performance

Profiling methodology and optimization strategy for performance work. Use when the user asks to "make this faster", "optimize", "profile", "reduce latency", "fix slow", "improve throughput", or when investigating performance regressions.

Quality

73%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/performance/SKILL.md

Quality

Content

57%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-structured conceptual guide for performance optimization methodology, with good organization and useful frameworks (trade-off table, bottleneck patterns, output template). Its main weaknesses are the lack of concrete, executable examples — no actual profiling commands or code snippets — and some verbosity in explaining concepts Claude already understands. It reads more like a teaching document than an actionable skill reference.

Suggestions

Add concrete, executable profiling commands for at least 2-3 languages/tools (e.g., `py-spy record -o profile.svg -- python script.py`, `time.perf_counter()` baseline snippet, `console.time()`/`performance.now()` for JS)

Add explicit validation/feedback loop to the Performance Loop: what to do if step 6 shows regression, how to decide whether to keep or revert a change, and a threshold for 'meaningful improvement'

Trim the anti-patterns section and Knuth quote — Claude knows these principles. Replace with a compact 'common mistakes' bullet list and use the saved tokens for executable examples

Dimension	Reasoning	Score
Conciseness	The content is generally well-structured but includes some unnecessary material Claude already knows — the Knuth quote, explanations of common concepts like what caching is, and the trade-off table examples are somewhat verbose. The anti-patterns section restates well-known principles. However, the tables and structured patterns do add value.	2 / 3
Actionability	The skill provides a clear methodology and output format template, but lacks concrete executable code or specific tool commands. There are no actual profiling commands (e.g., `py-spy`, `perf`, `time.perf_counter()` snippets), no benchmark setup examples, and the guidance remains at the conceptual/framework level rather than copy-paste ready.	2 / 3
Workflow Clarity	The Performance Loop provides a clear 6-step sequence, and the profiling strategy has a narrowing approach. However, there are no explicit validation checkpoints or feedback loops — step 6 says 'measure again' but doesn't specify what to do if results regress or are inconclusive. For a methodology involving potentially destructive optimizations, the lack of rollback/verification guidance is a gap.	2 / 3
Progressive Disclosure	The content is well-organized with clear sections progressing from overview to specifics. The 'See Also' section provides one-level-deep references to related skills. Tables break up information effectively. The output format template is appropriately placed at the end.	3 / 3
	Total	9 / 12 Passed

Description

89%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a solid skill description with excellent trigger term coverage and a clear 'Use when' clause that makes it easy for Claude to select appropriately. Its main weakness is the somewhat vague 'what' portion—it describes a methodology area rather than listing specific concrete actions the skill enables. Adding explicit capabilities would elevate it further.

Suggestions

Replace 'Profiling methodology and optimization strategy for performance work' with specific concrete actions, e.g., 'Profiles CPU and memory hotspots, identifies bottlenecks, recommends algorithmic and caching optimizations, and benchmarks code changes.'

Dimension	Reasoning	Score
Specificity	The description names the domain ('profiling methodology and optimization strategy for performance work') but does not list multiple specific concrete actions. It describes a general area rather than enumerating specific capabilities like 'profile CPU hotspots, analyze memory allocations, benchmark functions, reduce algorithmic complexity.'	2 / 3
Completeness	Clearly answers both 'what' (profiling methodology and optimization strategy for performance work) and 'when' (explicit 'Use when...' clause with multiple trigger scenarios). The when clause is detailed and actionable.	3 / 3
Trigger Term Quality	Excellent coverage of natural trigger terms users would actually say: 'make this faster', 'optimize', 'profile', 'reduce latency', 'fix slow', 'improve throughput', and 'performance regressions'. These are highly natural phrases covering common variations.	3 / 3
Distinctiveness Conflict Risk	Performance profiling and optimization is a clear niche with distinct trigger terms. The specific phrases like 'profile', 'reduce latency', 'improve throughput' are unlikely to conflict with other skills like general code editing or debugging.	3 / 3
	Total	11 / 12 Passed

Validation

90%

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 — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	10 / 11 Passed

Repository: tslateman/duet
Commit: c3b1fc2

Reviewed: 3 months ago

Table of Contents

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