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cx-telemetry-querying

Use this skill for any question involving telemetry data: "investigate an issue", "debug a problem", "find out why something is slow", "check error rates", "analyze user behavior", "understand a production incident", "query telemetry data", "look at logs", "search logs", "find errors", "find stack traces", "filter by severity", "check traces", "examine spans", "investigate request latency", "debug service-to-service calls", "look up a trace ID", "analyze RUM data", "query rum.events", "check frontend performance", "frontend errors", "Core Web Vitals", "JavaScript exceptions", "query metrics", "check CPU usage", "run a PromQL query", "check error rate", "look up a metric", "check memory usage", "how do I write a DataPrime query", "DataPrime syntax", or wants to answer questions using observability data from logs, metrics, traces, RUM, or APM.

68

Quality

86%

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SKILL.md
Quality
Evals
Security

Quality

Content

85%Weight 40%Scale 1-5

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

A well-structured routing skill: excellent progressive disclosure with per-pillar reference loading, fully executable CLI commands, and a clearly sequenced discovery workflow with pivot-on-failure feedback. The main cost is token duplication — Key Principles and parts of the CLI table and Examples restate guidance already given earlier in the file.

Suggestions

Remove or drastically shrink the 'Key Principles' section — every bullet restates guidance already covered by Loading References, Discovery Workflow, and Fallback and Pivoting.

Trim the CLI Commands Reference table to commands not already shown verbatim in the Discovery Workflow (e.g., keep cx schema, cx dashboards rows; drop duplicate search-fields rows), or drop Examples 2-4 in favor of one canonical ambiguous-question example.

Add one concrete search command or pattern to Discovery Workflow Step 3 (e.g., a ripgrep example for metric registration) so that step matches the executability of the rest of the workflow.

DimensionReasoningScore

Conciseness

Mostly efficient — commands are concrete and tables are dense — but there is noticeable duplication: the 'Key Principles' section restates the Loading References table, Discovery Workflow, and Fallback section ('Load references before querying', 'Pivot on failure'); the CLI Commands Reference table re-lists discovery commands already shown verbatim; and the Examples section re-derives routing already covered by the Quick Routing Guide. This fits anchor 3 (mostly efficient but could be tightened) rather than 2 (several unnecessary explanations), since nothing explains concepts Claude already knows.

3 / 5

Actionability

Fully executable, copy-paste-ready commands throughout: 'cx metrics search --name '*transaction*'', 'cx search-fields "payment_failed" -s value --dataset logs', 'cx spans "filter $l.serviceName == \'<service>\'" --limit 10', with exact flags, datasets, and a concrete requirement note about API credentials. Examples are worked end-to-end (Example 1 walks the ambiguous business question through metrics, logs, and spans discovery). Matches the top anchor.

5 / 5

Workflow Clarity

The Discovery Workflow is a clearly sequenced 4-step process with explicit branch checkpoints ("If a matching metric is found, load ... and continue") and an error-recovery loop in Fallback and Pivoting ("Do not stop after one failed attempt. Try at least two pillars"). It falls short of anchor 5 because Step 3 (codebase search) has no concrete commands or checkpoints — only prose about what to look for — and validation of discovery results (e.g., verifying a found field actually contains data) is implicit; still clearly above anchor 3, which lacks checkpoints entirely. The destructive/batch validation cap does not apply since all commands are read-only.

4 / 5

Progressive Disclosure

SKILL.md is a clean overview that routes to seven one-level-deep reference files (all of which exist in the bundle: dataprime-reference.md, logs-querying.md, spans-querying.md, metrics-querying.md, promql-guidelines.md, rum-querying.md, rum-fields.md), each loaded per pillar via the Loading References table with explicit paths. Navigation is easy, content is appropriately split, and no detail that belongs in references is inlined. Matches the top anchor.

5 / 5

Total

17

/

20

Passed

Description

81%Weight 40%Scale 1-5

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

An unusually thorough trigger list that maximizes discoverability — the 'when' is best-in-class with natural phrasing, synonyms, and product-specific terms. The main weakness is that it never explicitly states what the skill does (the querying capability and tooling are only implied), which caps specificity and completeness.

Suggestions

Add an explicit capability statement up front (e.g., 'Query logs, metrics, traces, and RUM data with the cx CLI to answer observability questions') before the trigger list, so the 'what' is stated rather than implied.

Name the concrete actions the skill performs (searching metrics/fields, running DataPrime/PromQL queries, listing dashboards) instead of only the user questions that trigger it.

Optionally trim duplicate trigger entries (e.g., 'check error rate' vs 'check error rates', 'look at logs' vs 'search logs') to reduce description length without losing coverage.

DimensionReasoningScore

Specificity

The description names the domain ("any question involving telemetry data" / "observability data from logs, metrics, traces, RUM, or APM") and includes concrete action phrases ("query telemetry data", "run a PromQL query", "query rum.events", "look up a trace ID"), but these are framed as user utterances rather than a statement of what the skill actually does — the underlying capability (querying via the cx CLI / Coralogix) is never stated. It sits between anchor 3 (domain + concrete actions, not comprehensive) and anchor 4 (several specific actions with minor gaps), closer to 3 because the capability statement itself is absent.

3 / 5

Completeness

The 'when' is exceptionally explicit ("Use this skill for any question involving telemetry data" followed by dozens of trigger phrases). The 'what' is present but only embedded in trigger phrasing — "query telemetry data", "query metrics", "answer questions using observability data from logs, metrics, traces, RUM, or APM" — rather than an explicit capability statement, so it could be more direct. This fits anchor 4 (both present, one could be more explicit) rather than 5 (both clearly and concretely stated).

4 / 5

Trigger Term Quality

Comprehensive natural-language triggers with abundant synonyms and variations: "investigate an issue", "debug a problem", "find out why something is slow", "look at logs", "search logs", "find errors", "find stack traces", "filter by severity", plus product-specific terms ("Core Web Vitals", "PromQL", "DataPrime syntax", "rum.events", "JavaScript exceptions"). This matches the top anchor: coverage of natural terms including synonyms and specific technical identifiers.

5 / 5

Distinctiveness Conflict Risk

Clear niche (observability/telemetry querying) with distinct, unambiguous triggers including product-specific terms like "DataPrime syntax", "query rum.events", and "run a PromQL query" that would rarely match the wrong skill. Minimal conflict risk; matches the top anchor of a clear niche with distinct triggers.

5 / 5

Total

17

/

20

Passed

Validation

100%

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

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
coralogix/cx-cli
Reviewed

Table of Contents

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