Chrysopoeia

Pull max value from code → find gold (high-val), lead (heavy), dross (dead). Amplify gold, transmute lead, purge dross.

Use When

• Working code sluggish → optimize perf
• API surface crufty → refine
• Bundle/mem/startup too big → shrink
• Prep open-source release → extract core
• Code works but dull → polish, not rewrite

In

• Required: Codebase/module (paths)
• Required: Value metric (perf, API clarity, bundle, readability)
• Optional: Profiling data/benchmarks
• Optional: Target (e.g., "-40% bundle", "sub-100ms res")
• Optional: Constraints (public API frozen, back-compat req)

Do

Step 1: Assay — Classify

Classify every element by value.

1. Define value metric from In
2. Inventory elements (fns, modules, exports, deps)
3. Classify each:

Value Classification:
+--------+---------------------------------------------------------+
| Gold   | High value, well-designed. Amplify and protect.         |
| Silver | Good value, minor imperfections. Polish.                |
| Lead   | Functional but heavy — poor performance, complex API.   |
|        | Transmute into something lighter.                       |
| Dross  | Dead code, unused exports, vestigial features.          |
|        | Remove entirely.                                        |
+--------+---------------------------------------------------------+

4. Perf work → profile first:
   • Hot paths (time sink)
   • Cold paths (rare → maybe dross)
   • Mem alloc patterns
5. Produce Assay Report: element-by-element w/ evidence

→ Every element classified w/ evidence. Gold marked protect. Lead ranked by impact.

If err: No profiler → static analysis: cyclomatic complexity, dep count, size as proxies. Huge codebase → critical path first.

Step 2: Refine — Amplify Gold

Protect + enhance highest-value elements.

1. Each Gold:
   • Full tests (most valuable asset)
   • Clear interface docs
   • Extractable as reusable module?
2. Each Silver:
   • Targeted improvements (naming, types, minor opt)
   • Tests → Gold-level
   • Resolve minor smells, no restructure
3. Do NOT modify Gold/Silver behavior → polish only

→ Gold + Silver better tested, documented, protected. No behavior change, quality up.

If err: "Gold" reveals hidden problems → reclassify. Honest > protect flawed.

Step 3: Transmute — Lead → Gold

Convert heavy elements to optimized equivalents.

1. Rank Lead by impact (highest resource first)
2. Each Lead → pick strategy:
   • Algo opt: O(n^2) → O(n log n), kill redundant compute
   • Cache/memoize: Store expensive res req'd repeat
   • Lazy eval: Defer compute until needed
   • Batch proc: Many small ops → fewer big ones
   • Simplify: Lower cyclomatic, flatten nesting
3. Apply + measure:
   • Before/after benchmarks (perf)
   • Before/after line counts (complexity)
   • Before/after dep counts (coupling)
4. Valid. behavior identical post-transmute

→ Measurable metric improvement. Each transmuted > Lead predecessor, same behavior.

If err: Lead resists opt in current interface → interface itself = problem. Sometimes transmute = change caller, not impl.

Step 4: Purge — Remove Dross

Kill dead weight systematically.

1. Each Dross → valid. truly unused:
   • Grep all refs (IDE find-usages)
   • Dynamic refs (string dispatch, reflection)?
   • External consumers (library)?
2. Remove confirmed:
   • Delete dead code, unused exports, vestigial features
   • Drop unused deps from manifests
   • Clean config for removed features
3. Valid. nothing breaks post-removal (tests)
4. Doc what + why (commit msgs, not code)

→ Codebase lighter. Bundle/dep count/volume measurably down. Tests pass.

If err: Removal breaks → wasn't dross → reclassify. Dynamic refs hide usage → temp logging before delete to confirm no runtime access.

Step 5: Verify — Weigh Gold

Measure overall improvement.

1. Run same benchmarks as Step 1
2. Before/after on metric
3. Doc results:
   • Refined elements (Gold/Silver wins)
   • Transmuted (Lead → Gold w/ measurements)
   • Purged (Dross removed w/ size/count impact)
   • Overall metric gain (e.g., "47% faster", "32% smaller bundle")

→ Measurable, documented metric improvement. Codebase demonstrably more valuable.

If err: Marginal improvement → orig code better than assumed. Doc learning → knowing code near-optimal = valuable.

Check

• Assay report classifies all w/ evidence
• Gold has full tests + docs
• Lead transmutes show before/after metric gain
• Dross removal valid'd w/ ref checks pre-delete
• Tests pass each stage
• Overall improvement measured + documented
• No behavior regressions
• In constraints met

Traps

• Premature opt: Opt w/o profile → always measure first, opt hot paths
• Polish dross: Effort on code should-be-deleted → classify before refine
• Break Gold: Opt degrades best code → Gold only improves, never worse
• Unmeasured: "Feels faster" ≠ chrysopoeia → quantify every gain
• Opt cold paths: Effort on startup-once code when req loop = bottleneck

→

• athanor — Full four-stage when restructure needed, not just opt
• transmute — Targeted conversion when Lead needs paradigm shift
• review-software-architecture — Architecture-level eval
• review-data-analysis — Data pipeline opt parallels code opt