affaan-m/ECC

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

85Collecting
See how to use itView GitHub source
npx skills add https://github.com/affaan-m/ECC --skill "skills/token-budget-advisor"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn token-budget-advisor's source instructions into a guide you can follow

According to the pinned SKILL.md from affaan-m/ECC: Intercept the response flow to offer the user a choice about response depth before Claude answers.

npx skills add https://github.com/affaan-m/ECC --skill "skills/token-budget-advisor"
Check the pinned source

Best fit

  • 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.

Bring this context

  • prose: words × 1.3
  • code-heavy or mixed/code blocks: chars / 4
  • Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.

Expected outputs

  • "Give me the short version first."
  • "How many tokens will your answer use?"
  • "Respond at 50% depth."

Key source sections

Read token-budget-advisor through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

Step 1 — Estimate input tokens

Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.

SKILL.md · Step 1 — Estimate input tokens
prose: words × 1.3code-heavy or mixed/code blocks: chars / 4Use the repository's canonical context-budget heuristics to estimate the prompt's token count mentally.
02

Step 2 — Estimate response size by complexity

Classify the prompt, then apply the multiplier range to get the full response window:

SKILL.md · Step 2 — Estimate response size by complexity
Classify the prompt, then apply the multiplier range to get the full response window:Response window = inputtokens × multmin to inputtokens × multmax (but don’t exceed your model’s configured output-token limit).
03

Step 3 — Present depth options

Present this block before answering, using the actual estimated numbers:

SKILL.md · Step 3 — Present depth options
25% → min + (max - min) × 0.2550% → min + (max - min) × 0.5075% → min + (max - min) × 0.75
04

Step 4 — Respond at the chosen level

Review the “Step 4 — Respond at the chosen level” section in the pinned source before continuing.

SKILL.md · Step 4 — Respond at the chosen level
Review and apply the “Step 4 — Respond at the chosen level” source section.
05

When to Use

Do not trigger when: user already set a level this session (maintain it silently), or the answer is trivially one line.

SKILL.md · When to Use
User wants to control how long or detailed a response isUser mentions tokens, budget, depth, or response lengthUser says "short version", "tldr", "brief", "al 25%", "exhaustive", etc.

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use token-budget-advisor to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned token-budget-advisor source to [task]. Pay particular attention to these source sections: “Step 1 — Estimate input tokens”, “Step 2 — Estimate response size by complexity”, “Step 3 — Present depth options”, “Step 4 — Respond at the chosen level”, “When to Use”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current token-budget-advisor result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “Step 1 — Estimate input tokens” has been checked.

The source section “Step 2 — Estimate response size by complexity” has been checked.

The source section “Step 3 — Present depth options” has been checked.

The source section “Step 4 — Respond at the chosen level” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

token-budget-advisor

回答する前に、どれだけの回答深度を消費するかについてユーザーに情報に基づいた選択を提供する。ユーザーが回答の長さ、深さ、またはトークンバジェットを明示的に制御したい場合にこのスキルを使用する。トリガー条件:"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"、またはユーザーが回答のサイズや深さの制御を明示的に求めるその他の明確なバリエーション。トリガーしない条件:ユーザーが現在のセッションでレベルを指定済み(そのレベルを維持)、リクエ

A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.

Open source detail

token-budget-advisor

在回答前,为用户提供关于消耗多少响应深度的知情选择。当用户明确希望控制响应长度、深度或令牌预算时使用此技能。触发条件:"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",或用户明确要求控制答案大小或深度的清晰变体。不触发条件:用户已在当前会话中指定了级别(保持该级别),请求明显是单字答案,或"token"指代认证/会话/支付令牌而非响应大小。

A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.

Open source detail

ab-testing

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program

A separate implementation from coreyhaines31/marketingskills; compare its source, maintenance signals, and permission requirements.

Open source detail

FAQ

What does token-budget-advisor do?

Intercept the response flow to offer the user a choice about response depth before Claude answers.

How do I start using token-budget-advisor?

The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/token-budget-advisor". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
234,327
Repository forks
35,711
Quality
85/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

85/100
Documentation26/30
Specificity19/25
Maintenance20/20
Trust signals20/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

token-budget-advisor by affaan-m

回答する前に、どれだけの回答深度を消費するかについてユーザーに情報に基づいた選択を提供する。ユーザーが回答の長さ、深さ、またはトークンバジェットを明示的に制御したい場合にこのスキルを使用する。トリガー条件:"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"、またはユーザーが回答のサイズや深さの制御を明示的に求めるその他の明確なバリエーション。トリガーしない条件:ユーザーが現在のセッションでレベルを指定済み(そのレベルを維持)、リクエ

token-budget-advisor by affaan-m

在回答前,为用户提供关于消耗多少响应深度的知情选择。当用户明确希望控制响应长度、深度或令牌预算时使用此技能。触发条件:"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",或用户明确要求控制答案大小或深度的清晰变体。不触发条件:用户已在当前会话中指定了级别(保持该级别),请求明显是单字答案,或"token"指代认证/会话/支付令牌而非响应大小。

ab-testing by coreyhaines31

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program

churn-prevention by coreyhaines31

When the user wants to reduce churn, build cancellation flows, set up save offers, recover failed payments, or implement retention strategies. Also use when the user mentions 'churn,' 'cancel flow,' 'offboarding,' 'save offer,' 'dunning,' 'failed payment recovery,' 'win-back,' 'retention,' 'exit survey,' 'pause subscription,' 'involuntary churn,' 'people keep canceling,' 'churn rate is too high,' 'how do I keep users,' or 'customers are leaving.' Use this whenever someone is losing subscribers o

design-intelligence by event4u-app

Grounded design brief from the adopted corpus — style, WCAG-checked color tokens, typography, layout pattern, anti-patterns. Use on ui-design-brief or any which-style/palette/font/chart decision.

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 3 min

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:

ComplexityMultiplier rangeExample prompts
Simple3× – 8×"What is X?", yes/no, single fact
Medium8× – 20×"How does X work?"
Medium-High10× – 25×Code request with context
Complex15× – 40×Multi-part analysis, comparisons, architecture
Creative10× – 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

LevelTarget lengthIncludeOmit
25% Essential2-4 sentences maxDirect answer, key conclusionContext, examples, nuance, alternatives
50% Moderate1-3 paragraphsAnswer + necessary context + 1 exampleDeep analysis, edge cases, references
75% DetailedStructured responseMultiple examples, pros/cons, alternativesExtreme edge cases, exhaustive references
100% ExhaustiveNo restrictionEverything — full analysis, all code, all perspectivesNothing

Shortcuts — skip the question

If the user already signals a level, respond at that level immediately without asking:

What they sayLevel
"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.

Source repo
affaan-m/ECC
Skill path
skills/token-budget-advisor/SKILL.md
Commit SHA
4e973d3eaf92
Repository license
MIT
Data collected