everything-claude-code/skills/token-budget-advisor/SKILL.md

name, description, origin

name	description	origin
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: "token budget", "token count", "token usage", "token limit", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", "75%", "100%", "at 25%", "at 50%", "at 75%", "at 100%", "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 (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%", "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" / "full"	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.