clops-mcp

<!-- mcp-name: io.github.clops-mcp/clops-mcp --> # clops Long-running, multi-step Claude Code workflows, written in Python and run one focused step at a time. ## The problem You write a skill for a workflow you run every week. It works. Then you add the edge cases, the exceptions, the "don't forget to check X" — and it stops working. Claude cheats the steps it finds boring. It forgets details buried in the middle. It overcorrects on whatever you emphasised last. The more detail you add to make it reliable, the less reliable it gets, because the entire document is in context the entire time and Claude has to keep deciding, unprompted, which part applies right now. clops inverts that. Each step of the workflow is a separate **Op** with its own prompt, its own input, and its own subagent. The runtime decides which Op runs next and renders the prompt for it. The agent doing the work sees one step's worth of instruction — not the workflow. The whole goal of clops is to help Claude think about what it needs to think about right now, and not a whole lot else. The payoff compounds: a workflow you get right once stays right, and you can compose it into bigger ones instead of rewriting it. Your workflows stop being disposable. ## What this is not **This is not an agent framework.** clops has no API key, no provider SDK, and no model calls of its own. It runs inside Claude Code, which supplies the model and the subagents. If what you want is durable execution, retries, and observability for unattended production agents, you want LangGraph or Temporal, not this. **This is not for unattended work.** clops targets interactive and semi-interactive workflows — the ones where you are in the loop, or will be shortly. The value is *preparation*: the machine spends twenty minutes building context, and when your attention arrives the work is ready to go. If nobody is coming back, clops buys you nothing. ## Why not just write a skill? | Objection | Answer | |---|---| | "Just…