chore(agents): Expand perf-tuning scope and parallel agent count#1274
chore(agents): Expand perf-tuning scope and parallel agent count#1274
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- Add `website/server` to investigation target alongside `src` - Increase parallel agent count from team-based to 10 explicit agents Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
📝 WalkthroughWalkthroughUpdated the performance-tuning command documentation to expand scope from Changes
Estimated code review effort🎯 1 (Trivial) | ⏱️ ~2 minutes Possibly related PRs
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⚡ Performance Benchmark
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request enhances the performance tuning process by broadening the scope of investigation and increasing the number of agents working in parallel. These changes aim to improve the efficiency and thoroughness of performance investigations. Highlights
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Review SummaryOverall: Looks good ✅ This is a minimal, low-risk PR that updates the
Code QualityClean and clear changes. The diff is easy to understand and well-scoped. Potential IssuesDetailsHardcoded agent count (minor): The change from "agent team" to "10 agents in parallel" is more explicit, which is good for reproducibility. However, the number 10 is somewhat arbitrary — depending on the model/runtime, spawning 10 agents may hit rate limits or context constraints. This is a prompt file though, so it's easy to adjust later. Scope expansion: Adding Premortem AnalysisDetails
VerdictShip it. No blocking issues.
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Code Review
This pull request updates the instructions for a performance tuning agent. The scope is expanded to include the website/server directory, and the number of parallel agents for investigation is explicitly set to 10. While being specific about the agent count is an improvement, hardcoding the number could be inflexible. I've suggested a minor change to frame it as a maximum, which would allow for better adaptability to different environments.
| ## Investigation & Planning | ||
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| Spawn an agent team to investigate efficiently and thoroughly, then form an improvement plan. | ||
| Spawn 10 agents in parallel to investigate efficiently and thoroughly, then form an improvement plan. |
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Hardcoding the number of parallel agents to 10 might not be optimal for all environments, as it doesn't account for varying system resources. It would be more flexible to specify this as a maximum number of agents rather than a fixed count.
| Spawn 10 agents in parallel to investigate efficiently and thoroughly, then form an improvement plan. | |
| Spawn up to 10 agents in parallel to investigate efficiently and thoroughly, then form an improvement plan. |
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🧹 Nitpick comments (1)
.agents/commands/code/perf-tuning.md (1)
10-10: Document the rationale for 10 parallel agents.The change from "agent team" to an explicit count of 10 agents improves clarity, but the specific number appears arbitrary without justification. Consider whether this count was determined through testing or benchmarking.
Please confirm:
- Was the count of 10 agents based on performance testing or resource constraints?
- Does this number align with the typical size/complexity of the codebase areas being analyzed?
If this was determined empirically, consider adding a brief comment explaining the rationale (e.g., "10 agents balances thoroughness with resource usage for typical investigation scope").
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In @.agents/commands/code/perf-tuning.md at line 10, Update the line "Spawn 10 agents in parallel to investigate efficiently and thoroughly, then form an improvement plan." to include a brief rationale for the choice of 10 (e.g., based on performance testing, resource constraints, or typical codebase size/complexity); state whether the number was empirically determined and, if so, summarize the benchmarking or trade-offs (throughput vs. resource usage) that led to 10, or note that it is an example and suggest how to adjust it for different environments.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Nitpick comments:
In @.agents/commands/code/perf-tuning.md:
- Line 10: Update the line "Spawn 10 agents in parallel to investigate
efficiently and thoroughly, then form an improvement plan." to include a brief
rationale for the choice of 10 (e.g., based on performance testing, resource
constraints, or typical codebase size/complexity); state whether the number was
empirically determined and, if so, summarize the benchmarking or trade-offs
(throughput vs. resource usage) that led to 10, or note that it is an example
and suggest how to adjust it for different environments.
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.agents/commands/code/perf-tuning.md
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #1274 +/- ##
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Coverage 87.18% 87.18%
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Files 115 115
Lines 4324 4324
Branches 1002 1002
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Hits 3770 3770
Misses 554 554 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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website/serverto investigation target alongsidesrcChecklist
npm run testnpm run lint