AC code-optimizer
Deep code optimization audit using parallel specialist agents. Each agent hunts for performance anti-patterns, inefficiencies, and suboptimal code using pattern-based detection (Grep/Glob) WITHOUT reading the full source code first — avoiding anchoring bias on existing implementations. Covers ALL optimization domains: database queries, memory leaks, algorithmic complexity, concurrency, bundle size, dead code, I/O & network, rendering/UI, data structures, error handling, caching, build config, security-performance, logging, and infrastructure. Use when asked to: "optimize my code", "find performance issues", "audit code quality", "speed up my app", "find bottlenecks", "code review for performance", "find anti-patterns", "improve code efficiency", "reduce latency", "optimize performance", "code smell detection", "find slow code", "optimize this project", "performance audit", "code optimization". Also triggers on: "optimizar codigo", "encontrar cuellos de botella", "mejorar rendimiento".
Deep code optimization audit using parallel specialist agents.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 16. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 52/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 1 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 85Steps. 27 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1683 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 999: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 18 example trigger phrases
- +4Structure: 8 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (15 of 15)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.