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 LeoLin990405/r-analytics-skill --skill logginggit clone --depth 1 https://github.com/LeoLin990405/r-analytics-skillWrote 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/leolin990405/r-analytics-skill/logging)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/logging"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/logging/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/leolin990405/r-analytics-skill/logging"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/logging.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 | $0.00019 | $0.00895 |
| Opus 5 | $0.00010 | $0.00447 |
| Sonnet 5 | $0.00004 | $0.00179 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
logging 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 8d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
logging
Python-style logging for R.
Basic Usage
library(logging)
# Initialize basic configuration
basicConfig()
# Log messages
logdebug("Debug message")
loginfo("Info message")
logwarn("Warning message")
logerror("Error message")
logfinest("Finest message")
Configuration
# Basic config with level
basicConfig(level = "DEBUG")
# With file handler
basicConfig(level = "INFO")
addHandler(writeToFile, file = "app.log")
# Custom format
basicConfig(level = "INFO")
Log Levels
# Levels (lowest to highest)
# FINEST < FINER < FINE < DEBUG < INFO < WARNING < ERROR
# Set level
setLevel("DEBUG")
setLevel("INFO", container = "logger.name")
# Get level
getLevel()
Named Loggers
# Get/create logger
logger <- getLogger("myapp")
# Set level for specific logger
setLevel("DEBUG", container = "myapp")
# Log with logger name
loginfo("Message", logger = "myapp")
# Hierarchical loggers
setLevel("INFO", container = "myapp")
setLevel("DEBUG", container = "myapp.database")
Handlers
# Console handler (default)
addHandler(writeToConsole)
# File handler
addHandler(writeToFile, file = "app.log")
# Custom handler
myHandler <- function(msg, handler) {
# Custom logic
cat(msg, "\n")
}
addHandler(myHandler)
# Remove handler
removeHandler("writeToConsole")
Formatters
# Default format includes:
# - Timestamp
# - Level
# - Logger name
# - Message
# Custom formatter
formatter <- function(record) {
sprintf("[%s] [%s] %s",
record$timestamp,
record$levelname,
record$msg)
}
Formatted Messages
# Printf-style
loginfo("Processing file: %s", filename)
loginfo("Completed %d of %d items", current, total)
# Multiple arguments
loginfo("User %s action %s", user, action)
Exception Logging
tryCatch({
risky_operation()
}, error = function(e) {
logerror("Operation failed: %s", e$message)
})
# With condition
logwarn("Condition: %s", conditionMessage(cond))
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.
- 8d ago First seen · 210 lines · 19 tokens per session scan A c0b9432b0d12
logging is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 6mo ago), licensed MIT. It adds 19 tokens to every session and 895 once invoked, about $0.0001 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
alibaba-observability-incident-responder
Respond to Alibaba Cloud incidents using CloudMonitor alarms, SLS log analytics, ARMS APM distributed tracing, and alert governance for ECS, RDS, ACK, and network services.
azure-resource-health-incident-triage
Use this skill for Azure Resource Health, Service Health, activity-log alert, and first-pass incident triage when the question is whether Azure platform health is part of the problem.
aws-observability-incident-responder
Investigate broad AWS incidents and observability gaps using CloudWatch metrics, logs, alarms, traces, EventBridge events, service health, runbooks, timelines, blast radius, root-cause discipline, and post-incident actions. Prefer RDS/Aurora investigator for database-specific performance incidents.
aws-rds-aurora-performance-investigator
Investigate Amazon RDS and Aurora-specific incidents involving latency, connection exhaustion, slow queries, lock waits, storage pressure, CPU/I/O saturation, replica lag, failover behavior, Performance Insights, and database capacity. Prefer this for database performance; prefer broad observability responder for…
azure-cosmosdb-performance-investigator
Use this skill for Azure Cosmos DB performance investigation, especially RU spikes, query latency, throttling, hot partitions, indexing inefficiency, partition-skew analysis, request-charge profiling, diagnostic-log review, and evidence-driven remediation planning.
azure-observability-investigator
Use this skill for Azure Monitor, Log Analytics, Application Insights, alerting, KQL triage, telemetry-gap analysis, workbooks, or operator-grade incident and posture investigations.