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lightrun-ask-prod

Answer questions about live production system behavior — current variable values, execution durations, hit counts, and value distributions — by instrumenting running services with Lightrun MCP tools. Use when the question requires live runtime data rather than static code analysis (e.g. "show recent requests to this endpoint", "show the runtime distribution for this operation", "what values appear for this expression in production?", "which branch runs for customer X?"). Do not use for incident diagnosis, pull-request review, code changes, setup, or deployment. Route latency, timeout, deadline, SLO, or hang diagnosis to lightrun-slow-execution-diagnosis when available; route other diagnosis, or fallback when that skill is unavailable, to lightrun-live-runtime-debugging when available. If neither diagnosis skill is available, report the scope gap.

75

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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 operational skill body with a clear sequenced workflow, explicit validation and feedback loops, and appropriate one-level-deep reference delegation. Minor conciseness and the inherently illustrative (justified) example code keep actionability and conciseness just short of full marks.

DimensionReasoningScore

Conciseness

Mostly lean operational guidance that assumes Claude's competence (no concept explanations), but the six-step flow and selection bullets could be trimmed slightly in places without losing clarity.

4 / 5

Actionability

Provides a capability-selection table, concrete source-selection logic, and a schema-driven example; the example call is illustrative rather than executable, but that flexibility is explicitly justified by the dynamic MCP-schema nature of the tool.

4 / 5

Workflow Clarity

A clearly sequenced six-step flow with explicit validation checkpoints (preflight pass/fail, no-capture-on-failure, status/result-availability semantics) and a feedback loop ("After repeated no-data or low-information results, revalidate or change the source...").

5 / 5

Progressive Disclosure

The body is an overview that delegates shared MCP-discovery rules to references/mcp-tool-discovery.md via a well-signaled one-level-deep link (with anchor), and that bundle file exists and is appropriately scoped.

5 / 5

Total

18

/

20

Passed

Description

100%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.

A strong, third-person description that pairs concrete capabilities with natural trigger phrases and explicit scope boundaries plus inter-skill routing. It clearly answers what, when, and when-not with minimal conflict risk.

DimensionReasoningScore

Specificity

Names the domain and multiple concrete measurement actions — "current variable values, execution durations, hit counts, and value distributions" via "instrumenting running services with Lightrun MCP tools" — giving comprehensive coverage rather than vague language.

5 / 5

Completeness

Explicitly answers both what (answer live runtime questions via Lightrun instrumentation) and when ("Use when the question requires live runtime data rather than static code analysis") with concrete trigger phrases plus a negative-scope list.

5 / 5

Trigger Term Quality

Includes four natural user phrasings ("show recent requests to this endpoint", "what values appear for this expression in production?", "which branch runs for customer X?") that a user would actually say, beyond technical jargon.

5 / 5

Distinctiveness Conflict Risk

Clear niche (live runtime data via Lightrun MCP) with explicit routing away from incident/PR/code/deploy work and named handoff skills, minimizing overlap with sibling diagnosis skills.

5 / 5

Total

20

/

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
lightrun-platform/lightrun-ai
Reviewed

Table of Contents

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