Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Write an atomic, idempotent SQLite v1→v2 migration with dedupe, FK and CHECK constraints

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-16 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.33
Wall time: 1.3h
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: write a safe schema migration (SQLite) `app.db` is at schema v1: `users(id, full_name, email, created 'DD/MM/YYYY')`, `orders(id, user_email, total_cents, status)`. Write `migrate.py` (stdlib sqlite3) that upgrades `app.db` IN PLACE to v2: - `users(id INTEGER PRIMARY KEY, first_name TEXT NOT NULL, last_name TEXT NOT NULL, email TEXT NOT NULL UNIQUE, created_at TEXT NOT NULL)` — trim names; first_name = first word, last_name = the rest ("Chloé de Vries" → "Chloé", "de Vries"); email lowercased+trimmed; created_at ISO `YYYY-MM-DD`. - Users whose emails differ only by case/whitespace are duplicates: keep the LOWEST id, delete the others, and re-point their orders. - `orders(id INTEGER PRIMARY KEY, user_id INTEGER NOT NULL REFERENCES users(id), total_cents INTEGER NOT NULL, status TEXT NOT NULL CHECK(status IN ('paid','refunded','pending')))` — user_id resolved via email (case-insensitive); status normalised (trim + lowercase). - Table `schema_version(version INTEGER)` containing exactly one row `2`. - Must be atomic (all-or-nothing in one transaction) and idempotent: running `python migrate.py` again on a v2 database must change nothing and exit 0. Keep order ids and kept user ids unchanged. Run it once so `app.db` is migrated. No network.

Scorer re-runs migrate.py twice on a fresh v1 db (dump hash must not change), checks exact schema, constraints, merged duplicate users and re-pointed orders. 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. Scorer builds a fresh v1 database, runs migrate.py twice: both runs exit 0 and the database dump is identical after run 1 and run 2 (idempotent). 2. users schema exactly: id INTEGER, first_name TEXT NOT NULL, last_name TEXT NOT NULL, email TEXT NOT NULL, created_at TEXT NOT NULL (in that order); orders columns id, user_id, total_cents, status with the stated types; orders has a foreign key to users. 3. CHECK constraint rejects an invalid status; UNIQUE constraint rejects a duplicate email. 4. schema_version contains exactly one row with value 2. 5. User and order counts match the key (duplicates merged); orders per user and total cents per user match exactly (orders re-pointed to the kept lowest id); status counts after normalisation match. 6. Every kept user row (names split, email lowercased, ISO date) matches exactly. 7. The agent's own migrated app.db must also pass all the data/schema checks.

Setup notes: Use Case Scout batch 4 case d04-sqlite-schema-migration (offline fixture). Prepared with batch4/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/key.json`. Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. Claude Code's own reported cost for claude-opus-5.5: $0.331507. Score: 13/13 checks. 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: $0.33
Wall time: 1.3h
Human step-ins: 0
Turns: 6

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	135,941	5,264	$0.33

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. Scorer builds a fresh v1 database, runs migrate.py twice: both runs exit 0 and the database dump is identical after run 1 and run 2 (idempotent). 2. users schema exactly: id INTEGER, first_name TEXT NOT NULL, last_name TEXT NOT NULL, email TEXT NOT NULL, created_at TEXT NOT NULL (in that order); orders columns id, user_id, total_cents, status with the stated types; orders has a foreign key to users. 3. CHECK constraint rejects an invalid status; UNIQUE constraint rejects a duplicate email. 4. schema_version contains exactly one row with value 2. 5. User and order counts match the key (duplicates merged); orders per user and total cents per user match exactly (orders re-pointed to the kept lowest id); status counts after normalisation match. 6. Every kept user row (names split, email lowercased, ISO date) matches exactly. 7. The agent's own migrated app.db must also pass all the data/schema checks.
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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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

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  "result": "I wrote `migrate.py` and ran it once, so `app.db` is now at schema v2. A second run printed \"already at schema v2; nothing to do\", exited 0, and left the file byte-for-byte the same (same SHA-1 hash before and after).\n\n**What the data looked like before:**\n- **Duplicates:** 200 users made up 25 groups of duplicate emails. The extra copies were mostly upper-case versions (e.g. `[REDACTED:email]`). Keeping the lowest id in each group leaves **170 users**. Some duplicates had a different name from the user they matched (e.g. id 1 \"Ben Park\" vs id 169 \"Hana Ng\"). As specified, the kept row's name wins.\n- **Statuses:** there were 7 spellings (`' pending'`, `'PAID'`, `'Paid '`, and so on). All 1000 orders now use `paid`, `pending` or `refunded`.\n- **Names:** 15 names had extra spaces at the start or end. They're trimmed and split, e.g. `\"  Eva de Vries \"` \u2192 `Eva` / `de Vries`.\n- **Dates:** all were valid `DD/MM/YYYY` and are now `YYYY-MM-DD`.\n- No order pointed to an unknown email.\n\n**How the script works:**\n- **One transaction:** everything runs inside a single `BEGIN IMMEDIATE \u2026 COMMIT`. The old tables are renamed, the new tables are created with the exact v2 definitions, and the data is copied across with ids kept. Then the old tables are dropped and `schema_version` is created with the single row `2`.\n- **Re-running:** it reads `schema_version` inside the same transaction. If the version is already 2 it rolls back without changing anything. Any other unexpected version is treated as an error.\n- **Fails safely:** it stops and rolls back if an order can't be matched to a user, a status isn't one of the three allowed values, a date or total is bad, or the order count changes. It also runs SQLite's foreign-key check before committing.\n\n**Checks I ran:**\n- **Forced failure:** on a copy of the database I set one order's status to `'lost'`. The script stopped with `ValueError: order 500: invalid status 'lost'` and the copy was left as untouched v1 tables.\n- **Normal run:** on another copy, it migrated and then a second run did nothing.\n- **After the real run:** SQLite's integrity check reports `ok` and there are no foreign-key violations.\n\nThe script takes an optional database path (`python migrate.py other.db`) and defaults to `app.db`.",
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