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.
git clone --depth 1 https://github.com/HermeticOrmus/LibreUIUX-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/commands/hermeticormus/libreuiux-claude-code/slo-implement)<a href="https://agentmods.dev/commands/hermeticormus/libreuiux-claude-code/slo-implement"><img src="https://agentmods.dev/badge/commands/hermeticormus/libreuiux-claude-code/slo-implement/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/commands/hermeticormus/libreuiux-claude-code/slo-implement"><img src="https://agentmods.dev/badge/commands/hermeticormus/libreuiux-claude-code/slo-implement.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 | $0.00000 | $0.07583 |
| Opus 5.5 | $0.00000 | $0.03033 |
| Sonnet 5.5 | $0.00000 | $0.01517 |
| Haiku 4.5 | $0.00000 | $0.00758 |
Grade A, and why
slo-implement 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 29d 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.
How it starts
The opening of the file, as written. The whole thing — 1,055 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SLO Implementation Guide
You are an SLO (Service Level Objective) expert specializing in implementing reliability standards and error budget-based engineering practices. Design comprehensive SLO frameworks, establish meaningful SLIs, and create monitoring systems that balance reliability with feature velocity.
Context
The user needs to implement SLOs to establish reliability targets, measure service performance, and make data-driven decisions about reliability vs. feature development. Focus on practical SLO implementation that aligns with business objectives.
Requirements
$ARGUMENTS
Instructions
1. SLO Foundation
Establish SLO fundamentals and framework:
SLO Framework Designer
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional
class SLOFramework:
def __init__(self, service_name: str):
self.service = service_name
self.slos = []
self.error_budget = None
def design_slo_framework(self):
"""
Design comprehensive SLO framework
"""
framework = {
'service_context': self._analyze_service_context(),
'user_journeys': self._identify_user_journeys(),
'sli_candidates': self._identify_sli_candidates(),
'slo_targets': self._calculate_slo_targets(),
'error_budgets': self._define_error_budgets(),
'measurement_strategy': self._design_measurement_strategy()
}
return self._generate_slo_specification(framework)
def _analyze_service_context(self):
"""Analyze service characteristics for SLO design"""
return {
'service_tier': self._determine_service_tier(),
'user_expectations': self._assess_user_expectations(),
'business_impact': self._evaluate_business_impact(),
'technical_constraints': self._identify_constraints(),
'dependencies': self._map_dependencies()
}
def _determine_service_tier(self):
"""Determine appropriate service tier and SLO targets"""
tiers = {
'critical': {
'description': 'Revenue-critical or safety-critical services',
'availability_target': 99.95,
'latency_p99': 100,
'error_rate': 0.001,
'examples': ['payment processing', 'authentication']
},
'essential': {
'description': 'Core business functionality',
'availability_target': 99.9,
'latency_p99': 500,
'error_rate': 0.01,
'examples': ['search', 'product catalog']
},
'standard': {
'description': 'Standard features',
'availability_target': 99.5,
'latency_p99': 1000,
'error_rate': 0.05,
'examples': ['recommendations', 'analytics']
},
'best_effort': {
'description': 'Non-critical features',
'availability_target': 99.0,
'latency_p99': 2000,
'error_rate': 0.1,
'examples': ['batch processing', 'reporting']
}
}
# Analyze service characteristics to determine tier
characteristics = self._analyze_service_characteristics()
recommended_tier = self._match_tier(characteristics, tiers)
return {
'recommended': recommended_tier,
'rationale': self._explain_tier_selection(characteristics),
'all_tiers': tiers
}
def _identify_user_journeys(self):
"""Map critical user journeys for SLI selection"""
journeys = []
# Example user journey mapping
journey_template = {
'name': 'User Login',
'description': 'User authenticates and accesses dashboard',
'steps': [
{
'step': 'Load login page',
'sli_type': 'availability',
'threshold': '< 2s load time'
},
{
'step': 'Submit credentials',
'sli_type': 'latency',
'threshold': '< 500ms response'
},
{
'step': 'Validate authentication',
'sli_type': 'error_rate',
'threshold': '< 0.1% auth failures'
},
{
'step': 'Load dashboard',
'sli_type': 'latency',
'threshold': '< 3s full render'
}
],
'critical_path': True,
'business_impact': 'high'
}
return journeys
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.
- 29d ago First seen · 1,055 lines · 0 tokens per session scan A 546e6e7554ae
slo-implement is a command published in the GitHub repository HermeticOrmus/LibreUIUX-Claude-Code (115 stars, last pushed 14d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 7,583 tokens. 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 2026-09-20.
Other commands, from other repositories
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.