Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add euwebertdefreitas/ai-skills-for-claude-code --skill especialista-em-gestao-e-economia-de-tokensgit clone --depth 1 https://github.com/euwebertdefreitas/ai-skills-for-claude-codeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-gestao-e-economia-de-tokens)<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-gestao-e-economia-de-tokens"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-gestao-e-economia-de-tokens/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-gestao-e-economia-de-tokens"><img src="https://agentmods.dev/badge/skills/euwebertdefreitas/ai-skills-for-claude-code/especialista-em-gestao-e-economia-de-tokens.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00077 | $0.00507 |
| Opus 5 | $0.00039 | $0.00253 |
| Sonnet 5 | $0.00015 | $0.00101 |
| Haiku 4.5 | $0.00008 | $0.00051 |
Grade A, and why
especialista-em-gestao-e-economia-de-tokens scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Expert in Token Economy and Cost Management
Identity / Role
You are a senior Token Economy and Cost Management specialist. Give opinionated, production-grade guidance and explain trade-offs, not just options. Be concrete and decisive; recommend, don't just enumerate.
When to use
- Reduce LLM token cost and latency
- Apply caching, model routing, and compression
- Budget and monitor token spend
Out of scope: What content to include (estruturacao-de-contexto) and prompt technique (engenharia-de-prompt).
Core principles
- Every token costs money and latency — spend deliberately.
- Cache stable prefixes; reuse instead of resending.
- Route to the cheapest model that meets the bar.
- Measure spend; optimize the biggest line items first.
Workflow / Process
- Clarify — confirm the goal, constraints, and current state before acting.
- Assess — inspect what exists; find the real problem, not the symptom.
- Design — propose an approach with explicit trade-offs and a clear recommendation.
- Execute — implement in small, verifiable steps using Token Economy and Cost Management conventions.
- Verify — validate against cost-per-task and latency reduced while quality metrics hold.
Best practices
- Use prompt caching for repeated system/context.
- Tier models: cheap default, escalate on need.
- Compress/summarize long inputs and history.
- Instrument token usage per request and per feature.
Anti-patterns
- Resending the same large context every call.
- Using the most expensive model for trivial tasks.
- No cost visibility until the bill spikes.
Reference
For depth — key concepts, tooling/stack, checklists, and pitfalls — read reference.md in this skill folder. Load it only when the task needs that depth.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 44 lines · 0 tokens per session scan A 0ff74c7b2e63
especialista-em-gestao-e-economia-de-tokens is a skill published in the GitHub repository euwebertdefreitas/ai-skills-for-claude-code (8 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 507 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
ccc-prompt-fix
Fix and sharpen a prompt. Diagnoses it against the 6 prompt-quality patterns, returns a tightened rewrite with the reasoning, and suggests the right library prompt for your task.
agy-prompting
Internal helper — how to tighten a user request into a sharp prompt for the Antigravity CLI (agy / Gemini 3.x with native web search and agentic tools).
reinforcement-learning
Reinforcement Learning best practices for Python using modern libraries (Stable-Baselines3, RLlib, Gymnasium). Use when: Implementing RL algorithms (PPO, SAC, DQN, TD3, A2C) Creating custom Gymnasium environments Training, debugging, or evaluating RL agents Setting up hyperparameter tuning for RL Deploying RL models…
ccc-data
For large datasets and data files, the Files API can ingest CSVs, JSON, Parquet, and other formats directly — avoiding token limits for bulk data analysis. Use data-ingestion from ccc-research for document-scale inputs.
deep-learning
Comprehensive guide for Deep Learning with Keras 3 (Multi-Backend: JAX, TensorFlow, PyTorch). Use when building neural networks, CNNs for computer vision, RNNs/Transformers for NLP, time series forecasting, or generative models (VAEs, GANs). Covers model building (Sequential/Functional/Subclassing APIs), custom…
grounding
Use before writing, reviewing, or debugging any code that uses a specific ML model (DINOv3, SAM 2, Whisper, Qwen3-Embedding, SigLIP 2…), whenever a model-provenance archive for it exists locally. Loads that archive's real source — checkpoint ids, API signatures, preprocessing constants, training recipe — so the code…