Use this tool when you need to assemble reproducible practice cards from a supplied, validated scenario corpus. Prepare a reproducible seed and the inventory, corpus, or experiment parameters stated below.

1. Prepare a reproducible seed and the inventory, corpus, or experiment parameters stated below.
2. Enter seed, count, withoutReplacement, candidateInputsJson, following the field table and calculation rules below, then run the tool.
3. Read result. For result objects, inspect the nested output fields below, including any missing evidence or selected indices.
4. Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.

Enter 1–128 dense candidateInputsJson strings, each a strict JSON object for the imported calculator. Each string is at most 8192 UTF-16 units, the corpus at most 131072, and nesting at most 16 levels; nonfinite numbers and prototype-control keys are rejected. Select candidate positions under the sampling rules, then validate every candidate, including undrawn ones, by invoking the imported calculator with a fixed random source of 0.5. Any invalid candidate fails the whole request. Invoke it again for selected candidates with the request’s advancing source, and return original inputs, actual answer objects, selected indices, and never-selected indices. Duplicate candidate texts or equal answers may occur at distinct positions.^{[2, 3, 4, 5]}

Use a nonblank seed of at most 256 UTF-16 units and a safe-integer count from 1 to 64, subject to the tool’s narrower inventory bound. By default the seed is hashed into a deterministic pseudorandom state; each run restarts that state, so identical full inputs and implementation reproduce the result. The SDK may provide context.random, which replaces the seeded source and advances across the request; the seed remains required. Every source value must be finite in [0,1), with a request limit of 100000 source calls. Draw an index as floor(U×remainingSize). For indexed inventory draws, with replacement draw from the full inventory each time; without replacement remove each chosen position from an ordered pool and reject counts exceeding the inventory size. Uniform-source draws are over positions, not necessarily distinct values. This is not secure randomness.

The imported parser splits commas and whitespace at the top level, preserving function tokens. Each shadow needs two to four literal lengths, optional single inset, and at most one color token; missing blur/spread default to zero and color to currentcolor. Only zero can omit a length unit, negative blur fails, calc lengths are unsupported, and none returns an empty list. Color tokens are retained without full CSS color validation.

Each result.exercises entry preserves its supplied input and imported answer object. Additional roundTripAnswer, secondaryAnswer, prompts, or derived quantities appear only where the mechanics above describe them. selectedCandidateIndices and unusedCandidateIndices refer to the original corpus positions, not answer values.

The default source is deterministic pseudorandomness, not an independently audited or secure draw. Replacement applies to inventory positions; distinct positions may render identically or yield equal answers. A fixture does not independently verify the supplied answers or reference content. Each answer retains the imported calculator’s unit conventions and domain assumptions. Randomizing a supplied scenario does not broaden that calculator’s capabilities.

Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Arrays must be dense, with no missing cells. Extremely large composite outputs are rejected.

For a related task, use Random HTML Tag Counting Exercise Generator: A lexical HTML tag count records start-tag tokens in source rather than rendered elements. These cards sample supplied markup and ignore apparent tags in comments and raw-text content. For a complementary step, use Random HTML Whitespace Normalization Corpus Generator: Whitespace normalization can change ordinary HTML text while preserving content where whitespace is significant. These fixtures use a lexical scanner to collapse ASCII whitespace in supplied source, without creating a browser document.

• Random HTML Tag Counting Exercise Generator

  A lexical HTML tag count records start-tag tokens in source rather than rendered elements. These cards sample supplied markup and ignore apparent tags in comments and raw-text content.

• Random HTML Whitespace Normalization Corpus Generator

  Whitespace normalization can change ordinary HTML text while preserving content where whitespace is significant. These fixtures use a lexical scanner to collapse ASCII whitespace in supplied source, without creating a browser document.