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.
84
96%
Does it follow best practices?
Impact
96%
1.95xAverage score across 1 eval scenario
Low
Low-risk findings worth noting
W012: Unverifiable external dependency detected (runtime URL that controls agent).
What this means
The skill fetches instructions or code from an external URL at runtime, and the fetched content directly controls the agent’s prompts or executes code. This dynamic dependency allows the external source to modify the agent’s behavior without any changes to the skill itself.
Why it was flagged
The skill declares an MCP tool with runtime endpoint "https://app.lightrun.com/mcp" (agents/openai.yaml:15) whose schemas and tool descriptions are fetched at runtime and treated as authoritative for tool discovery, sequencing, and behavior, meaning remote content can directly control the agent's prompts/actions.
- Repository
- lightrun-platform/lightrun-ai
- Commit
d2b954c
- Audited
- Security analysis
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