// Use this skill when adding, renaming, removing, or reviewing mesh-llm OTLP metrics, telemetry attributes, metrics exporter settings, or telemetry documentation.
| name | telemetry-privacy-review |
| description | Use this skill when adding, renaming, removing, or reviewing mesh-llm OTLP metrics, telemetry attributes, metrics exporter settings, or telemetry documentation. |
| metadata | {"short-description":"Review mesh-llm telemetry privacy"} |
Use this skill before changing mesh-llm OTLP metrics, exporter activation, or telemetry attribute names.
telemetry.enabled = true; mesh config
endpoints are explicit operator configuration.TELEMETRY_ATTRIBUTE_ALLOWLIST in
crates/mesh-llm/src/runtime/survey.rs for every new exported attribute.docs/plugins/telemetry.md with the metric or attribute inventory and
privacy handling.Run the narrowest relevant checks for the touched area. For telemetry runtime changes, start with:
cargo test -p mesh-llm runtime::survey::tests --lib
cargo test -p mesh-llm telemetry_config --lib
If routing telemetry changed, also run the focused mesh routing telemetry test:
cargo test -p mesh-llm routing_telemetry_sink_receives_request_pressure_and_attempt_events --lib
Use this skill when running benchmark orchestration, local single-stage or split benchmarks, benchmark report flow, or performance-oriented skippy runtime checks.
Use this skill when validating skippy staged execution against full-model execution, adding model families, changing split boundaries, testing activation wire dtypes, or diagnosing mismatch behavior.
Use this skill when inspecting GGUF models, planning layer ranges, generating or validating skippy package artifacts, fake packages for direct GGUFs, materialized stage cache behavior, or GGUF writer integration.
Use this skill when running, configuring, debugging, or embedding skippy-server, binary stage transport, OpenAI frontend integration, activation wire dtype settings, stage configs, lifecycle status, or nonblocking telemetry.
Use this skill when certifying mesh-llm KV/cache stability under repeated OpenAI tool-call loops, same-prefix cache reuse, suffix-prefill limits, or native Skippy slot/decode/eviction failures.
Use when changing mesh-llm automation or CLI flows that discover Hugging Face GGUF models, plan CPU Hugging Face Jobs for layer-package splitting, estimate max cost, or publish skippy layer packages/catalog entries.