hm-llm-guardrails

hm-llm-guardrails is a skill for Claude Code, Codex from rodrigohighermind/highermind-code-skills. It costs 149 tokens per session (3,695 once invoked), scanned A, original, MIT.

A production checklist for applications that use large language models such as Claude, GPT, Gemini, or Llama through an API. It covers safeguards for chat history, limits, retries, fallbacks, timeouts, duplicate requests, monitoring, tools, agents, and persistent chat.

In plain words
What is it for?
Use it before releasing an AI feature, when API costs or response times rise, when adding tool calling or agents, when storing conversations, or when connecting another model provider.
Why use it?
Language-model services can respond unpredictably, take varying amounts of time, and charge per request. The checklist helps prevent runaway costs, stalled requests, weak failure handling, and missing operational visibility.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it before releasing an AI feature, when API costs or response times rise, when adding tool calling or agents, when storing conversations, or when connecting another model provider.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails
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 rodrigohighermind/highermind-code-skills --skill hm-llm-guardrails
Clone the repo
git clone --depth 1 https://github.com/rodrigohighermind/highermind-code-skills

Made for: Claude Code, Codex.

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.

agentmods badge for hm-llm-guardrails

README.md
[![agentmods](https://agentmods.dev/badge/skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails/github.svg)](https://agentmods.dev/skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails)
Your own site
<a href="https://agentmods.dev/skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails"><img src="https://agentmods.dev/badge/skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails/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.

agentmods 80×15 button for hm-llm-guardrails

Your own site · 80×15
<a href="https://agentmods.dev/skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails"><img src="https://agentmods.dev/badge/skills/rodrigohighermind/highermind-code-skills/hm-llm-guardrails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 149 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,695 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 pass 7 Sept 2026
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00149 $0.03695
Opus 5.5 $0.00060 $0.01478
Sonnet 5.5 $0.00030 $0.00739
Haiku 4.5 $0.00015 $0.00369

Measured 2mo ago against content hash 02675d60cb5a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

hm-llm-guardrails 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 2mo 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.

hm-llm-guardrails/SKILL.md · 370 lines

How it starts

The opening of the file, as written. The whole thing — 370 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/hm-llm-guardrails — Patterns LLM-app (v2)

Você está agora em modo LLM guardrails. Seu trabalho e validar que app que integra LLM (Claude, GPT, Gemini, Llama via API) tem todos os guardrails de produção em pe. Não esta otimizado pra cobrir um caso de uso. Cobre TODOS os patterns recorrentes.

Princípio central

LLMs sao componentes não-deterministicos com custo variavel e latencia variavel, em provedores externos. Tratar como qualquer outra dependência externa: rate limit, retry, fallback, timeout, dedupe, observabilidade. Sem guardrails, você não tem produto — tem demo.

Quando usar

  • Antes de shippar feature LLM pra produção
  • Quando custo de API explode sem explicacao
  • Quando user reclama de "respostas demoram demais", "trava no meio", "respondeu coisa errada"
  • Quando adicionar tool calling, agentes, ou chat persistente
  • Apos integrar provider novo (multi-provider routing)

Patterns obrigatorios

1. Sliding window de chat history

Problema: chat history sem limite cresce sem limite. Conversa de 50+ turns estoura context window (200k tokens em Sonnet 4.6 / 1M com beta header) ou explode custo.

Pattern:

// SQL/ORM: últimas N messages, ordem cronologica preservada
const recent = await db
  .select()
  .from(messages)
  .where(eq(messages.threadId, id))
  .orderBy(desc(messages.createdAt))
  .limit(CHAT_HISTORY_LIMIT) // tipicamente 30
const history = recent.reverse()

Limite típico: 30 turns (15 user/assistant pares). Suficiente pra contexto recente, longe do limite de tokens.

Avancado: Sliding window + summary das mensagens antigas (compactacao). Soma das duas no prompt = janela infinita efetiva. Custa 1 LLM call extra periodicamente pra gerar summary.

2. Lazy client factory (sem singleton stale)

Problema: SDK client (Anthropic, OpenAI) instanciado no module-load com getApiKey(). Se user trocar key em runtime (settings page), continua usando a antiga até restart.

Pattern:

let cachedClient: { key: string; client: Anthropic } | null = null

export class ApiKeyMissingError extends Error {}

export function getAnthropic(): Anthropic {
  const key = getApiKey()
  if (!key) throw new ApiKeyMissingError()
  if (cachedClient && cachedClient.key === key) return cachedClient.client
  cachedClient = { key, client: new Anthropic({ apiKey: key, timeout: 90_000, maxRetries: 1 }) }
  return cachedClient.client
}

Read the full file on GitHub · 370 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.

  1. 2mo ago First seen · 370 lines · 149 tokens per session scan A 02675d60cb5a

Subscribe to this mod's changes

hm-llm-guardrails is a skill published in the GitHub repository rodrigohighermind/highermind-code-skills (197 stars, last pushed 4mo ago), licensed MIT. It adds 149 tokens to every session and 3,695 once invoked, about $0.0006 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 2026-08-30.

Related

hm-llm-guardrails compared with skills from other repositories

hm-llm-guardrails next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
hm-llm-guardrailsrodrigohighermind/highermind-code-skillsSkill1974mo agoMITA149Claude Code, Codex
llm-app-patternsdavila7/claude-code-templatesSkill33ktodayMITA54Claude Code, Codex
prompt-engineerJeffallan/claude-skillsSkill12k7d agoMITA93Claude Code
prompt-optimizationlangwatch/langwatchSkill5.0kyesterdayApache-2.0A105Claude Code, Codex
omh-model-optimizationrlaope/oh-my-hermesSkill5.2ktodayMITA84Claude Code, Codex
ideogram4digitalsamba/claude-code-video-toolkitSkill2.5ktodayMITA99Claude Code
prompt-library-for-startupsaws/agent-toolkit-for-awsSkill3.4kyesterdayApache-2.0A228Claude Code