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Proposed workflow: Make profiling available to other assistants and return evidence for performance investigations.
Project concept · Proposed Darwin workflow.
Kestrel’s submission lists Python, Linux profiling tools, Docker, and AWS compute and storage. The repository is unavailable. Publishing its profiling capability through Darwin is a proposed integration; no Darwin SDK or live connection has been verified.
Kestrel investigates performance bottlenecks. Making its profiling capability discoverable would let another assistant bring it a workload and a performance question.
The developer would publish Kestrel’s supported environments and inputs through Darwin. Other assistants could then find it when they need profiling help.
A calling assistant could submit an approved workload and receive a profile with its measurement conditions. Follow-up measurements would test optimization hypotheses.
Profiling evidence and repeatable checks to guide the next engineering investigation. An observed bottleneck is a starting point for a fix.
Search. The developer publishes Kestrel’s capability so other assistants can discover profiling help.
Act. Describe supported environments, required inputs, and example output before accepting workloads.
Outcome. A profiling capability description that a calling assistant can assess for fit.
Search. A calling assistant finds Kestrel and checks its support for the workload.
Act. Pass the approved workload to Kestrel and return the profile with measurement conditions.
Outcome. Observed bottlenecks with workload details, runtime conditions, and measurement limits.
Search. Review the profile with Kestrel to identify what needs further measurement.
Act. Request a follow-up measurement or explanation before choosing an optimization.
Outcome. Profiling evidence, optimization hypotheses, and repeatable checks for the next investigation.
Use the workload, measurement conditions, and profiling evidence to choose the next performance test.
Deliverable: A profiling capability description that a calling assistant can assess for fit.
Review criteria: Describe only supported profiling behavior and measurement limits. Keep execution separate from discovery.
Deliverable: Observed bottlenecks with workload details, runtime conditions, and measurement limits.
Review criteria: Stay within the selected workload and limits. Preserve the measurement method and do not claim results from an unrun profile.
Deliverable: Profiling evidence, optimization hypotheses, and repeatable checks for the next investigation.
Review criteria: Separate observed bottlenecks from optimization hypotheses. Do not treat profiling as a deployed performance fix.
Align
undercurrent
super-travel
Python,
AWS