md points to, loaded on demand.
- Verification step in long Skills — model generates output, checks against a 5–7 item checklist, revises. Adds 30s per call but cut downstream cleanup ~70%.
Results after 3 weeks
- Average prompt-to-acceptable-output dropped from 3-4 turns to 1-2.
- Token usage dropped 22% across workspaces.
- "This output is weird, let me try again" rate dropped from once per 4 prompts to once per 15.
- Next model release should now be a net positive, not net negative.
Still unsolved: versioning CLAUDE.md
Project-level files are in git, but the global CLAUDE.md lives in chat history, which is fragile. No rollback mechanism yet.
Mental model
The model is the engine. Skills + CLAUDE.md + memory is the car. Build the car once; each new engine makes it faster.
📖 Read the full source: r/ClaudeAI
👀 See Also

Splitting Agent Context into Three Layers to Solve the 700-Line Monolith Problem
A team building a 6-agent autonomous system solved context file bloat by separating agent context into three layers based on concern type and change frequency: CLAUDE.md for identity, BRIEFING.md for mission, and PLAYBOOK.md for operations. This approach prevents silent failures from argument limits and makes editing predictable.

Qwen3.x models fail silently in OpenClaw due to streaming output format mismatch
Qwen3.x models in streaming mode output to the 'reasoning' field instead of 'content', causing OpenClaw to silently fall through to fallback models. A proxy that translates API formats and injects 'think: false' fixes the issue, enabling full tool-call evaluation.

OpenClaw Job Search Automation Guide — Preferences, Cron Jobs, and Filtering
A practical guide using OpenClaw to automate job hunting: define a clear preference spec, choose a tool for fetching postings (browser automation, scraping, or structured API), and validate the workflow before setting up a cron job.

Documentation for Writing MCP Tools in C# .NET Framework for Claude Desktop/Code
Complete documentation and C# coding examples for creating custom MCP tools using .NET Framework 4.8, enabling Claude Desktop/Code to automate interactions with external processes, software, APIs, and IoT devices.