token-budget-advisor
Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
Best use case
token-budget-advisor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
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?
Offers the user an informed choice about how much response depth to consume before answering. Use this skill when the user explicitly wants to control response length, depth, or token budget. TRIGGER when: "token budget", "token count", "token usage", "token limit", "response length", "answer depth", "short version", "brief answer", "detailed answer", "exhaustive answer", "respuesta corta vs larga", "cuántos tokens", "ahorrar tokens", "responde al 50%", "dame la versión corta", "quiero controlar cuánto usas", or clear variants where the user is explicitly asking to control answer size or depth. DO NOT TRIGGER when: user has already specified a level in the current session (maintain it), the request is clearly a one-word answer, or "token" refers to auth/session/payment tokens rather than response size.
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** Gemini 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](../egc-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 Originally from [Token Budget Advisor](https://github.com/Xabilimon1/Token-Budget-Advisor-Claude-Code-), adapted for Gemini CLI. Original project also ships a Python estimator script, but this repository keeps the skill self-contained and heuristic-only.
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