Paths: File paths ( shared/ , references/ , ../ln-* ) are relative to skills repo root. If not found at CWD, locate this SKILL.md directory and go up one level for repo root. ln-811-algorithm-optimizer Type: L3 Worker Category: 8XX Optimization Parent: ln-810-performance-optimization-coordinator Optimizes target function performance via autoresearch loop: establish baseline benchmark, research best practices, generate 3-7 hypotheses, apply each with keep/discard verification. Overview Aspect Details Input target_file + target_function (or audit findings from ln-650) Output Optimized function with benchmark proof, optimization report Pattern Autoresearch: modify → benchmark → keep (≥10%) / discard Workflow Phases: Pre-flight → Baseline → Research → Hypothesize → Optimize Loop → Report Phase 0: Pre-flight Checks Check Required Action if Missing Target file exists Yes Block optimization Target function identifiable Yes Block optimization Test infrastructure Yes Block optimization (see ci_tool_detection.md) Test coverage for target function Yes Block — no coverage = no safety net Git clean state Yes Block (need clean baseline for revert) Benchmark infrastructure No Generate benchmark (see references) MANDATORY READ: Load shared/references/ci_tool_detection.md — use Benchmarks + Test Frameworks sections. Coverage Verification Before starting optimization, verify target function has test coverage: Step Action 1 Grep test files for target function name / imports from target module 2 If ≥1 test references target → PROCEED 3 If 0 tests reference target → BLOCK with "no test coverage for {function}" Without test coverage, benchmark improvements are meaningless — the optimized function may produce wrong results faster. Worktree & Branch Isolation MANDATORY READ: Load shared/references/git_worktree_fallback.md — use ln-811 row. All work (edits, benchmarks, KEEP commits) in worktree. Never modify main worktree. Phase 1: Establish Baseline Step 1.1: Detect or Generate Benchmark Situation Action Existing benchmark found Use as-is No benchmark exists Generate minimal benchmark (see benchmark_generation.md ) Step 1.2: Run Baseline Parameter Value Runs 5 Metric Median execution time Warm-up 1 discarded run Output baseline_median , baseline_p95 Save baseline result — all improvements measured against this. Phase 2: Research Best Practices MANDATORY READ: Load shared/references/research_tool_fallback.md for MCP tool chain. Research Strategy Priority Tool Query Template 1 mcp__context7__query-docs "{language} {algorithm_type} optimization techniques" 2 mcp__Ref__ref_search_documentation "{language} {function_name} performance best practices" 3 WebSearch "{algorithm_type} optimization {language} benchmark {current_year}" Research Output Collect optimization techniques applicable to the target function. For each technique note: Name and description Expected improvement category (algorithmic complexity, memory, cache, parallelism) Applicability conditions (data size, structure, language features) Phase 3: Generate Hypotheses (3-7) Hypothesis Sources MANDATORY READ: Load optimization_categories.md for category checklist. Source Priority Research findings (Phase 2) 1 Optimization categories checklist 2 Code analysis (anti-patterns in target) 3 Hypothesis Format Field Description id H1, H2, ... H7 category From optimization_categories.md description What to change expected_impact Estimated improvement % risk Low / Medium / High dependencies Other hypotheses this depends on Ordering Sort by: expected_impact DESC, risk ASC . Independent hypotheses first (no dependencies). Phase 4: Optimize Loop (Keep/Discard) Per-Hypothesis Cycle FOR each hypothesis (H1..H7): 1. APPLY: Edit target function (surgical change, function body only) 2. VERIFY: Run tests IF tests FAIL (assertion) → DISCARD (revert) → next hypothesis IF tests CRASH (runtime error, OOM, import error): IF fixable (typo, missing import) → fix & re-run ONCE IF fundamental (design flaw, incompatible API) → DISCARD + log "crash: {reason}" 3. BENCHMARK: Run 5 times, take median 4. COMPARE: improvement = (baseline - new) / baseline * 100 IF improvement >= 10% → KEEP: git add {target_file} git commit -m "perf(H{N}): {description} (+{improvement}%)" new baseline = new median IF improvement < 10% → DISCARD (revert edit) 5. LOG: Record result to experiment log + report Safety Rules Rule Description Scope Only target function body; no signature changes Dependencies No new package installations Revert git checkout -- {target_file} on discard Time budget 30 minutes total for all hypotheses Compound Each KEEP becomes new baseline for next hypothesis Traceability Each KEEP = separate git commit with hypothesis ID in message Isolation All work in isolated worktree; never modify main worktree Keep/Discard Decision Condition Decision Action Tests fail DISCARD Revert, log reason Improvement ≥ 10% KEEP Update baseline Improvement 10-20% BUT complexity increase REVIEW Log as "marginal + complex", prefer DISCARD Improvement < 10% DISCARD Revert, log as "insufficient gain" Regression (slower) DISCARD Revert, log regression amount Simplicity criterion (per autoresearch): If improvement is marginal (10-20%) and change significantly increases code complexity (>50% more lines, deeply nested logic, hard-to-read constructs), prefer DISCARD. Simpler code at near-equal performance wins. Phase 5: Report Results Report Schema Field Description target File path + function name baseline Original median benchmark final Final median after all kept optimizations total_improvement Percentage improvement hypotheses_tested Total count hypotheses_kept Count of kept optimizations hypotheses_discarded Count + reasons optimizations[] Per-kept: id, category, description, improvement% Experiment Log Write to {project_root}/.optimization/ln-811-log.tsv : Column Description timestamp ISO 8601 hypothesis_id H1..H7 category From optimization_categories.md description What changed baseline_ms Baseline median before this hypothesis result_ms New median after change improvement_pct Percentage change status keep / discard / crash commit Git commit hash (if kept) Append to existing file if present (enables tracking across multiple runs). Cleanup Action When Remove generated benchmark If benchmark was auto-generated AND no kept optimizations Keep generated benchmark If any optimization was kept (proof of improvement) Configuration Options :

Target

target_file : "" target_function : ""

Benchmark

benchmark_runs : 5 improvement_threshold : 10

percent

warmup_runs : 1

Hypotheses

max_hypotheses : 7 min_hypotheses : 3

Safety

time_budget_minutes : 30 allow_new_deps : false scope : "function_body"

function_body | module

Verification

run_tests : true run_lint : false Error Handling Error Cause Solution No benchmark framework Stack not in ci_tool_detection.md Generate inline benchmark All hypotheses discarded No effective optimization found Report "no improvements found" Benchmark noise too high Inconsistent timing Increase runs to 10, use p50 Test flake Non-deterministic test Re-run once; if flakes again, skip hypothesis References benchmark_generation.md optimization_categories.md shared/references/ci_tool_detection.md (Benchmarks section) shared/references/research_tool_fallback.md Definition of Done Test coverage for target function verified before optimization Target function identified and baseline benchmark established (5 runs, median) Research completed via MCP tool chain (Context7/Ref/WebSearch) 3-7 hypotheses generated, ordered by expected impact Each hypothesis tested: apply → tests → benchmark → keep/discard Each kept optimization = separate git commit with hypothesis ID Kept optimizations compound (each becomes new baseline) Marginal gains (10-20%) with complexity increase reviewed via simplicity criterion Tests pass after all kept optimizations Experiment log written to .optimization/ln-811-log.tsv Report returned with baseline, final, improvement%, per-hypothesis results Generated benchmark cleaned up if no optimizations kept All changes on isolated branch, pushed to remote Version: 1.0.0 Last Updated: 2026-03-08