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genpark-agent-session-compaction-tokenizer-skill

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    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables autonomous agents to tokenize session context windows and compact dialogue history deterministically, exposing this capability to MCP-compatible clients like Claude Desktop and Cursor. Helps manage context length and reduce token usage in agent sessions through a zero-dependency Python MCP server.
      7
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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables agents to deterministically shrink multi-turn conversation history and prompts by stripping verbose boilerplate and filler tokens while preserving critical tool parameters and named entities. This lets users cut inference costs by roughly 40-60% without losing essential context, running either as a native MCP server over JSON-RPC stdio or as an importable zero-dependency Python module.
      7
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    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP-compatible clients and autonomous agents to dynamically compress long-horizon context by pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by 40-70%. It reduces token burn and latency through deterministic, zero-dependency processing.
      7
      -
    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents and MCP-compliant clients to dynamically shrink long-horizon context windows, pruning verbose tool outputs, HTML boilerplate, and semantic redundancy by roughly 40-70% without frontier LLM round-trips. Runs deterministically as a zero-dependency Python stdio server, accepting a payload plus optional compression and risk-bound options.
      7
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    • F
      license
      Not graded
      quality
      B
      maintenance
      Enables deterministic, zero-dependency long-horizon conversational memory compaction and episodic anchor extraction for AI agents, with native MCP protocol support and structured JSON telemetry output.
      7
      -
    • A
      license
      Not graded
      quality
      B
      maintenance
      Compresses multi-turn conversation history by stripping verbose boilerplate and filler tokens while preserving critical tool parameters and named entities, deterministically cutting inference costs by 40-60%. Runs as a zero-dependency Python MCP server or importable module, compatible with Claude Desktop, Cursor, and other MCP runtimes.
      7
      MIT

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