feat: add code-review to availableSkills
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{"id":"skills-d87","title":"orch skill is documentation-only, needs working invocation mechanism","description":"The orch skill provides SKILL.md documentation but no working invocation mechanism.\n\n**Resolution**: Install orch globally via home-manager (dotfiles-3to). The skill documents a system tool, doesn't need to bundle it.\n\n**Blocked by**: dotfiles-3to (Add orch CLI to home-manager packages)","status":"closed","priority":2,"issue_type":"bug","created_at":"2025-12-14T11:54:03.157039164-08:00","updated_at":"2025-12-16T18:45:24.39235833-08:00","closed_at":"2025-12-16T18:45:24.39235833-08:00","close_reason":"Updated docs to use globally installed orch CLI"}
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{"id":"skills-dnm","title":"Refactor deploy-skill.sh: dedupe injection calls","description":"File: bin/deploy-skill.sh (lines 112-189)\n\nIssues:\n- Three nearly-identical inject_nix_config() calls\n- Only difference is config block content and target file\n- Repeated pattern bloats file\n\nFix:\n- Parameterize inject_nix_config() better\n- Or create config-specific injection functions\n- Reduce duplication\n\nSeverity: MEDIUM","status":"open","priority":3,"issue_type":"task","created_at":"2025-12-24T02:51:01.855452762-05:00","updated_at":"2025-12-24T02:51:01.855452762-05:00"}
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{"id":"skills-dpw","title":"orch: add command to show available/configured models","description":"## Problem\n\nWhen trying to use orch, you have to trial-and-error through models to find which ones have API keys configured. Each failure looks like:\n\n```\nError: GEMINI_API_KEY not set. Required for Google Gemini models.\n```\n\nNo way to know upfront which models are usable.\n\n## Proposed Solution\n\nAdd `orch models` or `orch status` command:\n\n```bash\n$ orch models\nAvailable models:\n ✓ flash (GEMINI_API_KEY set)\n ✓ gemini (GEMINI_API_KEY set)\n ✗ deepseek (OPENROUTER_KEY not set)\n ✗ qwen (OPENROUTER_KEY not set)\n ✓ gpt (OPENAI_API_KEY set)\n```\n\nOr at minimum, on failure suggest alternatives:\n```\nError: GEMINI_API_KEY not set. Try --model gpt or --model deepseek instead.\n```\n\n## Context\n\nHit this while trying to brainstorm with high-temp gemini - had to try 4 models before realizing none were configured in this environment.","status":"closed","priority":3,"issue_type":"feature","created_at":"2025-12-04T14:10:07.069103175-08:00","updated_at":"2025-12-04T14:11:05.49122538-08:00","closed_at":"2025-12-04T14:11:05.49122538-08:00"}
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{"id":"skills-e8h","title":"Investigate waybar + niri integration improvements","description":"Look into waybar configuration and niri compositor integration.\n\nPotential areas:\n- Waybar modules for niri workspaces\n- Status indicators\n- Integration with existing niri-window-capture skill\n- Custom scripts in pkgs/waybar-scripts\n\nRelated: dotfiles has home/waybar.nix (196 lines) and pkgs/waybar-scripts/","status":"open","priority":2,"issue_type":"task","created_at":"2025-12-28T20:11:23.115445797-05:00","created_by":"dan","updated_at":"2025-12-28T20:11:23.115445797-05:00"}
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{"id":"skills-e96","title":"skill: semantic-grep using LSP","description":"Use workspace/symbol, documentSymbol, and references instead of ripgrep.\n\nExample: 'Find all places where we handle User objects but only where we modify the email field directly'\n- LSP references finds all User usages\n- Filter by AST analysis for .email assignments\n- Return hit list for bead or further processing\n\nBetter than regex for Go interfaces, Rust traits, TS types.","status":"open","priority":3,"issue_type":"task","created_at":"2025-12-24T02:29:57.119983837-05:00","updated_at":"2025-12-24T02:29:57.119983837-05:00","dependencies":[{"issue_id":"skills-e96","depends_on_id":"skills-gga","type":"blocks","created_at":"2025-12-24T02:30:06.632906383-05:00","created_by":"daemon"}]}
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{"id":"skills-ebh","title":"Compare bd-issue-tracking skill files with upstream","description":"Fetch upstream beads skill files and compare with our condensed versions to identify differences","status":"closed","priority":2,"issue_type":"task","created_at":"2025-12-03T20:14:07.886535859-08:00","updated_at":"2025-12-03T20:19:37.579815337-08:00","closed_at":"2025-12-03T20:19:37.579815337-08:00"}
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{"id":"skills-ebl","title":"Benchmark vision model UI understanding","description":"## Goal\nMeasure how well vision models can answer UI questions from screenshots.\n\n## Test cases\n1. **Element location**: \"Where is the Save button?\" → coordinates\n2. **Element identification**: \"What buttons are visible?\" → list\n3. **State detection**: \"Is the checkbox checked?\" → boolean\n4. **Text extraction**: \"What does the error message say?\" → text\n5. **Layout understanding**: \"What's in the sidebar?\" → structure\n\n## Metrics\n- Accuracy: Does the answer match ground truth?\n- Precision: How close are coordinates to actual element centers?\n- Latency: Time from query to response\n- Cost: Tokens consumed per query\n\n## Prompt engineering questions\n- Does adding a grid overlay help coordinate precision?\n- What prompt format gives most actionable coordinates?\n- Can we get bounding boxes vs point coordinates?\n\n## Comparison baseline\n- Manual annotation of test screenshots\n- AT-SPI data (once enabled) as ground truth\n\n## Depends on\n- Test screenshots from real apps\n- Ground truth annotations","status":"open","priority":2,"issue_type":"task","created_at":"2025-12-17T14:13:10.038933798-08:00","updated_at":"2025-12-17T14:13:10.038933798-08:00"}
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