| name | loop-design-check |
| description | Design a goal-oriented agent loop, and review it for the ways loops go wrong — spinning and burning tokens, Goodhart-gaming the verifier, or running a wrong answer to completion. Two actions: (1) WRITE a loop — gate whether to build it, define a machine-decidable goal, pick the loop type, pick a skeleton; (2) REVIEW a loop — run it past five failure modes plus decidability, boundaries, fallback, judge independence, and keep-judgment-with-the-human red lines. Use when designing an autonomous agent loop, or when you already have one and worry it will spin, cheat, or run a wrong answer to the end. Complements the mechanism-layer loop skills (autonomous-loops, continuous-agent-loop) by covering the judgment layer they don't. 中文触发：写 loop、设计 loop、做一个 loop、检查 loop 对不对、loop 体检、loop 会不会跑飞、可判定目标、五个崩法、plan build judge。English triggers: design an agent loop, write a loop, check a loop, loop review, prevent a runaway loop, goal-oriented loop, decidable goal, plan/build/judge. |
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.
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
Miss any one → don't build a loop; do it by hand or another way.
What stops most people isn't "can I write a loop," it's "does my repo deserve one." A repo that deserves a loop has a reconciliation baseline (golden sample / upstream total) + tests + a lint guard. A repo that doesn't deserve a loop will only have its errors amplified by one.
Step 1 · Define a machine-decidable goal (the hard part — the loop lives or dies here)
The whole loop rides on the comparator's "is it done yet?" The comparator can only work if your exit condition can be judged yes/no by a machine.
- Bad: Vague ("make it good," "write it sharper") → the comparator can't judge → either it never passes (stuck retrying) or it guesses (passes/blocks at random).
- Good: Decidable ("all 96 unit tests green AND a change-list is produced," "module-02 fields filled, pytest passes, business logic untouched") → one check settles it; the loop converges cleanly.
Five-point goal framework:
- Done-criterion is machine-verifiable.
- Boundary conditions defined alongside the done-criterion ("what it must NOT do") — anti-Goodhart; missing boundaries = a license to cheat.
- Has a failure fallback — retry cap N + escalate to a human when exceeded.
- Goal is layered.
- Prefer reconciliation over assertion for the done-criterion — anchor to external fact (golden sample / upstream total / financial tie-out / platform back-office numbers) before your own assertions. "All tests pass" can be gamed (loosen asserts, fake mocks, swallow exceptions); "diff vs the reference < 0.01" can't.
Self-check: read the goal to someone who doesn't know the domain — can they run one command and tell whether it's done? If not, it isn't decidable enough. Go back.
Step 2 · Pick the loop type
| Your task | Loop type (cybernetic) | How it stops |
|---|
| Has a clear "done" test (write to done / a batch of images processed) | servo (/goal-style closed-loop) | stops on reaching the goal |
| No endpoint, must keep maintaining a state (inventory alert / scheduled health check) | regulator (/loop-style thermostat) | never stops; acts only on change (dead-band suppresses noise) |
| Periodic sampling, stop on a condition (watch a PR until CI is green) | regulator with an exit | stops when the exit condition holds |
| Must "ensure something happens on time" | wrap the above in /schedule | cron fires it |
Rule of thumb: clear "done" test → servo; must keep maintaining, no endpoint → regulator; must "happen on time" → wrap a regulator in schedule.
Step 3 · Pick a skeleton
Maintenance type (tend something that exists) → document-driven dispatch.
The loop isn't "run a fixed check on a timer," it's "read a doc on a timer, and dispatch only when the doc changed." The doc is the task queue + state machine + human interface.
Three disciplines: ① the problem column is human-write-only, the result column is loop-write-only, state advances one-way and never rolls back; ② the exit code is final (if the script says exit 1, the script wins); ③ state advances only as far as "awaiting verification" — the "done" cell is flipped by a human only. The loop is the worker, not the acceptance officer.
Greenfield type (build from scratch) → plan / build / judge, three roles.
| Role | Does | Key |
|---|
| Plan | break the goal into a spec + decidable acceptance conditions | acceptance must be script-judgeable |
| Build | write to the spec | must not change the acceptance conditions |
| Judge | run acceptance independently; pass → stop, fail → return with the failure reason to Build | independent + deterministic |
Three iron rules (all bet on the judge): ① the judge must be independent — not the same agent as Build (grading your own homework always inflates); ② deterministic rules — pytest / reconciliation diff / type check / diff, never "looks right"; ③ Build may not edit the acceptance conditions to pass. Three failed retries → escalate to a human.
Step 4 · Add damping (against oscillation / runaway)
Retry cap, hard stop, human flips the last switch = damping. Negative feedback with no damping oscillates (the Ralph-Wiggum loop: spinning in place, burning tokens).
Step 5 · Land in three stages (don't go fully automatic on day one)
① Run it once by hand (forces you to state exactly "how the judge decides") → ② harden into a skill / Claude Code sub-agents (a main Claude loops, dispatching plan/build/judge) → ③ hang it on cron for full automation.
Action 2 — Review a loop (checklist = five failure modes)
Run the loop past each row. Hitting any one = this loop will misfire; send it back. These five are negative experience (gotchas) — worth more than positive rules.
| # | Failure mode (how it breaks) | Review question (a hit = red) | Antibody |
|---|
| 1 | Goal is a correct platitude → spins, burns money | Can the exit condition be machine-judged yes/no? Or is it "manage it well / make it good"? | Replace with a decidable result condition (Action 1·Step 1) |
| 2 | "Verification" written as "check if it looks ok" → agent confidently says fine and stops | Is the judge the defendant itself? Does verification rest on "looks right" or deterministic rules? | Reconcile + exit code rules + independent judge |
| 3 | (worst) Only gates on "all tests pass" → agent deletes the tests | Is there a boundary ("what it must NOT do")? Or only a done-criterion? | Done-criterion + boundary together (the Goodhart antibody) |
| 4 | Counts on the agent asking mid-run → it won't; it runs the wrong answer to the end | Is there any "clarify only at runtime" point? | Front-load every clarification; settle it once before launch |
| 5 | Bloated CLAUDE.md + stale memory → the faster it loops, the more it errs | Are the docs/memory it depends on fresh? Who maintains them? | Layered memory + periodic lint |
Plus three red lines (violate any = not allowed to go automatic):
- Keep judgment with the human. Acceptance / the "done" cell is flipped by a human; the loop is not the acceptance officer.
- Responsibility doesn't transfer. Anything whose failure you can't afford (merge the wrong PR / publish the wrong thing / misallocate money) → don't hand over the authority automatically.
- Counter-intuitive warning. The more "self-improving / rewrites-its-own-rules" a loop is, the stricter the human review it needs (to see what it rewrote the rules into) — not looser. The machine is too fast to intercept after the fact, so the human's judgment must sit before the action (a hard gate), not as a post-hoc patch.
Worked example — reviewing a "nightly green-keeper" loop
You want a loop that runs every night and fixes whatever tests are failing.
- Naive goal: "make all tests pass." → Step-1 self-check fails: this is the bait for failure mode #3.
- Decidable goal (fixed): "all tests green AND no test file deleted or weakened AND coverage not lowered AND a change-list produced." Boundary now defined alongside the done-criterion.
- Type: servo with a retry cap of 3 (Step 2 + Step 4).
- Skeleton: plan/build/judge — the judge is CI run independently, never the fixing agent (Step 3).
Now run the review checklist, and it catches what the naive version would have missed:
- #3 hit → the naive "all tests pass" lets the agent delete a failing test to "win." Fixed by the boundary "no test file deleted/weakened."
- #2 hit → if the fixing agent also judged its own fix, it would pass itself. Fixed by "judge = independent CI, deterministic."
- #4 hit → if a fix is ambiguous, the agent won't stop to ask at 2 a.m.; it'll commit a guess. Fixed by front-loading: ambiguous fixes are left for the human, not guessed.
- Red line → the loop opens a PR but does not auto-merge; the human flips the last switch (responsibility doesn't transfer).
The naive loop and the reviewed loop differ by four lines of constraint — and that's the difference between "wakes you to a deleted test suite" and "wakes you to a clean PR."
One-line close
The hard part of writing a loop isn't "can I write a loop," it's defining a goal a machine can reconcile — decidable, bounded, reconciliation-based. The controller must be deterministic and external; keep judgment and the standard with the human; the system tends toward entropy, so maintain it.
A loop only rewards someone who has already thought it through. Count on it to think for you, and it will happily think wrong, with you, at scale.
Lineage: Wiener's two-level feedback (The Human Use of Human Beings, 1950) for the judgment/execution split and red lines; the plan/build/judge pattern from Anatoli's Loops explained and Addy's Loop Engineering.
Mechanism layer (how to wire the loop architecture): see autonomous-loops / continuous-agent-loop. This skill does not re-implement mechanism; it covers goal definition and runaway prevention only.
