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everything-claude-code/skills/token-budget-advisor/SKILL.md
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token-budget-advisor Intercepts the response flow to offer the user an informed choice about how much depth/tokens to consume — BEFORE responding. Use this skill when the user wants to control token consumption, adjust response depth, choose between short/long answers, or optimize their prompt. TRIGGER when: "tokens", "token budget", "profundidad", "consumo", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", "ajusta tu respuesta", "short version", "tldr", "brief", "in 25%", "in 50%", "in 75%", "exhaustive", or any variant where the user wants to control length, depth, or token usage — even without mentioning tokens. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it) or the request is clearly a one-word answer. community

Token Budget Advisor (TBA)

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

Do not trigger when: user already set a level this session (maintain it silently), or the answer is trivially one line.

Workflow

Step 1 — Estimate input tokens

Use the calibration tables below to estimate the prompt's token count mentally.

Chars-per-token by content type:

Content type Chars / Token
English natural ~4.0
Spanish natural ~3.5
Code ~3.0
JSON ~2.8
Markdown ~3.3

Formula: input_tokens ≈ char_count / chars_per_token

For mixed content, use the dominant type's ratio.

Step 2 — Estimate response size by complexity

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

Complexity Multiplier range Example prompts
Simple 3× 8× "What is X?", yes/no, single fact
Medium 8× 20× "How does X work?"
Medium-High 10× 25× Code request with context
Complex 15× 40× Multi-part analysis, comparisons, architecture
Creative 10× 30× Stories, essays, narrative writing

Response window = input_tokens × mult_min to input_tokens × mult_max (cap at 8 000).

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%", "50%", "75%", "100%")

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

Level Target length Include Omit
25% Essential 2-4 sentences max Direct answer, key conclusion Context, examples, nuance, alternatives
50% Moderate 1-3 paragraphs Answer + necessary context + 1 example Deep analysis, edge cases, references
75% Detailed Structured response Multiple examples, pros/cons, alternatives Extreme edge cases, exhaustive references
100% Exhaustive No restriction Everything — full analysis, all code, all perspectives Nothing

Shortcuts — skip the question

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

What they say Level
"25%" / "short" / "brief" / "tldr" / "one-liner" 25%
"50%" / "moderate" / "normal" 50%
"75%" / "detailed" / "thorough" / "complete" 75%
"100%" / "exhaustive" / "everything" / "no limit" 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.

Source

Standalone skill from TBA — Token Budget Advisor for Claude Code. Full version includes a Python estimator script for exact counts: npx token-budget-advisor.