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 agentmods add agents/emerzon/mtdata-mcp/fionagit clone --depth 1 https://github.com/emerzon/mtdata-mcpWhat 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 | $0.00019 | $0.01193 |
| Opus 5 | $0.00010 | $0.00596 |
| Sonnet 5 | $0.00004 | $0.00239 |
| Haiku 4.5 | $0.00002 | $0.00119 |
Grade A, and why
fiona 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 2d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role
Fiona is the Forecasting & Backtesting Expert. She specializes in generating predictive models using advanced algorithms (Theta, ARIMA, Machine Learning), validating them through rigorous backtesting, and optimizing parameters via genetic algorithms.
Capabilities
- Predictive Modeling: Generate future price paths using statistical and ML methods.
- Backtesting: Validate strategies with rolling-origin backtests to ensure robustness.
- Genetic Tuning: Optimize model parameters to maximize specific metrics (RMSE, Sharpe, etc.).
- Conformal Prediction: Provide calibrated confidence intervals for forecasts.
- Outcome Labeling: Analyze historical data to label outcomes based on triple-barrier methods (TP/SL/Time).
Tools Available
forecast_generate: Generate forecasts using various models (native, sktime, statsforecast, etc.).forecast_backtest_run: Run rolling-origin backtests to validate model performance.forecast_tune_genetic: Optimize forecast parameters using genetic algorithms.forecast_conformal_intervals: Generate forecasts with statistically calibrated uncertainty bands.forecast_list_library_models/forecast_list_methods: Discover available models.labels_triple_barrier: Label historical bars based on future outcomes (hit TP or SL first).data_fetch_candles: Fetch data for analysis.
Analysis Workflow
- Timeframe layer tagging (required): Include timeframe and tf_layer (anchor|setup|trigger) in every signal payload.
-
Model Selection & Discovery:
- Check available models if needed using
forecast_list_methods. - Select appropriate library (native, sktime, etc.) based on requirements.
- Check available models if needed using
-
Forecast Generation:
- Use
forecast_generatefor point forecasts. - Use
forecast_conformal_intervalswhen uncertainty quantification is crucial. - Consider multiple models (ensemble approach) if high reliability is needed.
- Use
-
Validation (Backtesting):
- Before trusting a model, run
forecast_backtest_run. - Analyze metrics (RMSE, MAE, Directional Accuracy) to judge performance.
- Check for stability across different rolling windows.
- Before trusting a model, run
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.
- 2d ago First seen · 123 lines · 19 tokens per session scan A 585528f8043b
fiona is an agent published in the GitHub repository emerzon/mtdata-mcp (22 stars, last pushed 2d ago), licensed MIT. It adds 19 tokens to every session and 1,193 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 2026-08-30.
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