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 futile.loggergit 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/futile.logger)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/futile.logger"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/futile.logger/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/futile.logger"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/futile.logger.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 | $0.00025 | $0.00864 |
| Opus 5 | $0.00013 | $0.00432 |
| Sonnet 5 | $0.00005 | $0.00173 |
| Haiku 4.5 | $0.00003 | $0.00086 |
Grade A, and why
futile.logger 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 — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
futile.logger
Log4j-style logging for R.
Basic Usage
library(futile.logger)
# Log messages
flog.trace("Trace message")
flog.debug("Debug message")
flog.info("Info message")
flog.warn("Warning message")
flog.error("Error message")
flog.fatal("Fatal message")
Formatted Messages
# Printf-style formatting
flog.info("Processing file: %s", filename)
flog.info("Completed %d of %d items", current, total)
flog.info("Value: %.2f", value)
# Multiple arguments
flog.info("User %s performed action %s at %s", user, action, time)
Log Levels
# Set threshold
flog.threshold(INFO) # Show INFO and above
flog.threshold(DEBUG) # Show DEBUG and above
flog.threshold(WARN) # Show WARN and above
# Get current threshold
flog.threshold()
# Log level constants
TRACE, DEBUG, INFO, WARN, ERROR, FATAL
Named Loggers
# Create named logger
flog.threshold(DEBUG, name = "myapp")
flog.info("Message", name = "myapp")
# Hierarchical loggers
flog.threshold(INFO, name = "myapp")
flog.threshold(DEBUG, name = "myapp.database")
# Child inherits from parent unless overridden
flog.info("DB query", name = "myapp.database")
Appenders
# Console (default)
flog.appender(appender.console())
# File
flog.appender(appender.file("app.log"))
# Tee (console and file)
flog.appender(appender.tee("app.log"))
# Multiple appenders
flog.appender(appender.tee("app.log"), name = "myapp")
# Custom appender
my_appender <- function(line) {
# Custom logic
cat(line, "\n")
}
flog.appender(my_appender)
Layouts
# Default layout
flog.layout(layout.simple)
# With timestamp
flog.layout(layout.format('[~t] [~l] ~m'))
# Custom format
# ~l = level
# ~t = timestamp
# ~n = namespace
# ~f = calling function
# ~m = message
flog.layout(layout.format('[~t] [~l] [~n] ~m'))
# JSON layout
flog.layout(layout.json)
# Tracing layout (includes function name)
flog.layout(layout.tracing)
Conditional Logging
# Check if level is enabled
if (flog.logger()$threshold <= DEBUG) {
# Expensive debug computation
flog.debug("Expensive: %s", expensive_computation())
}
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 · 172 lines · 25 tokens per session scan A 1e9a67ecce12
futile.logger is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 6mo ago), licensed MIT. It adds 25 tokens to every session and 864 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.