token-budget-advisor

token-budget-advisor

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.

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:

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% 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

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

"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. Weekly Installs501Repositoryaffaan-m/everyt…ude-codeGitHub Stars144.9KFirst Seen10 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykFailInstalled oncodex466opencode444cursor439github-copilot438antigravity438gemini-cli437