dt-obs-logs

Log querying, filtering, pattern analysis, and error rate calculation. Use when searching application or infrastructure logs, analyzing error patterns, or correlating log data. Trigger: "show error logs", "search logs for keyword", "log error rate", "recent errors", "logs from last hour", "find log entries", "top error messages", "log patterns", "parse JSON logs", "logs by process group", "log trends over time", "log entry counts per minute". Do NOT use for explaining existing queries, product documentation questions, distributed tracing or span analysis (use dt-obs-tracing).

Quality

82%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Quality

Content

64%

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

This is a solid, actionable skill with excellent executable DQL examples covering a comprehensive range of log analysis scenarios. Its main weaknesses are verbosity (redundant sections like Use Cases, Key Concepts, and Integration Points that restate information already demonstrated in examples) and a monolithic structure that could benefit from splitting reference material into separate files. The troubleshooting table is a strong addition but workflow validation steps are missing.

Suggestions

Remove or consolidate redundant sections: 'What This Skill Covers', 'Use Cases', 'Key Concepts', and 'Integration Points' largely duplicate information already present in the executable examples and function reference.

Add validation checkpoints to workflows, e.g., 'Run query without aggregation first to verify data exists, then add summarize steps' to help catch empty result sets early.

Extract the 'Key Functions' reference and 'Common Patterns' sections into separate bundle files (e.g., references/dql-log-functions.md, references/common-patterns.md) and link from the main skill to improve progressive disclosure.

Dimension	Reasoning	Score
Conciseness	The skill contains some unnecessary sections like 'What This Skill Covers' (duplicated by 'Use Cases'), 'Integration Points' (restates what's already shown in examples), and 'Key Concepts' which largely repeats information covered in the executable examples. The 'Use Cases' section listing trigger phrases is redundant with the frontmatter description. However, the code examples themselves are lean and the function reference is useful.	2 / 3
Actionability	Every workflow and pattern includes fully executable DQL queries that are copy-paste ready. The examples cover the full range of use cases (searching, filtering, aggregation, JSON parsing, error rates) with concrete field names, functions, and syntax. The troubleshooting table provides specific problem-solution pairs.	3 / 3
Workflow Clarity	The workflows list clear steps and provide executable examples, but there are no validation checkpoints or feedback loops. For log queries that could return no results or unexpected data, there's no guidance on verifying query correctness or iterating. The troubleshooting table partially compensates but isn't integrated into the workflows themselves.	2 / 3
Progressive Disclosure	The content is a monolithic file with no bundle files to offload detailed reference material. The Key Functions reference section, Common Patterns section, and Best Practices could be split into separate reference files. The cross-reference to smartscape-topology-navigation.md and related skills are well-signaled, but the main file is quite long (~200+ lines of content) with everything inline.	2 / 3
	Total	9 / 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.

This is an excellent skill description that covers all key dimensions thoroughly. It provides specific capabilities, extensive natural trigger terms, clear 'what' and 'when' guidance, and explicit disambiguation from related skills. The 'Do NOT use' clause with a redirect to the tracing skill is a particularly strong practice for reducing conflict risk.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: 'log querying, filtering, pattern analysis, and error rate calculation.' These are distinct, actionable capabilities rather than vague language.	3 / 3
Completeness	Clearly answers both 'what' (log querying, filtering, pattern analysis, error rate calculation) and 'when' (searching application/infrastructure logs, analyzing error patterns, correlating log data) with explicit trigger terms and even a 'Do NOT use' clause for disambiguation.	3 / 3
Trigger Term Quality	Excellent coverage of natural trigger terms users would actually say: 'show error logs', 'search logs for keyword', 'log error rate', 'recent errors', 'logs from last hour', 'find log entries', 'top error messages', 'log patterns', 'parse JSON logs', etc. These are highly natural phrases.	3 / 3
Distinctiveness Conflict Risk	Clearly scoped to log analysis with explicit boundary-setting via the 'Do NOT use' clause that distinguishes it from tracing/span analysis and documentation skills. The specific mention of 'dt-obs-tracing' as an alternative further reduces conflict risk.	3 / 3
	Total	12 / 12 Passed

Validation

100%

Warnings & errors only

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

Validation — 11 / 11 Passed

Validation for skill structure

No warnings or errors.

Repository: Dynatrace/dynatrace-for-ai
Commit: 9b399fa

Reviewed: 8 days ago

Table of Contents

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