loop-design-check

Loop Design + Review

Premise. An LLM is a feed-forward system: prompt in → tokens out, with no built-in "steer toward the goal" across turns. To make it behave like a goal-oriented system, you wrap a feedback loop around it. This skill helps you write that loop correctly and review it so it won't run away.

When to use / not

Use it when:

• You want to hand a repeating task to an agent that runs over and over (write→test, test→fix, fix→verify…).
• You already have a loop and worry it spins, cheats, or runs a wrong answer to completion.

Don't use it for:

• A one-off task → just do it; don't wrap a loop around it.
• A plain timer / poll → use /loop; no design needed.
• How to wire the loop architecture (pipelines → DAGs, long-run recovery) → that's the mechanism layer; see autonomous-loops / continuous-agent-loop. This skill only covers "is the goal right, and will it run away" — it does not re-explain mechanism.

Red-line premise: two levels of feedback

Level	Who owns it	What it does
Execution (low)	machine / agent	Measures "how far from the literal goal" and grinds it to zero. The machine is strong here.
Judgment (high)	human	Decides "is this goal itself right, should it change, should it stop." The machine can't step outside its own loop to question the goal.

A thermostat can feed back "how far from 26°C," but when you have a fever and want 28°C it can't judge whether 26 is the right target — it just grinds toward 26. "What to set today" is always the human's call. Handing judgment / sign-off / the last switch to the machine = removing the high-level feedback = it sprints, fast and hard, toward a goal no one questioned → wrong output.

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Action 1 — Write a loop (5 steps)

Step 0 · Subtract first: should you even build it? (4-condition gate, any miss = veto)

① the task repeats weekly or more　② verification can be automated　③ the token budget can take it　④ the agent has tools that actually run and see the result