Use this tool when you need to sample the fixed repertoire for recognition or text fixtures. 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, 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.

Sample from the fixed implementation inventory of 1614 entries. Return values, zero-based selectedEntryIndices, and repertoireSize. Range inventories resolve an entry index through inclusive code-point ranges; explicit inventories retrieve the stored string in order. The pinned repertoire is not downloaded or updated during a run.^{[2, 3]}

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 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. A sampled character does not guarantee a visible or distinct glyph, correct shaping, a valid linguistic combination, or a particular font feature.

Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Extremely large composite outputs are rejected.

For a related task, use Random Emoji Skin Tone Sequence Generator: Emoji modifiers can encode a skin-tone variation on a supported emoji base. This generator samples whole pinned Unicode Emoji 17.0 recommended modifier sequences, so sampling is over sequence entries rather than independently chosen tones. For a complementary step, use Random Combining Diacritical Mark Generator: Combining diacritical marks attach to surrounding text rather than necessarily displaying as standalone glyphs. This generator samples the assigned Combining Diacritical Marks block for text fixtures, without supplying a base or applying normalization.

• Random Emoji Skin Tone Sequence Generator

  Emoji modifiers can encode a skin-tone variation on a supported emoji base. This generator samples whole pinned Unicode Emoji 17.0 recommended modifier sequences, so sampling is over sequence entries rather than independently chosen tones.

• Random Combining Diacritical Mark Generator

  Combining diacritical marks attach to surrounding text rather than necessarily displaying as standalone glyphs. This generator samples the assigned Combining Diacritical Marks block for text fixtures, without supplying a base or applying normalization.