complexity-cuts — Lower Big-O on Existing Code
lemmaly prevents bad complexity before code is written. complexity-cuts fixes it after the fact: code already exists, it works, but its time or space complexity is worse than necessary.
Violating the letter of these rules is violating the spirit of the skill. Adapting "just a little" is how a faster-but-wrong rewrite ships.
When to Use This Skill
Use complexity-cuts when refactoring existing code that has poor Big-O:
- Nested loops,
O(n²) or worse scans, repeated work, redundant allocations, blown memory.
- Stated symptoms: "this is slow on large inputs", "times out", "OOM", "too much memory", "reduce complexity", "optimize this algorithm".
- N+1 query patterns in ORMs (Prisma, Drizzle, SQLAlchemy, Django, ActiveRecord).
await inside for over independent items causing serial latency.
For preventing bad complexity before code is written, use lemmaly. For math-level optimizations (Bloom, HLL, FFT, JL projection), escalate to mathguard.
The Iron Law
NO TRANSFORMATION WITHOUT EXISTING TESTS GREEN BEFORE AND AFTER
If the code has no tests, you write a characterization test first (golden input → current output). Then transform. Then verify the test still passes. If you skip this, the optimization can silently break callers — and faster-but-wrong is worse than slow-and-right.
Non-negotiable rules
State current and target Big-O before touching code. In one line:
- Current:
time = O(?), space = O(?)
- Target:
time = O(?), space = O(?)
- Dominant input dimension (n = what, how large in practice)
If you cannot state current Big-O, you do not yet understand the code. Read more.
Identify the bottleneck, do not guess. Point to the exact line(s) responsible for the dominant term. Nested loop? Repeated linear scan? Recomputation? Allocation inside a hot loop? The fix lives there, not elsewhere.
One transformation at a time, with a verify-revert-stop loop. The loop is:
- Apply exactly one transformation from the playbook.
- Run the existing test suite (or the characterization test you wrote per the Iron Law).
- If any test breaks: revert immediately. Do not patch the test. Do not patch around the failure. Revert.
- Count reverts on this piece of code. If 3 reverts in a row, STOP optimizing. The bottleneck is wrong, the transformation is wrong, or the code has invariants you have not modeled. Escalate to
invariant-guard and write the missing contract — do not try a fourth transformation.
- Only after a transformation lands green: pick the next one.
Stacked changes hide regressions. Patched tests hide regressions louder.
Preserve semantics exactly. Lower complexity must not change outputs, ordering guarantees, stability, or error behavior. If the optimization requires a semantic change (e.g. unordered output), call it out explicitly and confirm it is acceptable.
No invented numbers. Never write "10x faster" or "saves 200MB" without measuring. Write <measured: TBD> and move on, or actually measure with a representative input.
Always report the measured speedup ratio after a transformation lands. Once the new code is green, run a representative benchmark (same input, same machine, warm cache) and report before → after plus the ratio as N× faster (or N× less memory). One line, attached to the diff:
p50: 186 ms → 1.1 ms (169× faster, n=20,000, 200 samples)
If you cannot measure (e.g. the win is purely asymptotic on inputs you don't have), say so explicitly: asymptotic only, no measurement — O(n²) → O(n). Never silently skip this step.
The transformation playbook
The vast majority of real-world Big-O wins come from a small set of moves. Try them in this order:
Time-complexity reductions
| Smell |
Fix |
Typical win |
for x in A: if x in B where B is list/array |
Convert B to Set/Map once |
O(n·m) → O(n+m) |
| Nested loop computing pairs/joins |
Hash-join on the key; index by lookup field |
O(n·m) → O(n+m) |
Repeated .find / .indexOf / .includes inside a loop |
Precompute index Map<key, item> outside loop |
O(n^2) → O(n) |
| Repeated recomputation of same value |
Memoize / cache by input key |
O(n·f(n)) → O(n + f(n)) |
| Sort inside a loop |
Sort once outside |
O(n^2 log n) → O(n log n) |
| Linear scan for min/max/median repeatedly |
Heap / sorted structure |
O(n·k) → O(n log k) |
| Recursive recomputation (naive Fibonacci shape) |
Memoize, or convert to iterative DP |
exponential → O(n) |
| String concatenation in a loop (some langs) |
Use builder / join / array.push then join |
O(n^2) → O(n) |
| Repeated regex compile in loop |
Compile once outside |
constant-factor, large |
| Counting / grouping via nested loop |
Single pass with Counter / Map<k, count> |
O(n^2) → O(n) |
| Sliding-window written as nested loop |
Two-pointer |
|
1---2name: complexity-cuts3description: Lower Big-O on existing code via a one-transformation-at-a-time playbook with verify-revert-stop. For new code use lemmaly; for math-level wins escalate to mathguard.4---567# complexity-cuts — Lower Big-O on Existing Code89`lemmaly` prevents bad complexity before code is written. **complexity-cuts** fixes it after the fact: code already exists, it works, but its time or space complexity is worse than necessary.1011**Violating the letter of these rules is violating the spirit of the skill.** Adapting "just a little" is how a faster-but-wrong rewrite ships.1213## When to Use This Skill1415Use **complexity-cuts** when refactoring existing code that has poor Big-O:1617- Nested loops, `O(n²)` or worse scans, repeated work, redundant allocations, blown memory.18- Stated symptoms: "this is slow on large inputs", "times out", "OOM", "too much memory", "reduce complexity", "optimize this algorithm".19- N+1 query patterns in ORMs (Prisma, Drizzle, SQLAlchemy, Django, ActiveRecord).20- `await` inside `for` over independent items causing serial latency.2122For *preventing* bad complexity before code is written, use **`lemmaly`**. For math-level optimizations (Bloom, HLL, FFT, JL projection), escalate to **`mathguard`**.2324## The Iron Law2526```text27NO TRANSFORMATION WITHOUT EXISTING TESTS GREEN BEFORE AND AFTER28```2930If the code has no tests, you write a characterization test first (golden input → current output). Then transform. Then verify the test still passes. If you skip this, the optimization can silently break callers — and faster-but-wrong is worse than slow-and-right.3132## Non-negotiable rules33341. **State current and target Big-O before touching code.** In one line:35 - Current: `time = O(?)`, `space = O(?)`36 - Target: `time = O(?)`, `space = O(?)`37 - Dominant input dimension (n = what, how large in practice)3839 If you cannot state current Big-O, you do not yet understand the code. Read more.40412. **Identify the bottleneck, do not guess.** Point to the exact line(s) responsible for the dominant term. Nested loop? Repeated linear scan? Recomputation? Allocation inside a hot loop? The fix lives there, not elsewhere.42433. **One transformation at a time, with a verify-revert-stop loop.** The loop is:4445 1. Apply exactly one transformation from the playbook.46 2. Run the existing test suite (or the characterization test you wrote per the Iron Law).47 3. If any test breaks: **revert immediately.** Do not patch the test. Do not patch around the failure. Revert.48 4. Count reverts on this piece of code. If **3 reverts in a row**, STOP optimizing. The bottleneck is wrong, the transformation is wrong, or the code has invariants you have not modeled. Escalate to `invariant-guard` and write the missing contract — do not try a fourth transformation.49 5. Only after a transformation lands green: pick the next one.5051 Stacked changes hide regressions. Patched tests hide regressions louder.52534. **Preserve semantics exactly.** Lower complexity must not change outputs, ordering guarantees, stability, or error behavior. If the optimization requires a semantic change (e.g. unordered output), call it out explicitly and confirm it is acceptable.54555. **No invented numbers.** Never write "10x faster" or "saves 200MB" without measuring. Write `<measured: TBD>` and move on, or actually measure with a representative input.56576. **Always report the measured speedup ratio after a transformation lands.** Once the new code is green, run a representative benchmark (same input, same machine, warm cache) and report `before → after` plus the ratio as `N× faster` (or `N× less memory`). One line, attached to the diff:5859 ```text60 p50: 186 ms → 1.1 ms (169× faster, n=20,000, 200 samples)61 ```6263 If you cannot measure (e.g. the win is purely asymptotic on inputs you don't have), say so explicitly: `asymptotic only, no measurement — O(n²) → O(n)`. Never silently skip this step.6465## The transformation playbook6667The vast majority of real-world Big-O wins come from a small set of moves. Try them in this order:6869### Time-complexity reductions7071| Smell | Fix | Typical win |72|---|---|---|73| `for x in A: if x in B` where B is list/array | Convert B to `Set`/`Map` once | O(n·m) → O(n+m) |74| Nested loop computing pairs/joins | Hash-join on the key; index by lookup field | O(n·m) → O(n+m) |75| Repeated `.find` / `.indexOf` / `.includes` inside a loop | Precompute index `Map<key, item>` outside loop | O(n^2) → O(n) |76| Repeated recomputation of same value | Memoize / cache by input key | O(n·f(n)) → O(n + f(n)) |77| Sort inside a loop | Sort once outside | O(n^2 log n) → O(n log n) |78| Linear scan for min/max/median repeatedly | Heap / sorted structure | O(n·k) → O(n log k) |79| Recursive recomputation (naive Fibonacci shape) | Memoize, or convert to iterative DP | exponential → O(n) |80| String concatenation in a loop (some langs) | Use builder / `join` / `array.push` then join | O(n^2) → O(n) |81| Repeated regex compile in loop | Compile once outside | constant-factor, large |82| Counting / grouping via nested loop | Single pass with `Counter` / `Map<k, count>` | O(n^2) → O(n) |83| Sliding-window written as nested loop | Two-pointer
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