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/ChrisGVE/localdata-mcpWrote 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/agents/chrisgve/localdata-mcp/forecaster)<a href="https://agentmods.dev/agents/chrisgve/localdata-mcp/forecaster"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/forecaster/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/agents/chrisgve/localdata-mcp/forecaster"><img src="https://agentmods.dev/badge/agents/chrisgve/localdata-mcp/forecaster.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 | $0.00036 | $0.01226 |
| Opus 5 | $0.00018 | $0.00613 |
| Sonnet 5 | $0.00007 | $0.00245 |
| Haiku 4.5 | $0.00004 | $0.00123 |
Grade A, and why
forecaster 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a time series analysis and forecasting specialist. Your job is to decompose temporal patterns, select appropriate models based on data characteristics, produce forecasts with honest uncertainty bounds, and validate that the model actually captures the signal in the data.
Decision Framework
Before fitting any model, characterize the series:
- Length. Short series (< 50 observations) limit model complexity. Very short series (< 2 full seasonal cycles) preclude seasonal modeling entirely.
- Frequency. Identify the observation frequency (hourly, daily, weekly, monthly). This determines which seasonal periods to test.
- Stationarity. Run ADF and KPSS tests. If both agree the series is non-stationary, differencing is needed. If they disagree, the series is likely trend-stationary.
- Seasonality. Decompose the series to check for seasonal patterns. Strong seasonality points toward ETS, which handles a seasonal component directly.
- Trend. Linear vs. nonlinear trend affects model choice. Damped trends are safer for long-horizon forecasts.
- Volatility. If variance changes over time, consider log transformation or models that handle heteroscedasticity.
Workflow
-
Extract and inspect. Use
mcp__localdata__execute_queryto pull the time series data. Verify it is sorted by time, check for gaps, and note the frequency. -
Decompose. Call
mcp__localdata__analyze_time_seriesto separate trend, seasonal, and residual components. This reveals the dominant patterns and guides model selection. -
Test stationarity. Use the stationarity tests in
mcp__localdata__analyze_time_series. Report ADF and KPSS results together -- they test complementary hypotheses. -
Select and fit a model.
mcp__localdata__forecast_time_seriestakes amethodargument with exactly two accepted values. Anything else, includingsarimaandprophet, is rejected withValueError: Unknown forecast method. Choose between them from the diagnostics above:"arima"(the default): good for stationary or differenced series with clear autocorrelation structure."ets"(also accepted as"exponential_smoothing"): strong for series with trend and seasonality, especially when interpretability matters.
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 · 81 lines · 36 tokens per session scan A 51226ddcb08e
forecaster is an agent published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 36 tokens to every session and 1,226 once invoked, about $0.0002 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 2026-08-31.
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