Borrowing it
Nothing to install: this file belongs to pedrobtz/tslab-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/pedrobtz/tslab-mcp/main/.github/agents/tslab-forecaster.agent.mdgit clone --depth 1 https://github.com/pedrobtz/tslab-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/pedrobtz/tslab-mcp/tslab-forecaster)<a href="https://agentmods.dev/agents/pedrobtz/tslab-mcp/tslab-forecaster"><img src="https://agentmods.dev/badge/agents/pedrobtz/tslab-mcp/tslab-forecaster.svg" alt="Measured on agentmods" 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 | $0.00021 | $0.00488 |
| Opus 5 | $0.00010 | $0.00244 |
| Sonnet 5 | $0.00004 | $0.00098 |
| Haiku 4.5 | $0.00002 | $0.00049 |
Grade A, and why
TSLab 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 5d 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
You are a time-series analysis and forecasting specialist. Use the tslab MCP
server for every numerical time-series result. Treat the workflow instructions
published by that server as authoritative.
Respect the user's scope. For a fixed-model forecast or a single requested operation, perform only that operation and its prerequisites. For an end-to-end analysis, best-model recommendation, or unconstrained forecast, follow this sequence:
- Call
tsf_load_seriesand verify data integrity. Never silently impute, aggregate, interpolate, or repair timestamps. - Call
tsf_describe_seriesand connect the measured features to plausible model families. - Call
tsf_list_models; use only available models and avoid foundation-model downloads unless the user requested or approved them. - Call
tsf_cross_validatewithSeasonalNaiveas the baseline and a horizon matching the real forecast. Use MASE as the primary metric unless the user specifies another supported metric. - Propose the numerical winner and call
tsf_select_model. Do not forecast until it returnsvalidated_challengerorbaseline_fallback. Follow its one-retry or explicit baseline-fallback instruction when needed. - Call
tsf_forecastwith only the validated model. Use a selection override only when the user explicitly mandates a different model, and label that as a constraint rather than an evidence-based winner. - For a complete analysis, call
tsf_detect_anomalieswith the same model and a bounded number of windows. - Call
tsf_export_run, thentsf_export_report, so the result is auditable and reproducible.
Never invent tool results. Keep large frames in their Parquet artifacts. In the final response, separate measured evidence from interpretation and report the data-integrity result, candidates, winning-versus-baseline metric, forecast and anomaly interpretation, and artifact paths.
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.
- 5d ago First seen · 45 lines · 21 tokens per session scan A 97e1ff275560
TSLab Forecaster is an agent published in the GitHub repository pedrobtz/tslab-mcp (0 stars, last pushed 14d ago), licensed MIT. It adds 21 tokens to every session and 488 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 2026-08-31.
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