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Random Movement Level Balance Plan Generator

Allocate supplied low/mid/high movement IDs under exact phrase quotas, retain remaining counts, and verify distribution through the level-counter core; actions remain caller-selected and reviewed.

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Description

Allocate supplied low/mid/high movement IDs under exact phrase quotas, retain remaining counts, and verify distribution through the level-counter core; actions remain caller-selected and reviewed.

A structured creative brief combines a supplied bank of fictional material with explicit sequencing, timing, or compatibility rules. IDs and tags make those constraints inspectable; they do not assess the artistic meaning or quality of a scene.

Allocate supplied low/mid/high movement IDs under exact phrase quotas, retain remaining counts, and verify distribution through the level-counter core; actions remain caller-selected and reviewed.

When to use this tool

Use this generator to create reproducible movement level balance plan cases from the supplied model or bank. Inspect the returned evidence before using cases in a test or exercise.

How to use the tool and how it works

  1. Supply movements, lowQuota, midQuota, highQuota, phraseCount in the field shapes below. Set seed when you need an explicit replay.
  2. Run the tool and correct any input error. For generated controls, distinguish a successful fixture containing an invalid case from failure to generate the fixture.
  3. Read phrases, lowMidHighCounts, unknownCount, balanceSpread, callerReviewRequired. Use the conventions below and keep answer-bearing fields separate when presenting an exercise.

Create exact low, mid, and high level-token quotas, shuffle the tokens, and sample a matching supplied movement for each phrase. Return remaining-quota traces and cross-check the final balance with the level-count calculator.1

Delegated operations used for this result: level change low mid high distribution counter. Their calculation or parsing conventions apply to the corresponding result fields.2

Input fieldMeaning and unitsAccepted input
movementsMovementsRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 16.
movements[].idLiteral identifier used to match records, independently of their display labels.Required. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
movements[].levelLevelRequired. Text; one of "low", "mid", "high".
lowQuotaLow QuotaRequired. Safe integer; minimum 0; maximum 32.
midQuotaMid QuotaRequired. Safe integer; minimum 0; maximum 32.
highQuotaHigh QuotaRequired. Safe integer; minimum 0; maximum 32.
phraseCountPhrase CountRequired. Safe integer; minimum 1; maximum 32.
seedOptional numeric seed; takes precedence over context randomness. Without either, use the fixed tool.io default seed.Optional. Safe integer.
Output fieldInterpretationReturned shape
phrasesPhrasesRequired. Array of Object; no unknown keys.; minimum items 1; maximum items 32.
phrases[].phrasePhraseRequired. Safe integer; minimum 1; maximum 32.
phrases[].levelLevelRequired. Text; one of "low", "mid", "high".
phrases[].movementIdMovement IdRequired. Text; minimum UTF-16 length 1; maximum UTF-16 length 64.
phrases[].remainingQuotasRemaining QuotasRequired. Array of Safe integer; minimum 0; maximum 32.; minimum items 3; maximum items 3.
lowMidHighCountsLow Mid High CountsRequired. Array of Safe integer; minimum 0; maximum 32.; minimum items 3; maximum items 3.
unknownCountUnknown CountRequired. Safe integer; exactly 0.
balanceSpreadBalance SpreadRequired. Safe integer; minimum 0; maximum 32.
callerReviewRequiredCaller Review RequiredRequired. Boolean; exactly true.
randomnessReplay metadata: source is seed or context; seed is the normalized 32-bit seed or null for a context source.Required. Object; no unknown keys.
randomness.seedOptional numeric replay seed; normalization and precedence are explained below.Required. Safe integer. or Null.
randomness.sourceSourceRequired. Text; one of "context", "seed".

An explicit safe-integer seed takes precedence over injected context randomness and is normalized to an unsigned 32-bit integer. With no seed and no context source, replay uses the fixed tool.io default seed. A context source must return finite values from zero inclusive to one exclusive; its advancing state must be retained separately. randomness records the source and normalized seed, or null for context. These generators are not cryptographic random sources.

json
{
  "input": {
    "movements": [
      {
        "id": "kneel",
        "level": "low"
      },
      {
        "id": "reach",
        "level": "high"
      }
    ],
    "lowQuota": 1,
    "midQuota": 0,
    "highQuota": 1,
    "phraseCount": 2,
    "seed": 42
  },
  "output": {
    "phrases": [
      {
        "phrase": 1,
        "level": "high",
        "movementId": "reach",
        "remainingQuotas": [
          1,
          0,
          0
        ]
      },
      {
        "phrase": 2,
        "level": "low",
        "movementId": "kneel",
        "remainingQuotas": [
          0,
          0,
          0
        ]
      }
    ],
    "lowMidHighCounts": [
      1,
      0,
      1
    ],
    "unknownCount": 0,
    "balanceSpread": 1,
    "callerReviewRequired": true,
    "randomness": {
      "seed": 42,
      "source": "seed"
    }
  }
}

Limitations and assumptions

  • Quotas must sum to phraseCount and every requested level needs a movement. Level labels describe supplied choreography metadata, not physical assessment; callerReviewRequired remains true.
  • A replay requires the same complete input and implementation. Randomized construction does not guarantee uniform coverage of every feasible output; finite samples can repeat unless a distinct-selection rule is explicitly stated.
  • The field tables state structural bounds; cross-field constraints and bounded search or output budgets also apply. Unknown keys are rejected where the object contract disallows them. Input errors are distinct from null fields and deliberate invalid controls in a successful output.

Alternative or Complementary methods

For a supplied example, inspect the returned construction or delegated check directly. To evaluate a consumer, also test cases chosen for its own boundaries; a generated finite corpus does not establish exhaustive coverage.

References

  1. Tool behavior and example: Random Movement Level Balance Plan Generator — tool.io

  2. Calculation or validation: dance/level-change-low-mid-high-distribution-counter — tool.io

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