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databricks-spark-structured-streaming

Comprehensive guide to Spark Structured Streaming for production workloads. Use when building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, or tuning streaming cost and performance.

72

Quality

Does it follow best practices?

Impact

No eval scenarios have been run

SecuritybySnyk

Passed

No known issues

SKILL.md
Quality
Evals
Security

Quality

Content

77%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is concise and well-organized with a useful production checklist, but its actionability and progressive disclosure are undercut by an undefined `schema` variable in the Quick Start and by eight navigation links pointing to bundle files that do not exist. Adding the missing referenced files (or inlining their essential content) and defining `schema` would resolve the two lowest dimensions.

Suggestions

Create the eight referenced bundle files (kafka-streaming.md, stream-stream-joins.md, stream-static-joins.md, multi-sink-writes.md, merge-operations.md, checkpoint-best-practices.md, stateful-operations.md, trigger-and-cost-optimization.md, streaming-best-practices.md) under references/ so the navigation links resolve, or inline the most critical guidance directly in SKILL.md.

Define the `schema` variable in the Quick Start code (e.g., show a StructType/from_json schema) so the example is fully copy-paste executable.

If keeping the file-split structure, ensure the links point to real paths (e.g., references/kafka-streaming.md) and verify each linked file contains substantive, one-level-deep detail rather than further indirection.

DimensionReasoningScore

Conciseness

The body is lean — a one-line intro, a single Quick Start snippet, and compact navigation tables — with no padding explaining what Spark or Kafka is, matching the score-3 'lean and efficient; every token earns its place' anchor rather than the score-2 anchor that retains unnecessary explanation.

3 / 3

Actionability

The Quick Start offers executable readStream/writeStream code but references an undefined `schema` variable, and the bulk of the body is navigation pointers to files that do not exist, matching the score-2 'some concrete guidance but incomplete; missing key details' anchor; it is not 3 because the code is not fully copy-paste ready and the detailed guidance is absent.

2 / 3

Workflow Clarity

As a navigation/index skill it has a clear Quick Start sequence (read -> parse -> write) plus an explicit Production Checklist of verification items ('Checkpoint location is persistent', 'Exactly-once verified (txnVersion/txnId)', 'Watermark configured'), matching the score-3 anchor's checklists for complex processes; per scoring_notes simple well-organized skills can score 3.

3 / 3

Progressive Disclosure

The overview is cleanly organized with clearly signaled one-level-deep references (score-3 form), but scoring against the actual bundle structure per the guideline, no references/, scripts/, or assets/ directories exist and all eight linked .md files (kafka-streaming.md, stream-stream-joins.md, etc.) are missing, so the disclosure is non-functional — matching the score-2 anchor where referenced detail is not effectively delivered rather than the working score-3 anchor.

2 / 3

Total

10

/

12

Passed

Description

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is strong across all dimensions: it is specific, includes explicit 'Use when...' trigger guidance with natural terms, and carves out a clearly distinct niche. It slightly over-indexes on technical jargon (processingTime, availableNow, RTM) but not enough to drop any dimension.

DimensionReasoningScore

Specificity

Lists many concrete actions — 'building streaming pipelines, working with Kafka ingestion, implementing Real-Time Mode (RTM), configuring triggers (processingTime, availableNow), handling stateful operations with watermarks, optimizing checkpoints, performing stream-stream or stream-static joins, writing to multiple sinks, tuning streaming cost and performance' — matching the score-3 anchor of multiple specific concrete actions rather than the partial domain-only anchor at 2.

3 / 3

Completeness

Explicitly states both what it does ('Comprehensive guide to Spark Structured Streaming for production workloads') and when to use it ('Use when building streaming pipelines...'), matching the score-3 anchor that answers both what AND when with explicit triggers.

3 / 3

Trigger Term Quality

Covers natural user terms ('streaming pipelines, Kafka ingestion, watermarks, checkpoints, joins, multiple sinks, cost and performance') broadly enough to match the good-coverage anchor at 3, though it also leans technical (processingTime, availableNow, RTM); it is not the 2-anchor because common variations are well represented.

3 / 3

Distinctiveness Conflict Risk

The Spark Structured Streaming niche with distinctive triggers (RTM, watermarks, checkpoints, stream-stream/stream-static joins) is clearly bounded and unlikely to fire for unrelated skills, matching the clear-niche score-3 anchor.

3 / 3

Total

12

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

relative_links

Relative link issues: 9 missing

Warning

Total

15

/

16

Passed

Repository
databricks-solutions/ai-dev-kit
Reviewed

Table of Contents

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