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chrysopoeia

chrysopoeia

Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundl…

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name: chrysopoeia locale: caveman-ultra source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-19" description: > Extract maximum value from existing code — performance optimization, API surface refinement, and dead weight elimination. The art of turning base code into gold through systematic identification and amplification of value-bearing patterns. Use when optimizing a working but sluggish codebase, refining an API surface that has accumulated cruft, reducing bundle size or memory footprint, or preparing code for open-source release — when code works correctly but doesn't shine and needs polish rather than a full rewrite. license: MIT allowed-tools: Read Write Edit Bash Grep Glob metadata: author: Philipp Thoss version: "1.0" domain: alchemy complexity: intermediate language: multi tags: alchemy, optimization, value-extraction, performance, refinement, gold

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.                                        |
+--------+---------------------------------------------------------+
  1. Perf work → profile first:
    • Hot paths (time sink)
    • Cold paths (rare → maybe dross)
    • Mem alloc patterns
  2. 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
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