Claude Haiku 5.5 (effort default (no --effort flag) · thinking on) on Build a csvq CLI (select/where/sort/limit/count, csv|json|table output, exact exit codes) from a written spec

AClaude Haiku 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-17 · by @bobsiopener

Trust 80%Fully autonomoussuccess · first tryCompare

Estimated cost: $0.02
Wall time: 1m
Human step-ins: 0
Outcome: Pass

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Exact prompt

# Task: build the `csvq` command-line tool Write `csvq.py` (Python 3 standard library only, run as `python3 csvq.py ...`). It reads CSV (RFC 4180 quoting, UTF-8, header row required) from a file argument or from stdin when the file is `-` or omitted, and writes to stdout. `people.csv` is a sample. Options (may appear in any order): - `--select COLS` comma-separated column names to output, in that order (default: all, original order). - `--where EXPR` repeatable; all must match. EXPR is `col OP value` with OP one of `=`, `!=`, `<`, `<=`, `>`, `>=`, `~` (case-insensitive substring). If both the cell and value parse as numbers, compare numerically, otherwise as strings. An empty cell never matches `<`,`<=`,`>`,`>=`. - `--sort COL` ascending; `--sort -COL` descending. Numeric if every non-empty value in the column is a number, else string. Empty cells always sort last (both directions). Sort is stable. - `--limit N` output at most N rows (after filtering and sorting). - `--format csv|json|table` (default csv). csv: header + rows, quoted as needed by Python's csv module, `\n` line endings. json: a JSON array of objects (keys in output column order, all values strings), `ensure_ascii=False`, followed by a newline. table: columns left-aligned, padded to the widest cell (header included) with two spaces between columns, no trailing spaces, a dashes line under the header the width of each column. - `--count` print only the number of matching rows (after --where, ignoring --limit) and a newline. - `-h/--help` usage, exit 0. Errors go to stderr with a one-line message and a non-zero exit: unknown column (in any option) exits 2; bad usage (unknown option, bad --where, non-integer/negative --limit, bad --format) exits 2; unreadable input file exits 1. Nothing is printed to stdout on error. No network.

Byte-exact output: RFC 4180 quoting, numeric-vs-string comparison, empty cells sorting last in both directions, stable descending sort, `--sort -COL` argument parsing, table padding without trailing spaces, and distinct exit codes with clean stdout on error. 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. 21 hidden invocations (alternating file argument and stdin) produce byte-identical stdout and exit code 0, or for error cases the exact exit code 2 with empty stdout and a stderr message. 2. A missing input file exits 1 with empty stdout and a stderr message. 3. -h exits 0 and its usage text mentions --where. 4. Reading stdin via '-' works.

Setup notes: Use Case Scout batch 5 case e04-csvq-cli (offline fixture). Prepared with batch5/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. Claude Code's own reported cost for claude-haiku-5.5: $0.015302. Score: 24/24. 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: $0.02
Wall time: 1m
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	205,481	16,824	$0.02

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. 21 hidden invocations (alternating file argument and stdin) produce byte-identical stdout and exit code 0, or for error cases the exact exit code 2 with empty stdout and a stderr message. 2. A missing input file exits 1 with empty stdout and a stderr message. 3. -h exits 0 and its usage text mentions --where. 4. Reading stdin via '-' works.
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

[
 {
  "duration_api_ms": 77180,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.01530224,
  "usage": {
   "input_tokens": 14,
   "cache_creation_input_tokens": 25443,
   "cache_read_input_tokens": 180024,
   "output_tokens": 16824,
   "output_tokens_details": {
    "thinking_tokens": 11365
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 25443,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 664,
     "cache_read_input_tokens": 37339,
     "cache_creation_input_tokens": 643,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 643
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-haiku-5-5": {
    "inputTokens": 14,
    "outputTokens": 16824,
    "cacheReadInputTokens": 180024,
    "cacheCreationInputTokens": 25443,
    "webSearchRequests": 0,
    "costUSD": 0.01530224,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 11365,
    "canonicalModel": "claude-haiku-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
   "spawned_by_subagents": 0,
   "completed": 0,
   "failed": 0,
   "killed": {
    "parent": 0,
    "user": 0,
    "system": 0
   },
   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 7,
  "subtype": "success",
  "api_error_status": null,
  "result": "I wrote `csvq.py` in the work directory. It uses only the standard library, and I ran it against `people.csv`, stdin, and the error cases. All the behaviour in the spec checks out. I didn't write a test file.\n\nI took these readings where the spec was silent:\n\n- **Argument parsing is hand-rolled, not argparse.** argparse rejects `--sort -COL` because it reads `-COL` as a flag, and the spec requires that form. Options accept `--opt VALUE` and `--opt=VALUE`, and `--` ends option parsing.\n- **Whitespace around column names is stripped** in `--select`, `--where` and `--sort`. In `--where`, the value is stripped too, so `name ~ Ana` matches as expected.\n- **\"Number\" means a plain decimal or exponent form** after stripping whitespace. `nan`, `inf` and `1_000` compare as strings.\n- **Numeric-or-string sort is decided on the rows that survive `--where`**, not on the whole file.\n- **Duplicate `--select` columns are a usage error** (exit 2), because a JSON object can't hold duplicate keys.\n- **Repeated `--sort`, `--limit` or `--format` options:** the last one wins.\n- **Short rows are padded with empty cells and extra cells are dropped.** A blank line is skipped, and a file with no header row exits 1.\n- **Duplicate header names:** lookup uses the first occurrence. JSON output will silently collapse duplicate keys. I did not handle this.\n- **Table output with a multi-line cell** prints the embedded newline as-is, which breaks the alignment.\n- **`--count` ignores `--select`, `--sort`, `--limit` and `--format`**, but still validates their column names and values.\n- **Malformed CSV** (a `csv.Error`) exits 1, as an unreadable input does.",
  "ttft_ms": 1451,
  "type": "result",
  "duration_ms": 78004,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 1255,
  "time_to_request_ms": 201,
  "first_content_frame_ms": 1255,
  "queued_turn_count": 0,
  "result_index": 0
 }
]