lightrun-slow-execution-diagnosis

Diagnose performance incidents in running services with Lightrun MCP: slow endpoints, latency or SLO regressions, timeouts, deadline-exceeded errors, hangs, and slowness that is intermittent, sporadic, occasional, happens only sometimes, or appears only under load. Use when diagnosing a performance incident; prefer this over lightrun-live-runtime-debugging, including for mixed slow-and-incorrect cases. Use slow-execution snapshots when a duration boundary separates problematic executions; use focused active-path snapshots or call stacks for known hangs that may not reach an end marker. Select the narrowest code section, derive an evidence-based threshold, and correlate captured state with code and telemetry. Use lightrun-live-runtime-debugging for non-performance diagnosis and lightrun-ask-prod for one-off timing, when available; otherwise report the scope gap. Do not use for pull-request review, code changes, setup, deployment, or post-deployment performance validation.

1.95x

Quality

96%

Does it follow best practices?

Impact

96%

1.95x

Average score across 1 eval scenario

Securityby

Low

Low-risk findings worth noting

Quality

Content

92%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.

The body is a well-structured operational workflow with concrete executable code, explicit checkpoints and feedback loops, and clean one-level-deep reference separation. Its only weakness is minor verbosity in guard-clause phrasing that could be tightened.

Suggestions

Tighten the 'Usage telemetry' section and repeated boundary phrasing to recover tokens without losing operational content.

Consider condensing the activation-gate and preflight guard clauses into a single pass/fail block to reduce hedging prose.

Dimension	Reasoning	Score
Conciseness	The body is dense and operational with no padding about concepts Claude already knows, but a few guard-clause sentences (the usage-telemetry section, some hedging phrasing) could be trimmed, so it is efficient rather than maximally lean.	4 / 5
Actionability	Provides a copy-paste-ready quickstart with exact create parameters, concrete poll-command chains for both lifecycle branches, a specific polling cadence (2s then 1/2/5s up to ~15s), and a runtime/version table, covering the common cases fully.	5 / 5
Workflow Clarity	The multi-step process is clearly sequenced with an explicit checkpoint ('Proceed to evidence-path selection only after source discovery passes'), preflight pass/fail criteria, feedback loops (revalidate before repeating, retarget on marker failure), and validation around the destructive/runtime action creation.	5 / 5
Progressive Disclosure	SKILL.md is an overview that offloads bulk detail (threshold derivation, marker placement, dual-lifecycle polling) to two clearly signaled, one-level-deep references that both exist in ./references/, giving easy navigation.	5 / 5
	Total	19 / 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.

The description is comprehensive, concrete, and well-bounded: it states what the skill does, when to use it, how it relates to sibling skills, and what it explicitly excludes. Trigger terms are extensive and natural, and the voice is correctly third person throughout.

Dimension	Reasoning	Score
Specificity	Names the domain and lists multiple concrete actions ('slow endpoints, latency or SLO regressions, timeouts, deadline-exceeded errors, hangs') plus operational steps ('Select the narrowest code section, derive an evidence-based threshold, and correlate captured state with code and telemetry'), giving comprehensive coverage rather than minor gaps.	5 / 5
Completeness	Explicitly answers both what ('Diagnose performance incidents in running services with Lightrun MCP...') and when ('Use when diagnosing a performance incident') with concrete trigger phrases and scope boundaries.	5 / 5
Trigger Term Quality	Covers natural user phrasing comprehensively with synonyms and variations ('slow endpoints, latency or SLO regressions, timeouts, deadline-exceeded errors, hangs, intermittent, sporadic, occasional, happens only sometimes, appears only under load'), beyond just a few missing terms.	5 / 5
Distinctiveness Conflict Risk	Distinguishes itself clearly from sibling skills ('prefer this over lightrun-live-runtime-debugging', 'Use lightrun-ask-prod for one-off timing') and excludes unrelated work ('Do not use for pull-request review, code changes, setup, deployment, or post-deployment performance validation'), giving a clear niche with minimal conflict risk.	5 / 5
	Total	20 / 20 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 — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository: lightrun-platform/lightrun-ai
Path: skills/lightrun-slow-execution-diagnosis/SKILL.md
Commit: d2b954c

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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