What is ag-otimizar-cache?

An auditor for reducing the amount of repeated instruction text sent with coding-agent skills. It scans skill files, estimates token use, reports possible cache locations, and can add cache markers when asked.

Use it to inspect one skill or all skills, find large reusable instruction blocks, estimate prompt-cache savings, or apply cache markers to selected files.

It helps identify skills that consume unnecessary context and shows the estimated difference before changes are applied. The default audit reports findings without modifying files.

ag-otimizar-cache — Skill for Claude Code by andregusman-raiz

Install

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.

Any agent

npx skills add andregusman-raiz/a-gusman-claude --skill ag-otimizar-cache

Clone the repo

git clone --depth 1 https://github.com/andregusman-raiz/a-gusman-claude

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-otimizar-cache/github.svg)](https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-otimizar-cache)

Your own site

<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-otimizar-cache"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-otimizar-cache/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-otimizar-cache"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-otimizar-cache.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,620 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 3 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

medium Agent Snooping · line 3
Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.

Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
medium Agent Snooping · line 36
Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.

Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
medium Excessive Agency · line 4
Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.

Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00050	$0.01620
Opus 5.5	$0.00020	$0.00648
Sonnet 5.5	$0.00010	$0.00324
Haiku 4.5	$0.00005	$0.00162

Measured 1mo ago against content hash 644322b83429, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ag-otimizar-cache 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 1mo 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.

skills/ag-otimizar-cache/SKILL.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AG-OTIMIZAR-CACHE — Auditor de Prompt Cache

Invocacao

/ag-otimizar-cache                        # Dry-run: reporta candidatos (default)
/ag-otimizar-cache --dry-run              # Explicito — so reporta, nao altera
/ag-otimizar-cache --apply                # Aplica markers nos arquivos
/ag-otimizar-cache skill:ag-1-construir   # Audita apenas 1 skill
/ag-otimizar-cache all                    # Audita todas as machines + reference skills

O que faz

Auditoria em 3 fases:

SCAN -> ANALYZE -> REPORT [-> APPLY se --apply]

SCAN: Lista todas as skills em ~/.claude/skills/ag-*/SKILL.md + ag-referencia-*/SKILL.md
ANALYZE: Para cada skill, calcula tamanho, identifica blocos estaticos, detecta candidatos
REPORT: Relatorio com tokens estimados antes/depois por skill
APPLY (so com --apply): Injeta markers  nos pontos corretos

Criterios de candidato (ver prompt-cache-policy.md)

Uma skill e candidata se:

Corpo SKILL.md >= 300 linhas de instrucoes
Tem bloco estatico longo (tabelas, checklists, pipelines) antes de secao dinamica
Invocada frequentemente (machines principais: ag-0 a ag-9, ag-meridian, ag-sentinel, ag-fortress)
NAO tem cache_policy.enabled: false no frontmatter

Estimativa de tokens

Heuristica: tokens ≈ chars / 4 (aproximacao conservadora para PT-BR + codigo misto).

Calculo de economia:

tokens_cacheados = tokens(bloco antes do marker)
tokens_nao_cacheados = tokens(bloco apos o marker)
economia_por_hit = tokens_cacheados × custo_input  (custo de cache hit < custo de input normal)
break_even = 2 invocacoes dentro de 5 min

Obs: valores exatos dependem da API Anthropic — esta skill reporta estimativas por heuristica.

Ponto de insercao do marker

O marker vai IMEDIATAMENTE apos o ultimo bloco estatico e ANTES de qualquer secao dinamica.

Sinais de "secao dinamica" (interrompe o bloco cacheavel):

Secoes com {PROJECT_ROOT}, {ARGS}, {USER_INPUT}
Secoes de "Invocacao" com exemplos variaveis
Secoes de "Output" com placeholders de run

Read the full file on GitHub · 155 lines

Changes

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.

1mo ago First seen · 155 lines · 50 tokens per session scan A 644322b83429

Subscribe to this mod's changes

ag-otimizar-cache is a skill published in the GitHub repository andregusman-raiz/a-gusman-claude (18 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 1,620 once invoked, about $0.0002 per session on Opus 5.5. 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.

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