Install
$ agentstack add skill-leolin990405-r-analytics-skill-qs ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ● Environment & secrets Used
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
qs
Quick serialization of R objects.
Basic Usage
library(qs)
# Save any R object
qsave(obj, "data.qs")
# Load
obj <- qread("data.qs")
Presets
# Fast preset (speed priority)
qsave(obj, "data.qs", preset = "fast")
# High preset (compression priority)
qsave(obj, "data.qs", preset = "high")
# Balanced preset (default)
qsave(obj, "data.qs", preset = "balanced")
# Archive preset (maximum compression)
qsave(obj, "data.qs", preset = "archive")
# Uncompressed
qsave(obj, "data.qs", preset = "uncompressed")
Custom Settings
# Custom compression
qsave(obj, "data.qs",
algorithm = "zstd", # or "lz4", "zstd_stream", "lz4_stream"
compress_level = 4, # 1-22 for zstd, 1-12 for lz4
nthreads = 4
)
# Shuffle for better compression of numeric data
qsave(obj, "data.qs", shuffle_control = 15)
Supported Objects
# qs supports virtually all R objects:
# - Data frames, tibbles, data.tables
# - Lists, environments
# - Matrices, arrays
# - Functions, formulas
# - S3, S4, R6 objects
# - Factors with levels
# - Attributes preserved
# Complex nested structures
complex_obj <- list(
df = data.frame(x = 1:100),
model = lm(y ~ x, data = df),
func = function(x) x^2,
env = new.env()
)
qsave(complex_obj, "complex.qs")
Streaming
# Save to connection
con <- file("data.qs", "wb")
qsave(obj, con)
close(con)
# Read from connection
con <- file("data.qs", "rb")
obj <- qread(con)
close(con)
# Save to raw vector
raw_data <- qserialize(obj)
# Load from raw vector
obj <- qdeserialize(raw_data)
Performance
# qs is typically:
# - 3-10x faster than saveRDS
# - Better compression than RDS
# - Supports multithreading
# Benchmark
library(microbenchmark)
microbenchmark(
qs = qsave(df, "test.qs"),
rds = saveRDS(df, "test.rds"),
times = 10
)
Thread Control
# Set threads for save/load
qsave(obj, "data.qs", nthreads = 4)
obj <- qread("data.qs", nthreads = 4)
# Check available threads
qs::qread_threads()
Strict Mode
# Strict mode for reproducibility
qsave(obj, "data.qs", strict = TRUE)
# Validates object integrity on read
obj <- qread("data.qs", strict = TRUE)
Hash Verification
# Save with hash
qsave(obj, "data.qs", check_hash = TRUE)
# Verify on read
obj <- qread("data.qs", validate_checksum = TRUE)
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: LeoLin990405
- Source: LeoLin990405/r-analytics-skill
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.