token-budget-advisor
Intercept the response flow to offer the user a choice about response depth **before** Claude answers
Best use case
token-budget-advisor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Intercept the response flow to offer the user a choice about response depth **before** Claude answers
Teams using token-budget-advisor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/token-budget-advisor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How token-budget-advisor Compares
| Feature / Agent | token-budget-advisor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Intercept the response flow to offer the user a choice about response depth **before** Claude answers
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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](../context-budget/SKILL.md): - 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](https://github.com/Xabilimon1/Token-Budget-Advisor-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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