syncable-dev/memtrace-refactoring-guide

Overview

Guided refactoring workflow — identifies refactoring candidates using structural analysis, scores them by risk and priority, checks Cortex decision memory for rationale/bans/contracts, and produces a phased refactoring plan. Combines complexity metrics, dead code detection, bridge analysis, temporal evolution, and decision memory to prioritize what to refactor first and how to do it safely.

Steps

1. Identify refactoring candidates

Run these three tools in parallel to build a candidate list:

a) Complexity hotspots: Call find_most_complex_functions with top_n: 20

b) Dead code: Call find_dead_code to find unused symbols

c) Architectural bottlenecks: Call find_bridge_symbols to find chokepoints with too much responsibility

2. Score candidates by volatility

Call get_evolution with from: "90d ago" and mode: "compound".

Review top_touched_symbols and top_changed_files:

• Symbols that are BOTH complex AND frequently changing are the highest priority
• Complex but stable code can wait — it's not causing active pain
• Volatile but simple code may be fine — frequent changes to simple code is normal

Priority matrix:

| | Low Complexity | High Complexity | |---|---|---| | Stable (low change freq) | Leave alone | Monitor; refactor if touched | | Volatile (high change freq) | Normal; leave alone | TOP PRIORITY — refactor first |

3. Assess risk for top candidates

For each top-priority candidate, call get_impact with direction: both:

• Low risk → refactor directly
• Medium risk → refactor with comprehensive tests
• High/Critical risk → plan incremental migration with backward compatibility

Also call get_symbol_context to check:

• How many processes does this symbol participate in? (More = more testing needed)
• Is it part of a cross-repo API? (If yes, coordinate with consumers)

4. Check decision memory before refactoring/removing

For each top candidate, call recall_decision("<symbol/subsystem/refactor intent>"). If you have a numeric symbol_id, call why_is_this_here(symbol_id) and governing_contracts(symbol_id).

• A matching held decision/ban can veto or reshape the refactor.
• Contracts become acceptance criteria for the new design.
• CannotProve is unknown, not permission to delete.

5. Understand the neighbourhood

For each refactoring target, call analyze_relationships:

• find_callees — what does it depend on? These become candidates for extraction
• find_callers — what depends on it? These need updating after refactoring
• class_hierarchy — is it part of an inheritance chain? Liskov concerns

Full parameter spec for every Memtrace tool: references/mcp-parameters.md (bundled at the memtrace-skills plugin root).

6. Check community boundaries

Call list_communities and check: does the refactoring target sit at a community boundary?

• If yes, the refactoring may involve splitting responsibilities across modules
• If it belongs clearly to one community, the refactoring is more contained

7. Produce the refactoring plan

Synthesize into a phased plan:

Phase 1 — Quick Wins:

• Dead code removal (zero-risk deletions)
• Simple functions with high churn (reduce volatility)

Phase 2 — High-Impact Refactors:

• Complex + volatile functions (highest priority by the matrix)
• Bridge symbols with too many responsibilities (extract interfaces)

Phase 3 — Structural Improvements:

• Splitting oversized communities into smaller, focused modules
• Extracting shared logic from bridge symbols into dedicated services

For each item, include:

1. Target — function/class name, file, current complexity score
2. Why — complexity + volatility + blast radius rationale
3. How — specific refactoring approach (extract method, split class, introduce interface)
4. Decision Memory — relevant Cortex decisions/bans/contracts, or CannotProve as unknown
5. Risk — impact analysis rating + affected processes
6. Test Plan — which callers/processes to verify

Decision Points

| Condition | Action | |-----------|--------| | Complex + volatile + high blast radius | Highest priority — but plan carefully; incremental approach | | Complex + stable + low blast radius | Can wait; refactor when you're already touching nearby code | | Dead code with zero callers | Run Cortex provenance/recall first; zero callers is not proof that no decision/contract keeps it | | Bridge symbol with many dependents | Extract interface first, then refactor implementation behind it | | Symbol in cross-repo API | Coordinate with consumers; backward-compatible changes only | | Cortex returns a held ban/contract | Preserve it or ask before overriding it |

Output

A phased plan (Phases 1–3). One worked entry:

| Field | Example | |---|---| | Target | process_payment — src/billing/processor.py, complexity 38 | | Why | Complex + volatile (14 changes/90d in top_touched_symbols) + 23-symbol blast radius | | First move | Extract validation branch to validate_payment_request; keep callers untouched | | Risk | High — upstream spans 3 processes incl. checkout_flow; incremental migration |

Acceptance criteria:

• Every plan item cites complexity, volatility, blast radius, and decision-memory status — no gut-feel picks.
• High/Critical-risk items include a test plan naming affected callers/processes.

Common Mistakes

| Mistake | Reality | |---------|---------| | Refactoring the most complex function first | Complexity alone isn't enough — prioritize by complexity × volatility | | Deleting all dead code at once | Some "dead" code is called dynamically; verify before batch deletion | | Refactoring without checking blast radius | A "simple" refactor on a bridge symbol can cascade across the codebase | | Not checking temporal evolution | A complex function that hasn't changed in a year is lower priority than a simpler one that changes weekly |