Claude Haiku 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 Haiku 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: $0.01
Wall time: 53s
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-haiku-5.5: $0.011527. 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: $0.01
Wall time: 53s
Human step-ins: 0
Turns: 7

Model usage
Model	Input tok	Output tok	Cost
AClaude Haiku 5.5anthropic/claude-haiku-5.5effort default (no --effort flag) · thinking on	185,799	11,119	$0.01

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Haiku 5.5 (anthropic/claude-haiku-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": "`app.db` is now at schema v2, and `migrate.py` is in the work directory.\n\n**Result of the run:**\n- 170 users kept, 30 duplicates removed, 1000 orders kept. Order ids and kept user ids are unchanged.\n- `schema_version` holds one row, `2`. Foreign-key and integrity checks pass, and no order is orphaned.\n- Order statuses are now `paid` 432, `pending` 289, `refunded` 279.\n- A second `python migrate.py` reports \"already at schema v2; nothing to do\" and exits 0.\n\n**How the script works:**\n- The whole upgrade runs in one `BEGIN IMMEDIATE` transaction. All rows are computed in Python first, so bad data aborts before anything is written.\n- Any failure rolls back and exits 1. I confirmed this on a scratch copy with one orphan order: the file's checksum was unchanged afterwards.\n- Idempotency comes from the `schema_version` check inside the transaction.\n- The database path defaults to `app.db` next to the script; an optional argument overrides it.\n\n**Things you should know:**\n- **Duplicate data was discarded, not merged.** The 30 deleted rows had different names and dates from the kept row. For example, id 169 \"Hana Ng\" shares an email with id 1 \"Ben Park\", and id 1 was kept. The rule you specified keeps the lowest id's data. The deleted rows' ids and the kept id each one mapped to were printed during the run.\n- **Backup:** I copied the original to `app.db.v1.bak` before migrating. It's the only copy of the deleted duplicate rows, so keep it or delete it deliberately.\n- **Single-word names abort the migration.** The spec needs a non-null `last_name`, and none of the current data has single-word names. A future one will stop the run with an error rather than guess a last name.",
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