Docs fix — page openings: path /solutions/observability/streams — 8 pages

[github-actions]

Generated by gh-aw-docs-openings-sweep for elastic/docs-content on 2026-W24.

Path /solutions/observability/streams · 31 total in scope · subtree corpus 31 pages.

Findings (8)

- file: solutions/observability/streams/management/data-quality.md
  line: 17
  category: vague-h1
  severity: high
  evidence: "H1 is 'Manage data quality' — generic task verb without Streams context"
  suggested_fix: |
    # Manage data quality in Streams [streams-data-quality]

- file: solutions/observability/streams/management/extract.md
  line: 16
  category: vague-h1
  severity: high
  evidence: "H1 is 'Process documents' — generic without Streams or field extraction context"
  suggested_fix: |
    # Process documents in Streams [streams-extract-fields]

- file: solutions/observability/streams/management/significant-events.md
  line: 17
  category: vague-h1
  severity: high
  evidence: "H1 is 'Add significant events' — generic without Streams context"
  suggested_fix: |
    # Add significant events to Streams [streams-significant-events]

- file: solutions/observability/streams/management/streamlang.md
  line: 18
  category: weak-opening
  severity: medium
  evidence: "Opening paragraph is one sentence describing Streamlang but doesn't explain what the page covers or its purpose within 2 sentences"
  suggested_fix: |
    Streamlang is a YAML domain-specific language (DSL) for defining stream processing and routing logic in Streams. It provides a consistent processing interface that can be converted to multiple execution targets, including {{es}} ingest pipelines and ES|QL. This allows processing to run at ingest time or query time without rewriting rules. Use this page to understand Streamlang structure, syntax, processors, and conditions.

- file: solutions/observability/streams/streams.md
  line: 17
  category: vague-h1
  severity: high
  evidence: "H1 is 'Streams' — feature name without context about what it does"
  suggested_fix: |
    # Manage data streams in Kibana [streams]

- file: solutions/observability/streams/wired-streams.md
  line: 11
  category: weak-opening
  severity: medium
  evidence: "Opening paragraph is one sentence that repeats the description and doesn't explain page purpose or value"
  suggested_fix: |
    Wired streams receive log data through a dedicated endpoint and route it into child streams based on partitioning rules. Unlike classic streams that work with existing data streams, wired streams let you organize streams hierarchically with automatic inheritance of mappings, lifecycle settings, and processors. This page explains wired stream field naming conventions, how to enable and send data to wired streams, and how to view them in Discover.

- file: solutions/observability/streams/management/partitioning.md
  line: 17
  category: weak-opening
  severity: medium
  evidence: "Opening note takes precedence before substantive content. No clear opening paragraph immediately follows H1 that explains what the page covers."
  suggested_fix: |
    # Partition data into child streams [streams-partitioning]

    For wired streams, use the **Partitioning** tab to organize and route log data into meaningful child streams based on manual field-based rules or AI-generated suggestions. Partitioning helps you manage data from multiple systems by creating logical groupings (such as by team or technology) and applying different lifecycles to each partition. This page explains when to partition your data, how to create partitions manually or with AI, and best practices for partition granularity.

    :::{note}
    The **Partitioning** tab and the ability to route data into child streams is only available on [wired streams](../wired-streams.md).
    :::

- file: solutions/observability/streams/management/knowledge-indicators.md
  line: 20
  category: weak-opening
  severity: medium
  evidence: "Opening paragraph exceeds 4 sentences (5 sentences) and buries key information about accessing KIs in sentence 4"
  suggested_fix: |
    Knowledge Indicators (KIs) are structured facts that Elastic automatically extracts from raw log data without requiring schemas, service catalogs, or manual configuration. When you run extraction on a stream, Elastic returns facts about which services are running, the infrastructure they rely on, how they depend on each other, and the log schemas they use. This knowledge accumulates over time, expires when services disappear, and feeds into Rules, topology maps, AI investigations, and dashboards. Access Knowledge Indicators from **Significant Events** → **Knowledge Indicators** on the Streams main page.

Done when

• All listed pages have specific, contextual H1s and opening paragraphs that clearly convey page purpose within 2-4 sentences.
• A PR addressing this issue is merged.

Notes

• Processor reference pages (append, concat, convert, date, dissect, drop, enrich, grok, join, lowercase, math, network-direction, redact, remove, rename, replace, set, trim, uppercase) follow a consistent pattern with clear H1s and concise openings. No changes needed.
• Advanced settings, retention, and schema pages have appropriate H1s and openings.

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