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Control Response Length

Your AI offers you choices on how long or detailed its answers should be before responding. It estimates input complexity and token usage to suggest brief, medium, or exhaustive responses based on your request.

affaan-m on GitHub

Curated by PromptsEdge from a public repo · MIT license. All credit goes to the author.

What is this skill?

Intercept the response flow to offer the user a choice about response depth before Claude answers.

When to use

  • User wants to control how long or detailed a response is
  • User mentions tokens, budget, depth, or response length
  • User says "short version", "tldr", "brief", "al 25%", "exhaustive", etc.
  • Any time the user wants to choose depth/detail level upfront

How It Works

Step 1 — Estimate input tokens

Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.

Use the same calibration guidance as context-budget:

  • prose: words × 1.3
  • code-heavy or mixed/code blocks: chars / 4

For mixed content, use the dominant content type and keep the estimate heuristic.

Step 2 — Estimate response size by complexity

Classify the prompt, then apply the multiplier range to get the full response window:

ComplexityMultiplier rangeExample prompts
Simple3× – 8×"What is X?", yes/no, single fact
Medium8× – 20×"How does X work?"
Medium-High10× – 25×Code request with context
Complex15× – 40×Multi-part analysis, comparisons, architecture
Creative10× – 30×Stories, essays, narrative writing

Response window = input_tokens × mult_min to input_tokens × mult_max (but don’t exceed your model’s configured output-token limit).

Step 3 — Present depth options

Present this block before answering, using the actual estimated numbers:

Analyzing your prompt...

Input: ~[N] tokens  |  Type: [type]  |  Complexity: [level]  |  Language: [lang]

Choose your depth level:

[1] Essential   (25%)  ->  ~[tokens]   Direct answer only, no preamble
[2] Moderate    (50%)  ->  ~[tokens]   Answer + context + 1 example
[3] Detailed    (75%)  ->  ~[tokens]   Full answer with alternatives
[4] Exhaustive (100%)  ->  ~[tokens]   Everything, no limits

Which level? (1-4 or say "25% depth", "50% depth", "75% depth", "100% depth")

Precision: heuristic estimate ~85-90% accuracy (±15%).

Level token estimates (within the response window):

  • 25% → min + (max - min) × 0.25
  • 50% → min + (max - min) × 0.50
  • 75% → min + (max - min) × 0.75
  • 100% → max

Step 4 — Respond at the chosen level

LevelTarget lengthIncludeOmit
25% Essential2-4 sentences maxDirect answer, key conclusionContext, examples, nuance, alternatives
50% Moderate1-3 paragraphsAnswer + necessary context + 1 exampleDeep analysis, edge cases, references
75% DetailedStructured responseMultiple examples, pros/cons, alternativesExtreme edge cases, exhaustive references
100% ExhaustiveNo restrictionEverything — full analysis, all code, all perspectivesNothing

Shortcuts — skip the question

If the user already signals a level, respond at that level immediately without asking:

What they sayLevel
"1" / "25% depth" / "short version" / "brief answer" / "tldr"25%
"2" / "50% depth" / "moderate depth" / "balanced answer"50%
"3" / "75% depth" / "detailed answer" / "thorough answer"75%
"4" / "100% depth" / "exhaustive answer" / "full deep dive"100%

If the user set a level earlier in the session, maintain it silently for subsequent responses unless they change it.

Precision note

This skill uses heuristic estimation — no real tokenizer. Accuracy ~85-90%, variance ±15%. Always show the disclaimer.

Examples

Triggers

  • "Give me the short version first."
  • "How many tokens will your answer use?"
  • "Respond at 50% depth."
  • "I want the exhaustive answer, not the summary."
  • "Dame la version corta y luego la detallada."

Does Not Trigger

  • "What is a JWT token?"
  • "The checkout flow uses a payment token."
  • "Is this normal?"
  • "Complete the refactor."
  • Follow-up questions after the user already chose a depth for the session

Source

Standalone skill from TBA — Token Budget Advisor for Claude Code. Original project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.

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