Neo Optimization Analysis

When the user invokes this skill ($neo-optimize <target>), do the following:

1. Locate the target. It may be a function name (process_large_dataset), a file, or a description ("the user-search query path"). Use Grep/Read to find the actual implementation.

2. Capture the current implementation plus its callers if you can do so cheaply. Neo can suggest better algorithms, but only if it sees how the code is used.

3. Invoke Neo with an optimization-framed prompt. Allow up to 5 minutes.

   neo <<'QUERY'
   Suggest optimizations for the following code. Focus on: algorithmic improvements (lower asymptotic complexity), redundant computation, allocation in hot loops, IO batching opportunities. Skip micro-style changes.
   
   <paste current implementation + relevant callers>
   QUERY
   

4. Present Neo's suggestions ranked by expected impact. Each CodeSuggestion includes estimated_risk and blast_radius — surface those alongside the recommendation.

5. For high-risk changes, recommend benchmarking before applying. Neo's confidence reflects pattern-match strength, not measured speedup.

Notes

• Algorithmic suggestions tend to come back with high confidence when Neo has seen similar patterns before — that's the memory-driven reasoning effort kicking in.
• If Neo returns "I cannot find evidence" or low-confidence-only output, that's a signal the optimization isn't obvious and warrants human investigation rather than blind application.