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Claude Sonnet 5.5 (effort default (no --effort flag) · thinking on) on Plan the startup order of a self-hosted container stack (conditions, profiles, optional and disabled services, error precedence, cycles)

AClaude Sonnet 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first try
Estimated cost
$0.07
Wall time
20s
Human step-ins
0
Outcome
Pass

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Summary

Exact prompt

# Plan the startup order of a self-hosted container stack Our home server runs a compose-style stack (`compose.json` is the JSON form of the compose file). `plan.py` should work out, without starting anything, in which **step** each service would start, and refuse broken stacks with a precise error. The draft ignores most rules. Rewrite `plan(compose, targets=None, profiles=())` in `plan.py` (Python 3 stdlib only), keeping `PlanError(code, detail)` with its `.code` and `.detail` attributes. ## Model 1. `compose["services"]` maps service names to dicts. `depends_on` is absent, a list of names, or a dict `{dep: options}` where options may be `{}`/`null` and may contain `condition` (default `service_started`) and `required` (default `true`). The list form means `service_started`, required. 2. A service is **disabled** if `deploy.replicas` is `0`. 3. **Active set.** If `targets` is `None`: start from every non-disabled service with no `profiles` (missing or empty) or with at least one profile in `profiles`. If `targets` is a list: start from exactly those (error `unknown_target`, detail = name, for the first target in list order that is not a service; then `disabled`, detail = name, for the first disabled target). Then add dependencies transitively: any dependency that exists and is not disabled is added **regardless of its profiles**. 4. **Dependency errors**, checked over active services in name order, and for each over its dependencies in name order; raise the first one found, detail = `(service, dependency)` as a tuple: - stage A: dependency not a service -> `missing_dependency`; dependency disabled -> `disabled_dependency`. Both only when the dependency is required; non-required missing/disabled dependencies are dropped silently and are not checked in stages B, C or later. All stage A checks run before any stage B check. - stage B: condition not one of `service_started`, `service_healthy`, `service_completed_successfully` -> `bad_condition`. All of stage B runs before stage C. - stage C: `service_healthy` on a dependency with no `healthcheck` or with `healthcheck.disable` true -> `no_healthcheck`; `service_completed_successfully` on a dependency whose `restart` is `always` or `unless-stopped` -> `never_completes`. 5. **Cycles** (checked after stage C): if the remaining dependency graph of active services has cycles, raise `cycle` with detail = sorted list of **every** service that is on some cycle (any member of a strongly connected group of 2+ services, or a service depending on itself). Services that merely depend on a cycle are not listed. 6. **Steps.** A service with no remaining dependencies starts at step 0. Otherwise its step is the maximum over its dependencies of: dependency's step + 1 for `service_started`, + 2 for `service_healthy` or `service_completed_successfully`. 7. **Result.** Return a list of waves: `waves[k]` is the sorted list of active services starting at step k, for k = 0 .. max step (steps nobody uses give `[]`). 8. **Scale.** A generated chain-and-fan stack with 3,000 services and dependency depth 2,000 must be planned in under 5 seconds on our shared test box (no `RecursionError`). On `compose.json` with defaults the answer is `[["db", "redis"], [], ["migrate"], [], ["app"], ["proxy"]]`. ## Scoring Hidden check groups (partial credit = fraction passed; all must pass): `basic_waves` (1, 6, 7), `profiles_and_targets` (3), `optional_and_disabled` (2, 3, 4A), `error_precedence` (4, exact code and detail; a detail sequence may be a tuple or a list), `conditions` (4B-C, 6), `cycles` (5, exact detail), `fixture_answer`, `randomized_agreement` (200 random small stacks: same waves, or same error code and detail), `scale` (8).

Staged error precedence with exact (service, dependency) details, profile pull-in, required:false dropping, condition-dependent step offsets, an exact cycle-member list (strongly connected components, not 'anything downstream') and a depth-2,000 stack that breaks naive recursion. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked).

Success criteria: 1. basic_waves 2. profiles_and_targets 3. optional_and_disabled 4. error_precedence 5. conditions 6. cycles 7. fixture_answer 8. randomized_agreement (200 stacks) 9. scale (3,000 services < 5 s)

Setup notes: Use Case Scout batch 8 case i04-compose-startup-plan (offline fixture). Prepared with batch8/prepare.sh; hidden key/tests kept outside the agent's work dir. Prompt = TASK.md verbatim. Scored with the scout's scorer: `python score.py WORKDIR KEYDIR`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring: 2.04. Claude Code's own reported cost for claude-sonnet-5.5: $0.071502. Score: 9/9. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost
$0.07
Wall time
20s
Human step-ins
0
Turns
3
Model usage
ModelInput tokOutput tokCost
AClaude Sonnet 5.5anthropic/claude-sonnet-5.5effort default (no --effort flag) · thinking on61,2312,774$0.07

Reproduce this run

  1. Use the use case prompt on the use case page.
  2. Same setup: Claude Sonnet 5.5 (anthropic/claude-sonnet-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
  3. It counts as a success only if it meets every success criterion: 1. basic_waves 2. profiles_and_targets 3. optional_and_disabled 4. error_precedence 5. conditions 6. cycles 7. fixture_answer 8. randomized_agreement (200 stacks) 9. scale (3,000 services < 5 s)
  4. Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Evidence log
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  "result": "I rewrote `plan(compose, targets=None, profiles=())` in `plan.py`. On `compose.json` with defaults it returns `[[\"db\", \"redis\"], [], [\"migrate\"], [], [\"app\"], [\"proxy\"]]`, which matches the expected answer.\n\nThat is the only check I ran. I did not run the 3,000-service scale case or any of the hidden check groups. The scale case should be fine in principle, because the cycle check and the step calculation are both iterative and linear, with no recursion.\n\n`PlanError(code, detail)` is unchanged. I also added a small `__main__` block so `python3 plan.py compose.json` prints the plan.\n\n- **`targets=[]`:** an empty target list gives an empty active set, so `plan` returns `[]`.\n- **Non-required dependencies:** a non-required dependency that exists and isn't disabled is kept as a real dependency, so it still counts in steps and in the B and C checks. Only missing or disabled ones are dropped.\n- **Disabled test:** a service is disabled only when `deploy.replicas` is exactly `0`. A boolean `False` doesn't count.",
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