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genpark-audio-packet-jitter-resilience-buffer-skill

by Alpha-Park
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Python
Remote

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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    C2.4/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool targets a distinct action on the pipeline: ingest (input), extract (output), telemetry (observability), and benchmark (testing). There is no meaningful overlap; an agent can unambiguously pick the right tool for each stage.

    Naming Consistency5/5

    All four tools follow a strict verb_noun snake_case pattern (ingest_packet, extract_playout_frame, get_jitter_telemetry, run_benchmark_jitter_simulation). Conventions are predictable and readable throughout.

    Tool Count4/5

    Four tools is reasonable for a narrow audio jitter buffer skill, covering the core ingest/playout/telemetry/benchmark flow. It is slightly thin, but every tool earns its place with no filler.

    Completeness3/5

    The ingest-to-playout pipeline, telemetry, and benchmarking are covered, but there is no way to configure buffer depth, reset/flush state, or query buffer status. These are notable gaps for a jitter resilience buffer that agents may need to work around.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources