Why this fit
Teams evaluating AI models, assistants and generative workflows, particularly latency-sensitive workloads — source available on GitHub.
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AI / Catalogue record

Catalogue record
github.com
🐍 ☁️ - Bitcoin regime detection intelligence — know which market regime you are in and the probabilities of what happens next. 17 tools with...
Field notes
🐍 ☁️ - Bitcoin regime detection intelligence — know which market regime you are in and the probabilities of what happens next. 17 tools with historical pattern matching, Odin AI analysis, composite gauges, and real-time macro/institutional/on-chain data.
Best for
Teams evaluating AI models, assistants and generative workflows, particularly latency-sensitive workloads — source available on GitHub.
Known limitations
Workflows that require fully deterministic, auditable outputs with no human review step.
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Evidence summary
Why this fit
Teams evaluating AI models, assistants and generative workflows, particularly latency-sensitive workloads — source available on GitHub.
Known limitations
Workflows that require fully deterministic, auditable outputs with no human review step.
Catalogue confidence
Catalogue data checked Oct 27, 2026.
GitHub-backed record with source-attributed context.
Repository context
GitHub / KK6BZB/signallord-mcp-server
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Record provenance
Oryxen distinguishes catalogue metadata from repository context. These signals describe the directory record and its sources; they do not guarantee the current availability, security, or suitability of an external product.
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