skills/docs/worklogs/2025-12-26-multi-lens-code-review-workflow-testing.org

Multi-Lens Code Review Workflow Testing on Orch

• Session Summary
  • Date: 2025-12-26 (Continuation of 2025-12-24 session)
  • Focus Area: Testing multi-lens code review workflow on orch codebase
• Accomplishments
• Key Decisions
  • Decision 1: Use parallel Task agents for lens execution
  • Decision 2: Synthesize findings before filing issues
  • Decision 3: File issues in target repo, not skills repo
• Problems & Solutions
• Technical Details
  • Code Changes
  • Issues Filed in Orch
  • Lens Output Highlights
    • Smells Lens Findings
    • Dead-Code Lens Findings
    • Redundancy Lens Findings
  • Commands Used
  • Architecture Notes
• Process and Workflow
  • What Worked Well
  • What Was Challenging
• Learning and Insights
  • Technical Insights
  • Process Insights
  • Architectural Insights
• Context for Future Work
  • Open Questions
  • Next Steps
  • Related Work
• Raw Notes
  • Lens Analysis Pattern
  • Issues with Current Flow
• Session Metrics

Session Summary

Date: 2025-12-26 (Continuation of 2025-12-24 session)

Focus Area: Testing multi-lens code review workflow on orch codebase

Accomplishments

• Recovered session context from compaction (continued from 2025-12-24 ADR/lenses session)
• Ran 3 code review lenses (smells, dead-code, redundancy) on orch/src/orch/ via parallel Task agents
• Synthesized findings across all lenses using LLM judgment
• Filed 4 actionable beads issues in orch repo based on synthesis
• Validated the LLM-in-the-loop pattern for code review issue filing
• Synced beads in both skills and orch repos
• Push to remote blocked (git server 192.168.1.108:3000 unavailable)

Key Decisions

Decision 1: Use parallel Task agents for lens execution

• Context: Need to run multiple lenses efficiently without sequential bottleneck

• Options considered:

  1. Sequential lens execution - simpler but slower
  2. Parallel Task agents - faster, demonstrates multi-agent pattern
  3. Single agent running all lenses - loses multi-model perspective
• Rationale: Parallel agents match the "50-100 agents at once" vision from Yegge's patterns
• Impact: 3x faster execution, demonstrates scalable pattern for larger codebases

Decision 2: Synthesize findings before filing issues

• Context: Raw lens output produces many overlapping findings

• Options considered:

  1. Mechanical parsing - file every finding as separate issue
  2. LLM synthesis - agent applies judgment, groups related, sets priorities
• Rationale: LLM-in-the-loop pattern per Yegge - agent stays involved at every step
• Impact: Reduced 20+ raw findings to 4 actionable, prioritized issues

Decision 3: File issues in target repo, not skills repo

• Context: Lenses run from skills repo, findings belong to target codebase
• Rationale: Issues should live where the work will be done
• Impact: orch repo now has actionable refactoring tasks with proper context

Problems & Solutions

Problem	Solution	Learning
bd sync in orch failed with prefix mismatch	molecules.jsonl contains skills- prefixed proto tasks	bd-k2wg filed: mol catalog needs hierarchical loading
qwen returns empty content on some prompts	Use gpt/gemini as alternatives, filed orch-loq bug	Content filter issue or API quirk with certain prompt structures
Remote git server unavailable	Local commits preserved, push pending	Distributed git works - local work continues despite server outage
Context compaction lost previous session details	Summary preserved key decisions and file locations	Comprehensive summaries enable seamless session continuity

Technical Details

Code Changes

• Total files modified: 16 (across session including prior context)

• Key commits this session:

  • 1e64515 bd sync: 2025-12-26 01:57:15
  • 9624873 update: code-review proto with LLM-in-the-loop pattern
  • fb15000 refactor: restructure for cross-repo deployment

Issues Filed in Orch

Four synthesized issues based on multi-lens analysis:

1. orch-buy [P2] refactor: create ModelSpec dataclass for provider parsing

   • Addresses primitive obsession (smells) and duplication (redundancy)
   • Same model:stance parsing done in 3+ places in cli.py

2. orch-bp3 [P2] refactor: extract AttachmentProcessor from llm_adapter.py

   • Addresses bloat (500+ line file) and complexity (deep nesting)
   • llm_adapter.py at 590 lines, violates SRP

3. orch-esa [P2] refactor: split cli.py into command handlers

   • Addresses bloat (600+ lines) and smells (flag arguments)
   • CLI file handling multiple concerns

4. orch-loq [P2] bug: qwen returns empty content on some prompts

   • Discovered during lens testing
   • Possibly content filter or API issue

Lens Output Highlights

Smells Lens Findings

• Deep nesting in stdin argument parsing (cli.py:314-327)
• Primitive obsession with model:stance string parsing
• Feature envy in llm_adapter.py (get_model_capabilities unused param)
• Flag arguments changing behavior (format string in show_session)
• Overloaded "status" variable name

Dead-Code Lens Findings

• Duplicate imports in llm_adapter.py (lines 10-12 vs 127-130)
• Empty TYPE_CHECKING block with only pass
• Unused config parameter in get_model_capabilities()
• Re-exports for "backward compatibility" with no external consumers
• Suspicious logic in use_llm_backend() comment vs implementation

Redundancy Lens Findings

• Temperature validation duplicated in config.py and validation.py
• Alias resolution chain: cli.py -> llm_adapter.py -> models_registry.py
• Error printing pattern repeated in cli.py (2x)
• Cancellation handling duplicated in chat() and consensus()
• Magic number 300.0 timeout in 3 places
• Response content filtering pattern repeated 4 times

Commands Used

# Run parallel lens agents via Task tool
# Each agent independently explored orch/src/orch/ and analyzed findings

# Sync beads
bd sync

# Check orch issues
cd /home/dan/proj/orch && bd list --status=open

# Attempt push (failed - server down)
git push

Architecture Notes

• Parallel Task agents with subagent_type=Explore work well for lens analysis
• LLM synthesis step is crucial - raw findings too noisy for direct issue filing
• Proto workflow (skills-fvc) provides structure but agent applies judgment
• molecules.jsonl cross-repo loading has gaps (bd-k2wg filed)

Process and Workflow

What Worked Well

• Parallel agent execution - 3 lenses ran simultaneously
• Context recovery from compaction - summary preserved key decisions
• LLM-in-the-loop synthesis - reduced noise, grouped related findings
• Filing issues in target repo - proper ownership of work items
• Beads tracking - issues have context and history

What Was Challenging

• Remote server outage prevented push (12 local commits pending)
• bd sync prefix mismatch when molecules.jsonl loaded in wrong repo
• Session compaction lost some details but summary covered essentials
• qwen model returning empty responses on some prompts

Learning and Insights

Technical Insights

• Multi-lens analysis reveals overlapping concerns (bloat + smells often co-occur)
• Parallel agents can explore same codebase without conflicts
• LLM judgment at filing step prevents issue spam
• Model quirks (qwen empty responses) need fallback strategies

Process Insights

• "30-40% on code health" pattern from Yegge validated
• Multi-round convergent review could continue refining
• Proto templates provide structure without rigidity
• Session summaries enable context recovery after compaction

Architectural Insights

• molecules.jsonl deployment to ~/.beads/ works but catalog loading incomplete
• Cross-repo workflow: lenses from skills, issues in target repo
• Beads prefix system creates friction when templates cross repos
• Home-manager deployment enables system-wide tool availability

Context for Future Work

Open Questions

• How to handle molecules.jsonl prefix conflicts? (bd-k2wg)
• Should lenses output structured JSON for easier parsing?
• How many rounds of lens passes before diminishing returns?
• Best strategy for qwen empty response fallback?

Next Steps

• Implement bd mol catalog hierarchical loading (bd-k2wg)
• Run bloat lens on orch (skipped this session due to context focus)
• Test lenses on larger codebase (beads itself?)
• Add –synthesize flag to orch for automatic multi-model synthesis

Related Work

• 2025-12-24 ADR Revisions, LSP Research, Code Audit - Created lenses and proto
• Yegge "Vibe Coding" patterns - 30-40% code health, multi-round convergent review
• beads molecules feature - bd mol, bd wisp, bd pour commands
• orch multi-model consensus - Used for lens execution

Raw Notes

• Session recovered from context compaction mid-conversation
• Previous session created lenses/bloat.md, smells.md, dead-code.md, redundancy.md
• Proto skills-fvc with 7 child tasks for structured code review workflow
• Deployed to ~/.config/lenses/ and ~/.beads/molecules.jsonl via home-manager

• Steve Yegge patterns influencing design:

  • Software is throwaway (<1 year shelf life)
  • 50-100 agents at once prediction
  • 30-40% time on code health passes
  • Multi-round convergent review (4-5 passes)
  • "Land the Plane" protocol

Lens Analysis Pattern

1. Agent explores target codebase structure
2. Reads key files (focus on largest/most complex)
3. Applies lens-specific criteria
4. Reports findings with severity and location
5. Parent agent synthesizes across all lenses
6. Parent applies judgment to file grouped, prioritized issues

Issues with Current Flow

• molecules.jsonl loaded globally shows proto tasks in all projects
• Need bd mol catalog to filter by template label
• Prefix collision when syncing across repos

Session Metrics

• Commits made: 3
• Files touched: 16
• Lines added/removed: +1801/-26
• Tests added: 0
• Issues filed: 4 (in orch repo)
• Agents spawned: 3 (parallel lens analysis)