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Code Review Skill Creation and Worklog Cleanup

Session Summary

Date: 2025-12-28 (Continuation from 2025-12-26 session)

Focus Area: Creating /code-review skill, cleaning up worklog skill

Accomplishments

  • Ran orch consensus on code-review workflow design (gpt + gemini, qwen was flaky)
  • Created /code-review skill based on consensus recommendations
  • Closed proto skills-fvc and 7 child tasks (replaced by skill)
  • Added code-review to dotfiles claudeCodeSkills deployment
  • Added code-review to delbaker .skills manifest
  • Holistic review of skills repo (50 open issues, 2 blocked epics)
  • Completed all 5 worklog cleanup tasks (127 -> 88 lines, -31%)
  • Tested updated extract-metrics.sh script
  • Ran code-review on updated worklog skill (clean - no issues worth filing)
  • Filed 5 issues in dotfiles from code-review of flake.nix

Key Decisions

Decision 1: Skill over Proto for code-review workflow

  • Context: Had both lenses (prompts) and a beads proto (skills-fvc) for code review

  • Options considered:

    1. Keep proto as workflow orchestrator - unused, adds complexity
    2. Create Claude Code skill as entrypoint - matches actual usage pattern
    3. Ad-hoc documentation only - too loose
  • Rationale: Consensus from GPT + Gemini agreed skill is the right abstraction. Proto was never actually used (bd pour/wisp commands).
  • Impact: Simpler mental model - /code-review is the entrypoint, lenses are prompts it uses

Decision 2: Interactive by default for code-review

  • Context: How much automation for issue filing?

  • Options considered:

    1. Full automation - file all findings automatically
    2. Interactive - present findings, ask before filing
    3. Report only - never file, just output
  • Rationale: Both models recommended interactive. Prevents issue spam, keeps human in loop.
  • Impact: Skill asks "which findings to file?" after presenting summary

Decision 3: Consolidate worklog skill aggressively

  • Context: 5 cleanup tasks from earlier lens review
  • Rationale: Quick wins, reduce maintenance burden, test the lens -> issue -> fix cycle
  • Impact: 127 -> 88 lines (-31%), cleaner skill prompt

Problems & Solutions

Problem Solution Learning
Orch consensus with qwen hanging Kill and retry with gpt + gemini only qwen has reliability issues on long prompts
Orch consensus timing out Run models separately with orch chat, synthesize manually Parallel queries work, consensus command buffers until all complete
Proto tasks polluting other repos Close proto, use skill instead molecules.jsonl cross-repo loading needs work (bd-k2wg)
extract-metrics.sh not showing branch/status Added BRANCH and STATUS output to script Script was metrics-focused, now includes full git context
Semantic compression references Already removed when merging Guidelines/Remember Sometimes cleanup tasks overlap

Technical Details

Code Changes

  • Total files modified: 20

  • Key files changed:

    • skills/code-review/SKILL.md - New skill (120 lines)
    • skills/code-review/README.md - Skill documentation
    • skills/code-review/lenses/*.md - Bundled lens prompts
    • skills/worklog/SKILL.md - Refactored (127 -> 88 lines)
    • skills/worklog/scripts/extract-metrics.sh - Added branch/status output
    • modules/ai-skills.nix - Added code-review to skills list
    • ~/proj/dotfiles/home/claude.nix - Added code-review to claudeCodeSkills
    • ~/proj/delbaker/.skills - Added code-review to manifest

New Files Created

  • skills/code-review/SKILL.md - Main skill prompt
  • skills/code-review/README.md - Quick reference
  • skills/code-review/lenses/ - Bundled copies of lens prompts

Commands Used 

# Orch consensus (failed with 3 models)
uv run orch consensus --temperature 1.0 "..." gemini gpt qwen3

# Orch chat (worked for individual models)
uv run orch chat "..." --model gpt --temperature 1.0
uv run orch chat "..." --model gemini --temperature 1.0

# Test updated extract-metrics script
./skills/worklog/scripts/extract-metrics.sh

# Update skills flake in dotfiles
cd ~/proj/dotfiles && nix flake update skills

Architecture Notes

  • Skill deployment: home-manager symlinks skills from nix store to ~/.claude/skills/
  • Per-repo skills: .skills manifest + use-skills.sh creates repo-local symlinks
  • Lenses bundled in skill but also deployed to ~/.config/lenses/ for direct orch use
  • Proto/molecules layer deemed overhead - skill is simpler for this use case

Process and Workflow

What Worked Well

  • Orch consensus (when it worked) provided useful multi-model perspective
  • Quick iteration: create skill -> deploy -> test on real target (dotfiles flake.nix)
  • TodoWrite for tracking the 5 worklog tasks
  • Beads for tracking issues and closing them as work completed
  • Running code-review on recently modified code as validation

What Was Challenging

  • Orch reliability: qwen hanging, consensus command timing out
  • Remote git server down throughout session (local commits only)
  • Context recovery from previous session compaction

Learning and Insights

Technical Insights

  • orch chat is more reliable than orch consensus for long prompts
  • Skills are the right abstraction for Claude Code workflows - simpler than protos
  • Shell script changes need home-manager rebuild to deploy

Process Insights

  • Lens -> issue -> fix cycle works well for incremental cleanup
  • Running multiple lenses finds overlapping issues (good for synthesis)
  • Interactive review prevents over-filing low-value issues

Architectural Insights

  • Skills repo has 3 layers: skills (prompts), lenses (review prompts), workflows (protos)
  • Lenses are a subset of skills conceptually - focused single-purpose prompts
  • Proto/molecule layer adds complexity without proportional benefit currently

Context for Future Work

Open Questions

  • Should lenses output JSON for structured parsing?
  • How to handle orch reliability issues (qwen, timeouts)?
  • Should code-review skill use orch internally or leave it optional?

Next Steps

  • Run code-review on other skills (niri-window-capture has pending review)
  • Consider remaining 2 worklog tasks (j2a done, njb done - actually all done now)
  • Address dotfiles issues filed this session (5 issues in flake.nix)
  • Rebuild home-manager to deploy updated skills

Related Work

Raw Notes

  • Session started from context recovery (previous session compacted)
  • GPT recommendation: skill as entrypoint, orch for synthesis only, JSON output, interactive by default
  • Gemini recommendation: consolidate into skills/, single agent explores, orch at end for filtering
  • Both agreed: delete proto, make skill, interactive review
  • Worklog cleanup tasks all from earlier lens review (2025-12-25)
  • extract-metrics.sh output changed from "Session Metrics" to "Git Context"

Orch Consensus Key Points

From GPT:

  • Skill = primary workflow entrypoint
  • Orch = synthesis/filtering only, not for running every lens
  • JSON source of truth, markdown is rendering
  • Repo-local beads storage to avoid cross-repo pollution

From Gemini:

  • Rename lenses to skills (we kept them separate)
  • Single agent explores, orch filters at end
  • "Driver" pattern - human approves before filing
  • Delete proto as unused complexity

Commits This Session

  1. feat: add /code-review skill with bundled lenses
  2. docs: add code-review to skills list (ai-skills.nix)
  3. feat: add code-review skill (dotfiles)
  4. chore: update skills flake (dotfiles)
  5. refactor(worklog): consolidate skill prompt
  6. refactor(worklog): consolidate git commands into script

Session Metrics

  • Commits made: 6 (across skills and dotfiles repos)
  • Files touched: 20
  • Lines added/removed: +829/-70
  • Issues filed: 5 (in dotfiles)
  • Issues closed: 8 (proto) + 5 (worklog) = 13
  • Tests added: 0