Coordinate application performance optimization through profiling, planning, implementation, and benchmarking.
npx skills add https://github.com/tcytan/tecton --skill team-perf-opt
Please help me install this Agent Skill. Skill: team-perf-opt Source: https://github.com/tcytan/tecton/tree/main/.claude/skills/team-perf-opt Command: npx skills add https://github.com/tcytan/tecton --skill team-perf-opt
It helps teams improve application performance by orchestrating profiling, optimization planning, code changes, benchmarking, and review with structured feedback loops.
Ask your AI to run the team-perf-opt pipeline with a description of the performance problem you want to improve and the scope you care about.
High-intent search queries and answers about installing and using this skill.
Application performance optimization is coordinated by profiling bottlenecks, planning targeted fixes, implementing changes, and running review-fix cycles. This pipeline orchestrates single, fan-out, and independent optimization scenarios with benchmark validation and progress reporting.
Performance profiling quantifies baseline metrics and produces a prioritized bottleneck report for your selected scope. This evidence is then converted into measurable optimization strategies before applying minimal, plan-aligned code edits.
Benchmark validation runs before-and-after comparisons, performs correctness reviews, and triggers regression fixes up to a capped iteration count per branch. Structured verdicts based on benchmark outcomes determine whether further revision cycles are needed.
Independent per-target pipelines run optimization scenarios separately with checkpoints and progress reporting. Fan-out scenarios also support per-bottleneck branches, enabling deterministic task dependency management across multiple simultaneous optimization targets.
YAML frontmatter metadata is required for pipeline discovery, role-based execution routing, artifact session persistence, and deterministic task dependency management. This structured metadata enables the orchestrated benchmark and review-fix cycles to execute reliably.
Structured feedback from code review and benchmarking drives the revision cycles needed to reduce performance bottlenecks safely. Review-fix cycles validate correctness and regression risk, ensuring optimization changes trigger capped iterative fixes only when necessary.
ricableProfile, benchmark, and detect bottlenecks in agent workflows.
sanskarbasnetMonitor live applications for console errors, performance degradations, and page failures.
murphytruemanGenerate CI/CD pipeline configurations for design system quality checks.
catlog22Orchestrate a staged performance optimization pipeline with profiling, benchmarking, and review.
