Code refinement

Table of Contents

• Quick Start
• When to Use
• Analysis Dimensions
• Progressive Loading
• Required TodoWrite Items
• Workflow
• Tiered Analysis
• Cross-Plugin Dependencies

Code Refinement Workflow

Analyze and improve living code quality across six dimensions.

Quick Start

/refine-code
/refine-code --level 2 --focus duplication
/refine-code --level 3 --report refinement-plan.md

When To Use

• After rapid AI-assisted development sprints
• Before major releases (quality gate)
• When code "works but smells"
• Refactoring existing modules for clarity
• Reducing technical debt in living code

When NOT To Use

• Removing dead/unused code (use conserve:bloat-detector)

Analysis Dimensions

#	Dimension	Module	What It Catches
1	Duplication & Redundancy	duplication-analysis	Near-identical blocks, similar functions, copy-paste
2	Algorithmic Efficiency	algorithm-efficiency	O(n^2) where O(n) works, unnecessary iterations
3	Clean Code Violations	clean-code-checks	Long methods, deep nesting, poor naming, magic values
4	Architectural Fit	architectural-fit	Paradigm mismatches, coupling violations, leaky abstractions
5	Anti-Slop Patterns	clean-code-checks	Premature abstraction, enterprise cosplay, hollow patterns
6	Error Handling	clean-code-checks	Bare excepts, swallowed errors, happy-path-only
7	Additive Bias	imbue:justify	Workarounds over root fixes, test tampering, unnecessary additions

Plugin-Specific Patterns

Detection patterns for plugin and skill codebases where standard code quality heuristics miss structural issues.

Delegation Stub Bodies

A skill that declares "delegates to X" but still carries the full template body is doing double duty. The delegating skill should be a thin wrapper (under 30 lines) that routes to the target. Flag any delegating skill whose body exceeds 50 lines.

Module Explosion

Flag skills with 10+ module files where 40% or more of content overlaps. Signal: two modules covering the same API surface from different angles (e.g., both describing the same config options or the same CLI flags).

Oversized Single Modules

Flag individual module files exceeding 500 lines as candidates for splitting or trimming. Large modules defeat progressive loading by forcing full-file reads for partial information.

Dead Python References

Skills referencing Python commands (python -m module.name or python -c "from module import ...") where the referenced module does not exist in the plugin's src/ directory. These are stale references to renamed or removed code.

Progressive Loading

Load modules based on refinement focus:

• modules/duplication-analysis.md (~400 tokens): Duplication detection and consolidation
• modules/algorithm-efficiency.md (~400 tokens): Complexity analysis and optimization
• modules/clean-code-checks.md (~450 tokens): Clean code, anti-slop, error handling
• modules/architectural-fit.md (~400 tokens): Paradigm alignment and coupling

Load all for thorough refinement. For focused work, load only relevant modules.

Required TodoWrite Items

1. refine:context-established: Scope, language, framework detection
2. refine:scan-complete: Findings across all dimensions
3. refine:prioritized: Findings ranked by impact and effort
4. refine:plan-generated: Concrete refactoring plan with before/after
5. refine:evidence-captured: Evidence appendix per imbue:proof-of-work
6. refine:findings-verified: Citations confirmed by citation_verifier.py
7. refine:execution-complete: All wave-listed candidates closed-or-rationale'd (only required when invocation includes "execute findings" or stronger; see Step 6)

Workflow

Step 1: Establish Context (refine:context-established)

Detect project characteristics:

# Language detection
find . -not -path "*/.venv/*" -not -path "*/__pycache__/*" \
  -not -path "*/node_modules/*" -not -path "*/.git/*" \
  \( -name "*.py" -o -name "*.ts" -o -name "*.rs" -o -name "*.go" \) \
  | head -20

# Framework detection
ls package.json pyproject.toml Cargo.toml go.mod 2>/dev/null

# Size assessment
find . -not -path "*/.venv/*" -not -path "*/__pycache__/*" \
  -not -path "*/node_modules/*" -not -path "*/.git/*" \
  \( -name "*.py" -o -name "*.ts" -o -name "*.rs" \) \
  | xargs wc -l 2>/dev/null | tail -1

Step 2: Dimensional Scan (refine:scan-complete)

Load relevant modules and execute analysis per tier level. For dimension 7 (Additive Bias), run Skill(imbue:justify) to compute the bias score, check Iron Law compliance, and flag unnecessary additions or workarounds.

Step 3: Prioritize (refine:prioritized)

Rank findings by:

• Impact: How much quality improves (HIGH/MEDIUM/LOW)
• Effort: Lines changed, files touched (SMALL/MEDIUM/LARGE)
• Risk: Likelihood of introducing bugs (LOW/MEDIUM/HIGH)

Priority = HIGH impact + SMALL effort + LOW risk first.

Step 4: Generate Plan (refine:plan-generated)

For each finding, produce:

• File path and line range
• Anchor: verbatim source text at the cited line
• Current code snippet
• Proposed improvement
• Rationale (which principle/dimension)
• Estimated effort

Step 5: Evidence Capture (refine:evidence-captured)

Document with imbue:proof-of-work (if available):

• [E1], [E2] references for each finding
• Metrics before/after where measurable
• Principle violations cited

Fallback: If imbue is not installed, capture evidence inline in the report using the same [E1] reference format without TodoWrite integration.

Step 6: Execute Findings (refine:execution-complete)

Steps 1-5 produce a plan. Steps 6 produces closures. Both are part of the skill. Execution does not stop at planning unless the user explicitly says "plan only".

Execution mode detection

Match the user's invocation phrasing against this table to determine execution scope:

User said	Mode	Stop when
/code-refinement (no qualifier)	Plan only	After Step 5
--dry-run or "just plan"	Plan only	After Step 5
"execute findings" / "apply fixes"	Plan, execute Wave 1	After all SMALL-effort, and LOW-risk findings closed
"execute all findings" / "all phases" / "all waves"	Plan and execute every wave	After every finding (or every wave-listed candidate) is either closed by commit or has explicit per-item rationale in the synthesis
"ignore scope guard"	Override branch-size limits	Branch metrics do not gate execution. Continue past RED zone.
"do not stop until complete" / "until ALL ... complete"	No mid-task summaries	Only declare done when synthesis has every wave-listed candidate closed-or-rationale'd

The triggers compose: --tier 3 --execute all findings --ignore-scope-guard means run every Wave 2 and Wave 3 candidate to closure regardless of branch size.

Completion gate (when execution mode is active)

The task is not complete until ALL of the following hold:

1. Wave 2 candidates (medium-effort, listed in synthesis "Wave 2 Candidates" section): every entry has either a closure commit or an explicit per-item line in the synthesis stating why it is not viable.
2. Wave 3 candidates (large-effort, listed in synthesis "Wave 3 Candidates" section): same gate. Do not pre-emptively defer LARGE-effort items with generic "needs dedicated PR" rationale when the user said "execute all". Execute the mechanical ones (split-by-class, mixin-package, module-merge) and reserve "deferred" only for items requiring architecture-level decisions (schema changes, new dependency declarations, new venv layouts).
3. Synthesis updated: docs/refinement/<date>/00-synthesis.md records every closure with its commit SHA and every deferral with one-sentence rationale.

Anti-pattern detector for the agent itself

If the model finds itself doing any of the following during execution, this is a stop-hook leak. Go back to executing findings:

Anti-pattern	Recognise as
"Wave 2 closed. Moving to Wave 3." (mid-run summary)	Premature turn-completion signal: keep working
"Documenting deferred items with rationale" before all mechanical items are done	Skipping execution under a paper trail
Writing a completion summary while >0 listed candidates lack closure-or-rationale	Violation of completion gate
Re-asking user "should I continue?" when invocation included "do not stop"	Ignoring the explicit no-mid-task-summary contract

If the harness fires a stop signal mid-execution and the completion gate is not met, immediately resume with the next finding.

Verify Findings Are Grounded (refine:findings-verified)

Every finding must cite a real location and a verbatim anchor. Write findings to .review/findings.json and confirm each citation resolves:

python plugins/imbue/scripts/citation_verifier.py \
  --findings .review/findings.json --repo-root .

Drop or label UNVERIFIED any finding the verifier fails (exit 1); only verified findings enter the report. See Skill(imbue:review-core) Step 5 and Skill(imbue:structured-output) for the schema.

Exit Criteria

• All six analysis dimensions have a verdict (finding or "no issue detected") for the target scope.
• Each finding includes a file path, line range, and verbatim Anchor (the exact source text at that line).
• Every reported finding carries a Location + verbatim Anchor confirmed by citation_verifier.py (exit 0), or unverified findings were dropped or labeled UNVERIFIED.

Tiered Analysis

Tier	Time	Scope
1: Quick (default)	2-5 min	Complexity hotspots, obvious duplication, naming, magic values
2: Targeted	10-20 min	Algorithm analysis, full duplication scan, architectural alignment
3: Deep	30-60 min	All above and cross-module coupling, paradigm fitness, thorough plan

Cross-Plugin Dependencies

Dependency	Required?	Fallback
imbue:proof-of-work	Optional	Inline evidence in report
conserve:code-quality-principles	Optional	Built-in KISS/YAGNI/SOLID checks
archetypes:architecture-paradigms	Optional	Principle-based checks only (no paradigm detection)

Supporting Modules

• Code quality analysis - duplication detection commands and consolidation strategies

When optional plugins are not installed, the skill degrades gracefully:

• Without imbue: Evidence captured inline, no TodoWrite proof-of-work
• Without conserve: Uses built-in clean code checks (subset)
• Without archetypes: Skips paradigm-specific alignment, uses coupling/cohesion principles only