Best for
- 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.
affaan-m/ECC/skills/token-budget-advisor/SKILL.md
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
Decision brief
Intercept the response flow to offer the user a choice about response depth before Claude answers.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Not declared | No explicit evidence | Portability before use |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/affaan-m/ECC --skill "skills/token-budget-advisor"Inspect the Agent Skill "token-budget-advisor" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/skills/token-budget-advisor/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.
Classify the prompt, then apply the multiplier range to get the full response window:
Present this block before answering, using the actual estimated numbers:
Review the “Step 4 — Respond at the chosen level” section in the pinned source before continuing.
Do not trigger when: user already set a level this session (maintain it silently), or the answer is trivially one line.
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 85/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 234,327 | Source | Repository attention, not individual Skill quality |
| Compatibility | 0 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Intercept the response flow to offer the user a choice about response depth before Claude answers.
Do not trigger when: user already set a level this session (maintain it silently), or the answer is trivially one line.
Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.
Use the same calibration guidance as context-budget:
words × 1.3chars / 4For mixed content, use the dominant content type and keep the estimate heuristic.
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).
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):
min + (max - min) × 0.25min + (max - min) × 0.50min + (max - min) × 0.75max| 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 |
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.
This skill uses heuristic estimation — no real tokenizer. Accuracy ~85-90%, variance ±15%. Always show the disclaimer.
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.
Alternatives