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 fablegit 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/fable)<a href="https://agentmods.dev/skills/leolin990405/r-analytics-skill/fable"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/fable/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/fable"><img src="https://agentmods.dev/badge/skills/leolin990405/r-analytics-skill/fable.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 | $0.00022 | $0.00497 |
| Opus 5 | $0.00011 | $0.00249 |
| Sonnet 5 | $0.00004 | $0.00099 |
| Haiku 4.5 | $0.00002 | $0.00050 |
Grade A, and why
fable 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 9d 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.
What it actually says
fable Package
Tidy time series forecasting.
Setup
library(fable)
library(tsibble)
library(feasts)
# Create tsibble
ts_data <- df %>%
as_tsibble(index = date, key = id)
Basic Forecasting
# Fit models
fit <- ts_data %>%
model(
arima = ARIMA(value),
ets = ETS(value),
naive = NAIVE(value)
)
# Forecast
fc <- fit %>% forecast(h = 12)
# Plot
fc %>% autoplot(ts_data)
Model Specifications
# ARIMA
ARIMA(value)
ARIMA(value ~ pdq(1,1,1) + PDQ(1,1,1))
# ETS
ETS(value)
ETS(value ~ error("A") + trend("A") + season("M"))
# TSLM (regression)
TSLM(value ~ trend() + season())
# Prophet
prophet(value ~ season(period = "year", order = 10))
Multiple Series
# Fit by group
fit <- ts_data %>%
model(arima = ARIMA(value))
# Forecast all
fc <- fit %>% forecast(h = 12)
# Accuracy by group
accuracy(fit)
Decomposition
ts_data %>%
model(STL(value ~ season(window = "periodic"))) %>%
components() %>%
autoplot()
Cross-Validation
# Stretch tsibble
cv_data <- ts_data %>%
stretch_tsibble(.init = 100, .step = 1)
# Fit and forecast
cv_fc <- cv_data %>%
model(ARIMA(value)) %>%
forecast(h = 1)
# Accuracy
cv_fc %>% accuracy(ts_data)
Reconciliation
# Hierarchical forecasting
fit <- ts_data %>%
aggregate_key(region / store, value = sum(value)) %>%
model(ets = ETS(value))
fc <- fit %>%
reconcile(ets = min_trace(ets)) %>%
forecast(h = 12)
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.
- 9d ago First seen · 109 lines · 22 tokens per session scan A ca4ed8c96c18
fable is a skill published in the GitHub repository LeoLin990405/r-analytics-skill (5 stars, last pushed 6mo ago), licensed MIT. It adds 22 tokens to every session and 497 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
bio-applied-molecular-evolution
Test Hardy-Weinberg equilibrium, simulate Wright-Fisher drift/selection, and compute dN/dS, Tajima's D, and Fst with NumPy/SciPy. Use for neutral theory, molecular clock divergence time, selection scans, or effective population size (Ne) questions.
advanced-string-structures
Build tries, Aho-Corasick, and suffix arrays with Kasai LCP to index DNA/text and match many patterns in one pass. Use for genome motif scanning, k-mer indexing, longest-repeat search, or BWA/FM-index groundwork.
ai-science-esm2-embeddings
Generate ESM2 protein embeddings (fair-esm/transformers) and predict structure with ESMFold. Use when embedding sequences, scoring mutations zero-shot, annotating protein function, or doing fast MSA-free structure prediction.
ai-science-geneformer-scgpt
Tokenize scRNA-seq via Geneformer gene-rank or scGPT expression-bin encoding; annotate cell types, simulate in-silico knockouts. Use for foundation-model cell annotation, Geneformer/scGPT tokenization, or perturbation prediction.
ai-science-zero-shot-mutation
Score protein point mutations zero-shot with ESM-1v/ESM-2 masked-LM log-odds, ensembled, benchmarked on ProteinGym DMS. Use when predicting mutation effects, ranking missense variants, scoring VUS fitness with no labels.
bio-applied-advanced-ngs
Assemble genomes de novo: greedy OLC, de Bruijn graph/Eulerian path, N50/L50/NG50 stats, SPAdes/Flye/hifiasm CLI usage. Use when choosing k-mer size, picking an assembler for Illumina/ONT/HiFi reads, or scoring contiguity.