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signals: restore the pre-port flag marker emoji (🚩) (#913)
* signals: restore the pre-port flag marker emoji #903 inadvertently replaced the legacy FLAG_MARKER (U+1F6A9, '🚩') with '[!]', which broke any downstream dashboard / alert that searches span names for the flag emoji. Restores the original marker and updates the #910 docs pass to match. - crates/brightstaff/src/signals/analyzer.rs: FLAG_MARKER back to "\\u{1F6A9}" with a comment noting the backwards-compatibility reason so it doesn't drift again. - docs/source/concepts/signals.rst and docs/source/guides/observability/ tracing.rst: swap every '[!]' reference (subheading text, example span name, tip box, dashboard query hint) back to 🚩. Verified: cargo test -p brightstaff --lib (162 passed, 1 ignored); sphinx-build clean on both files; rendered HTML shows 🚩 in all flag-marker references. Made-with: Cursor * fix: silence manual_checked_ops clippy lint (rustc 1.95) Pre-existing warning in router/stress_tests.rs that becomes an error under CI's -D warnings with rustc 1.95. Replace the manual if/else with growth.checked_div(num_iterations).unwrap_or(0) as clippy suggests. Made-with: Cursor
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@ -114,11 +114,11 @@ Signals act as early warning indicators embedded in your traces:
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**Visual Flag Markers**
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When concerning signals are detected (disengagement, execution failures / loops, stagnation > 2, or ``poor`` / ``severe`` quality), Plano automatically appends a ``[!]`` marker to the span's operation name. This makes problematic traces immediately visible in your tracing UI without requiring additional queries.
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When concerning signals are detected (disengagement, execution failures / loops, stagnation > 2, or ``poor`` / ``severe`` quality), Plano automatically appends a 🚩 marker to the span's operation name. This makes problematic traces immediately visible in your tracing UI without requiring additional queries.
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**Example Span with Signals**::
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# Span name: "POST /v1/chat/completions gpt-4 [!]"
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# Span name: "POST /v1/chat/completions gpt-4 🚩"
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# Standard LLM attributes:
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llm.model = "gpt-4"
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llm.usage.total_tokens = 225
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