[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CY9CTVH18JYYZFVEGKMFYQ":3,"skill-same:01M6CY9CTVH18JYYZFVEGKMFYQ":56},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":13,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":41,"install":42,"resources":55},"01M6CY9CTVH18JYYZFVEGKMFYQ","deepen-plan",null,"Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details","The-Rabak\u002Fnaysmith","The-Rabak","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F44833742?v=4","01M6CY211HB58Y8JNWWEV9FGN1","01M6CY8HJW3TE1AQW1F13PHYB2",0,"GPL-2.0","https:\u002F\u002Fgithub.com\u002FThe-Rabak\u002Fnaysmith\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fdeepen-plan\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[23,24,25,26,27,28,29,30],"plan","deepen","deepen plan","research","best practices","parallel","enhance","implementation","2026-11-10 10:12:30",false,"---\nname: deepen-plan\ndescription: Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details\n---\n\n## Arguments\n[path to plan file]\n\n# Deepen Plan - Power Enhancement Mode\n\n## Introduction\n\n**Note: The current year is 2026.** Use this when searching for recent documentation and best practices.\n\nThis command takes an existing plan (from `\u002Fworkflows-plan`) and enhances each section with parallel research agents. Each major element gets its own dedicated research sub-agent to find:\n- Best practices and industry patterns\n- Performance optimizations\n- UI\u002FUX improvements (if applicable)\n- Quality enhancements and edge cases\n- Real-world implementation examples\n\nThe result is a deeply grounded, production-ready plan with concrete implementation details.\n\n## Plan File\n\n\u003Cplan_path> #$ARGUMENTS \u003C\u002Fplan_path>\n\n**If the plan path above is empty:**\n1. Check for recent plans: `ls -la docs\u002Fplans\u002F`\n2. Ask the user: \"Which plan would you like to deepen? Please provide the path (e.g., `docs\u002Fplans\u002F2026-01-15-feat-my-feature-plan.md`).\"\n\nDo not proceed until you have a valid plan file path.\n\n## Main Tasks\n\n### 1. Parse and Analyze Plan Structure\n\n\u003Cthinking>\nFirst, read and parse the plan to identify each major section that can be enhanced with research.\n\u003C\u002Fthinking>\n\n**Read the plan file and extract:**\n- [ ] Overview\u002FProblem Statement\n- [ ] Proposed Solution sections\n- [ ] Technical Approach\u002FArchitecture\n- [ ] Implementation phases\u002Fsteps\n- [ ] Code examples and file references\n- [ ] Acceptance criteria\n- [ ] Any UI\u002FUX components mentioned\n- [ ] Technologies\u002Fframeworks mentioned (Laravel, Vue.js, Nuxt, Python, TypeScript, etc.)\n- [ ] Domain areas (data models, APIs, UI, security, performance, etc.)\n\n**Create a section manifest:**\n```\nSection 1: [Title] - [Brief description of what to research]\nSection 2: [Title] - [Brief description of what to research]\n...\n```\n\n### 1.1 Validate Execution Readiness\n\n\u003Cthinking>\nCheck if the plan has sufficiently structured execution chunks for the subagent orchestration model in \u002Fworkflows-work. Plans need per-task success criteria, test commands, and file lists.\n\u003C\u002Fthinking>\n\n**Scan each implementation task\u002Fphase for these required fields:**\n\n- [ ] **Files:** List of files to create or modify\n- [ ] **Depends on:** Dependencies on other tasks\n- [ ] **Success criteria:** Testable checkboxes defining \"done\"\n- [ ] **Test command:** Exact command to verify completion\n\n**Expected task format:**\n\n```markdown\n##### Task N.1: [Task Name]\n**Files:** `path\u002Fto\u002Ffile1.php`, `path\u002Fto\u002Ffile2.php`\n**Depends on:** Task N-1.2 (or \"None\")\n**Success criteria:**\n- [ ] Criterion 1\n- [ ] Criterion 2\n**Test command:** `command to run`\n```\n\n**Scoring:**\n\nCount how many implementation tasks have all four fields. Report:\n\n```\nExecution Readiness: X\u002FY tasks have complete structure (Z%)\n```\n\n**Actions based on score:**\n\n| Score | Action |\n|-------|--------|\n| 80-100% | Plan is execution-ready. Proceed with deepening. |\n| 50-79% | Flag incomplete tasks. During deepening, add missing fields. |\n| 0-49% | Plan needs significant restructuring. Add an \"Execution Readiness\" enhancement pass that decomposes vague phases into structured tasks with all required fields. **Note:** `\u002Fworkflows-work` will refuse to execute plans that lack this structure. |\n\n**For tasks missing structure, the deepening process should:**\n\n1. Break vague phases into specific, scoped tasks\n2. Identify which files each task will create or modify\n3. Write concrete success criteria (not vague goals)\n4. Determine the test command (look at existing test patterns in the codebase)\n5. Map dependencies between tasks\n\n**This validation ensures the plan is ready for `\u002Fworkflows-work`'s subagent orchestration model**, where each task is delegated to a focused subagent with clear scope and termination criteria.\n\n### 1.5 Re-fetch Source Documents (if available)\n\nCheck the plan's YAML frontmatter for `source_docs:`. If present, re-fetch the original documents for deeper analysis:\n\n**For each source doc URL in `source_docs.tickets`, `source_docs.docs`, `source_docs.figma`:**\n\nLaunch parallel subagents to re-read the full documents (not just summaries this time):\n\n```\nTask general-purpose: \"Re-read this source document in full detail for plan deepening.\n\nURL: [url]\nType: [tickets|docs|figma]\n\nFetch the complete document content using this strategy:\n1. Try ToolSearch to find any relevant MCP tools available\n2. If MCP tools found, use them to fetch the document\n3. If no MCP tools, try WebFetch on the URL\n4. Last resort: output 'MANUAL_INPUT_NEEDED: Could not access [url]. Ask user to paste content.'\n\nFocus on extracting:\n- Detailed acceptance criteria and edge cases\n- Technical constraints not captured in the summary\n- Dependencies and integration points\n- Any updates since the plan was created (check timestamps)\n\nReturn the FULL content, not a summary. This will be used to ground the plan in source-of-truth documents.\"\n```\n\nFeed the full document contents to all subsequent deepening agents as additional context.\n\n### 2. Discover and Apply Available Skills\n\n\u003Cthinking>\nDynamically discover all available skills and match them to plan sections. Don't assume what skills exist - discover them at runtime.\n\u003C\u002Fthinking>\n\n**Step 1: Discover ALL available skills from ALL sources**\n\n```bash\n# 1. Project-local skills (highest priority - project-specific)\nls .github\u002Fskills\u002F\n\n# 2. User's global skills (~\u002F.copilot\u002F)\nls ~\u002F.copilot\u002Fskills\u002F\n\n# 3. compound-engineering plugin skills\nls ~\u002F.copilot\u002Fplugins\u002Fcache\u002F*\u002Fcompound-engineering\u002F*\u002Fskills\u002F\n\n# 4. ALL other installed plugins - check every plugin for skills\nfind ~\u002F.copilot\u002Fplugins\u002Fcache -type d -name \"skills\" 2>\u002Fdev\u002Fnull\n\n# 5. Also check installed_plugins.json for all plugin locations\ncat ~\u002F.copilot\u002Fplugins\u002Finstalled_plugins.json\n```\n\n**Important:** Check EVERY source. Don't assume compound-engineering is the only plugin. Use skills from ANY installed plugin that's relevant.\n\n**Step 2: For each discovered skill, read its SKILL.md to understand what it does**\n\n```bash\n# For each skill directory found, read its documentation\ncat [skill-path]\u002Fskill.md\n```\n\n**Step 3: Match skills to plan content**\n\nFor each skill discovered:\n- Read its SKILL.md description\n- Check if any plan sections match the skill's domain\n- If there's a match, spawn a sub-agent to apply that skill's knowledge\n\n**Step 4: Spawn a sub-agent for EVERY matched skill**\n\n**CRITICAL: For EACH skill that matches, spawn a separate sub-agent and instruct it to USE that skill.**\n\nFor each matched skill:\n```\nTask general-purpose: \"You have the [skill-name] skill available at [skill-path].\n\nYOUR JOB: Use this skill on the plan.\n\n1. Read the skill: cat [skill-path]\u002Fskill.md\n2. Follow the skill's instructions exactly\n3. Apply the skill to this content:\n\n[relevant plan section or full plan]\n\n4. Return the skill's full output\n\nThe skill tells you what to do - follow it. Execute the skill completely.\"\n```\n\n**Spawn ALL skill sub-agents in PARALLEL:**\n- 1 sub-agent per matched skill\n- Each sub-agent reads and uses its assigned skill\n- All run simultaneously\n- 10, 20, 30 skill sub-agents is fine\n\n**Each sub-agent:**\n1. Reads its skill's SKILL.md\n2. Follows the skill's workflow\u002Finstructions\n3. Applies the skill to the plan\n4. Returns whatever the skill produces (code, recommendations, patterns, reviews, etc.)\n\n**Example spawns:**\n```\nTask general-purpose: \"Use the laravel-conventions skill at ~\u002F.copilot\u002Fplugins\u002F...\u002Flaravel-conventions. Read SKILL.md and apply it to: [Laravel sections of plan]\"\n\nTask general-purpose: \"Use the frontend-design skill at ~\u002F.copilot\u002Fplugins\u002F...\u002Ffrontend-design. Read SKILL.md and apply it to: [UI sections of plan]\"\n\nTask general-purpose: \"Use the agent-native-architecture skill at ~\u002F.copilot\u002Fplugins\u002F...\u002Fagent-native-architecture. Read SKILL.md and apply it to: [agent\u002Ftool sections of plan]\"\n\nTask general-purpose: \"Use the security-patterns skill at ~\u002F.copilot\u002Fskills\u002Fsecurity-patterns. Read SKILL.md and apply it to: [full plan]\"\n```\n\n**No limit on skill sub-agents. Spawn one for every skill that could possibly be relevant.**\n\n### 3. Discover and Apply Learnings\u002FSolutions\n\n\u003Cthinking>\nCheck for documented learnings from \u002Fworkflows-compound. These are solved problems stored as markdown files. Spawn a sub-agent for each learning to check if it's relevant.\n\u003C\u002Fthinking>\n\n**LEARNINGS LOCATION - Check these exact folders:**\n\n```\ndocs\u002Fsolutions\u002F           \u003C-- PRIMARY: Project-level learnings (created by \u002Fworkflows-compound)\n├── performance-issues\u002F\n│   └── *.md\n├── debugging-patterns\u002F\n│   └── *.md\n├── configuration-fixes\u002F\n│   └── *.md\n├── integration-issues\u002F\n│   └── *.md\n├── deployment-issues\u002F\n│   └── *.md\n└── [other-categories]\u002F\n    └── *.md\n```\n\n**Step 1: Find ALL learning markdown files**\n\nRun these commands to get every learning file:\n\n```bash\n# PRIMARY LOCATION - Project learnings\nfind docs\u002Fsolutions -name \"*.md\" -type f 2>\u002Fdev\u002Fnull\n\n# If docs\u002Fsolutions doesn't exist, check alternate locations:\nfind .github\u002Fdocs -name \"*.md\" -type f 2>\u002Fdev\u002Fnull\nfind ~\u002F.copilot\u002Fdocs -name \"*.md\" -type f 2>\u002Fdev\u002Fnull\n```\n\n**Step 2: Read frontmatter of each learning to filter**\n\nEach learning file has YAML frontmatter with metadata. Read the first ~20 lines of each file to get:\n\n```yaml\n---\ntitle: \"N+1 Query Fix for Briefs\"\ncategory: performance-issues\ntags: [eloquent, n-plus-one, eager-loading, with]\nmodule: Briefs\nsymptom: \"Slow page load, multiple queries in logs\"\nroot_cause: \"Missing eager loading with() on relationship\"\n---\n```\n\n**For each .md file, quickly scan its frontmatter:**\n\n```bash\n# Read first 20 lines of each learning (frontmatter + summary)\nhead -20 docs\u002Fsolutions\u002F**\u002F*.md\n```\n\n**Step 3: Filter - only spawn sub-agents for LIKELY relevant learnings**\n\nCompare each learning's frontmatter against the plan:\n- `tags:` - Do any tags match technologies\u002Fpatterns in the plan?\n- `category:` - Is this category relevant? (e.g., skip deployment-issues if plan is UI-only)\n- `module:` - Does the plan touch this module?\n- `symptom:` \u002F `root_cause:` - Could this problem occur with the plan?\n\n**SKIP learnings that are clearly not applicable:**\n- Plan is frontend-only → skip `database-migrations\u002F` learnings\n- Plan is Python → skip `laravel-specific\u002F` learnings\n- Plan has no auth → skip `authentication-issues\u002F` learnings\n\n**SPAWN sub-agents for learnings that MIGHT apply:**\n- Any tag overlap with plan technologies\n- Same category as plan domain\n- Similar patterns or concerns\n\n**Step 4: Spawn sub-agents for filtered learnings**\n\nFor each learning that passes the filter:\n\n```\nTask general-purpose: \"\nLEARNING FILE: [full path to .md file]\n\n1. Read this learning file completely\n2. This learning documents a previously solved problem\n\nCheck if this learning applies to this plan:\n\n---\n[full plan content]\n---\n\nIf relevant:\n- Explain specifically how it applies\n- Quote the key insight or solution\n- Suggest where\u002Fhow to incorporate it\n\nIf NOT relevant after deeper analysis:\n- Say 'Not applicable: [reason]'\n\"\n```\n\n**Example filtering:**\n```\n# Found 15 learning files, plan is about \"Laravel API caching\"\n\n# SPAWN (likely relevant):\ndocs\u002Fsolutions\u002Fperformance-issues\u002Fn-plus-one-queries.md      # tags: [eloquent] ✓\ndocs\u002Fsolutions\u002Fperformance-issues\u002Fredis-cache-stampede.md    # tags: [caching, redis] ✓\ndocs\u002Fsolutions\u002Fconfiguration-fixes\u002Fredis-connection-pool.md  # tags: [redis] ✓\n\n# SKIP (clearly not applicable):\ndocs\u002Fsolutions\u002Fdeployment-issues\u002Fheroku-memory-quota.md      # not about caching\ndocs\u002Fsolutions\u002Ffrontend-issues\u002Fvue-reactivity-issue.md       # plan is API, not frontend\ndocs\u002Fsolutions\u002Fauthentication-issues\u002Fjwt-expiry.md           # plan has no auth\n```\n\n**Spawn sub-agents in PARALLEL for all filtered learnings.**\n\n**These learnings are institutional knowledge - applying them prevents repeating past mistakes.**\n\n### 4. Launch Per-Section Research Agents\n\n\u003Cthinking>\nFor each major section in the plan, spawn dedicated sub-agents to research improvements. Use the Explore agent type for open-ended research.\n\u003C\u002Fthinking>\n\n**For each identified section, launch parallel research:**\n\n```\nTask Explore: \"Research best practices, patterns, and real-world examples for: [section topic].\nFind:\n- Industry standards and conventions\n- Performance considerations\n- Common pitfalls and how to avoid them\n- Documentation and tutorials\nReturn concrete, actionable recommendations.\"\n```\n\n**Also use Context7 MCP for framework documentation:**\n\nFor any technologies\u002Fframeworks mentioned in the plan, query Context7:\n```\nmcp__plugin_compound-engineering_context7__resolve-library-id: Find library ID for [framework]\nmcp__plugin_compound-engineering_context7__query-docs: Query documentation for specific patterns\n```\n\n**Use WebSearch for current best practices:**\n\nSearch for recent (2024-2026) articles, blog posts, and documentation on topics in the plan.\n\n### 5. Discover and Run ALL Review Agents\n\n\u003Cthinking>\nDynamically discover every available agent and run them ALL against the plan. Don't filter, don't skip, don't assume relevance. 40+ parallel agents is fine. Use everything available.\n\u003C\u002Fthinking>\n\n**Step 1: Discover ALL available agents from ALL sources**\n\n```bash\n# 1. Project-local agents (highest priority - project-specific)\nfind .github\u002Fagents -name \"*.md\" 2>\u002Fdev\u002Fnull\n\n# 2. User's global agents (~\u002F.copilot\u002F)\nfind ~\u002F.copilot\u002Fagents -name \"*.md\" 2>\u002Fdev\u002Fnull\n\n# 3. compound-engineering plugin agents (all subdirectories)\nfind ~\u002F.copilot\u002Fplugins\u002Fcache\u002F*\u002Fcompound-engineering\u002F*\u002Fagents -name \"*.md\" 2>\u002Fdev\u002Fnull\n\n# 4. ALL other installed plugins - check every plugin for agents\nfind ~\u002F.copilot\u002Fplugins\u002Fcache -path \"*\u002Fagents\u002F*.md\" 2>\u002Fdev\u002Fnull\n\n# 5. Check installed_plugins.json to find all plugin locations\ncat ~\u002F.copilot\u002Fplugins\u002Finstalled_plugins.json\n\n# 6. For local plugins (isLocal: true), check their source directories\n# Parse installed_plugins.json and find local plugin paths\n```\n\n**Important:** Check EVERY source. Include agents from:\n- Project `.github\u002Fagents\u002F`\n- User's `~\u002F.copilot\u002Fagents\u002F`\n- compound-engineering plugin (but SKIP workflow\u002F agents - only use review\u002F, research\u002F, design\u002F, docs\u002F)\n- ALL other installed plugins (agent-sdk-dev, frontend-design, etc.)\n- Any local plugins\n\n**For compound-engineering plugin specifically:**\n- USE: `agents\u002Freview\u002F*` (all reviewers)\n- USE: `agents\u002Fresearch\u002F*` (all researchers)\n- USE: `agents\u002Fdesign\u002F*` (design agents)\n- USE: `agents\u002Fdocs\u002F*` (documentation agents)\n- SKIP: `agents\u002Fworkflow\u002F*` (these are workflow orchestrators, not reviewers)\n\n**Step 2: For each discovered agent, read its description**\n\nRead the first few lines of each agent file to understand what it reviews\u002Fanalyzes.\n\n**Step 3: Launch ALL agents in parallel**\n\nFor EVERY agent discovered, launch a Task in parallel:\n\n```\nTask [agent-name]: \"Review this plan using your expertise. Apply all your checks and patterns. Plan content: [full plan content]\"\n```\n\n**CRITICAL RULES:**\n- Do NOT filter agents by \"relevance\" - run them ALL\n- Do NOT skip agents because they \"might not apply\" - let them decide\n- Launch ALL agents in a SINGLE message with multiple Task tool calls\n- 20, 30, 40 parallel agents is fine - use everything\n- Each agent may catch something others miss\n- The goal is MAXIMUM coverage, not efficiency\n\n**Step 4: Also discover and run research agents**\n\nResearch agents (like `best-practices-researcher`, `framework-docs-researcher`, `git-history-analyzer`, `repo-research-analyst`) should also be run for relevant plan sections.\n\n### 6. Wait for ALL Agents and Synthesize Everything\n\n\u003Cthinking>\nWait for ALL parallel agents to complete - skills, research agents, review agents, everything. Then synthesize all findings into a comprehensive enhancement.\n\u003C\u002Fthinking>\n\n**Collect outputs from ALL sources:**\n\n1. **Skill-based sub-agents** - Each skill's full output (code examples, patterns, recommendations)\n2. **Learnings\u002FSolutions sub-agents** - Relevant documented learnings from \u002Fworkflows-compound\n3. **Research agents** - Best practices, documentation, real-world examples\n4. **Review agents** - All feedback from every reviewer (architecture, security, performance, simplicity, etc.)\n5. **Context7 queries** - Framework documentation and patterns\n6. **Web searches** - Current best practices and articles\n\n**For each agent's findings, extract:**\n- [ ] Concrete recommendations (actionable items)\n- [ ] Code patterns and examples (copy-paste ready)\n- [ ] Anti-patterns to avoid (warnings)\n- [ ] Performance considerations (metrics, benchmarks)\n- [ ] Security considerations (vulnerabilities, mitigations)\n- [ ] Edge cases discovered (handling strategies)\n- [ ] Documentation links (references)\n- [ ] Skill-specific patterns (from matched skills)\n- [ ] Relevant learnings (past solutions that apply - prevent repeating mistakes)\n\n**Deduplicate and prioritize:**\n- Merge similar recommendations from multiple agents\n- Prioritize by impact (high-value improvements first)\n- Flag conflicting advice for human review\n- Group by plan section\n\n### 7. Enhance Plan Sections\n\n\u003Cthinking>\nMerge research findings back into the plan, adding depth without changing the original structure.\n\u003C\u002Fthinking>\n\n**Enhancement format for each section:**\n\n```markdown\n## [Original Section Title]\n\n[Original content preserved]\n\n### Research Insights\n\n**Best Practices:**\n- [Concrete recommendation 1]\n- [Concrete recommendation 2]\n\n**Performance Considerations:**\n- [Optimization opportunity]\n- [Benchmark or metric to target]\n\n**Implementation Details:**\n```[language]\n\u002F\u002F Concrete code example from research\n```\n\n**Edge Cases:**\n- [Edge case 1 and how to handle]\n- [Edge case 2 and how to handle]\n\n**References:**\n- [Documentation URL 1]\n- [Documentation URL 2]\n```\n\n### 8. Add Enhancement Summary\n\nAt the top of the plan, add a summary section:\n\n```markdown\n## Enhancement Summary\n\n**Deepened on:** [Date]\n**Sections enhanced:** [Count]\n**Research agents used:** [List]\n\n### Key Improvements\n1. [Major improvement 1]\n2. [Major improvement 2]\n3. [Major improvement 3]\n\n### New Considerations Discovered\n- [Important finding 1]\n- [Important finding 2]\n```\n\n### 9. Update Plan File\n\n**Write the enhanced plan:**\n- Preserve original filename\n- Add `-deepened` suffix if user prefers a new file\n- Update any timestamps or metadata\n\n## Output Format\n\nUpdate the plan file in place (or if user requests a separate file, append `-deepened` after `-plan`, e.g., `2026-01-15-feat-auth-plan-deepened.md`).\n\n## Quality Checks\n\nBefore finalizing:\n- [ ] All original content preserved\n- [ ] Research insights clearly marked and attributed\n- [ ] Code examples are syntactically correct\n- [ ] Links are valid and relevant\n- [ ] No contradictions between sections\n- [ ] Enhancement summary accurately reflects changes\n- [ ] Implementation tasks have execution-ready structure (files, success criteria, test commands, dependencies)\n\n## Post-Enhancement Options\n\nAfter writing the enhanced plan, use the **AskUserQuestion tool** to present these options:\n\n**Question:** \"Plan deepened at `[plan_path]`. What would you like to do next?\"\n\n**Options:**\n1. **View diff** - Show what was added\u002Fchanged\n2. **Run `\u002Ftechnical_review`** - Get feedback from reviewers on enhanced plan\n3. **Start `\u002Fworkflows-work`** - Begin implementing this enhanced plan\n4. **Deepen further** - Run another round of research on specific sections\n5. **Revert** - Restore original plan (if backup exists)\n\nBased on selection:\n- **View diff** → Run `git diff [plan_path]` or show before\u002Fafter\n- **`\u002Ftechnical_review`** → Call the \u002Ftechnical_review command with the plan file path\n- **`\u002Fworkflows-work`** → Call the \u002Fworkflows-work command with the plan file path\n- **Deepen further** → Ask which sections need more research, then re-run those agents\n- **Revert** → Restore from git or backup\n\n## Example Enhancement\n\n**Before (from \u002Fworkflows-plan):**\n```markdown\n## Technical Approach\n\nUse React Query for data fetching with optimistic updates.\n```\n\n**After (from \u002Fworkflows-deepen-plan):**\n```markdown\n## Technical Approach\n\nUse React Query for data fetching with optimistic updates.\n\n### Research Insights\n\n**Best Practices:**\n- Configure `staleTime` and `cacheTime` based on data freshness requirements\n- Use `queryKey` factories for consistent cache invalidation\n- Implement error boundaries around query-dependent components\n\n**Performance Considerations:**\n- Enable `refetchOnWindowFocus: false` for stable data to reduce unnecessary requests\n- Use `select` option to transform and memoize data at query level\n- Consider `placeholderData` for instant perceived loading\n\n**Implementation Details:**\n```typescript\n\u002F\u002F Recommended query configuration\nconst queryClient = new QueryClient({\n  defaultOptions: {\n    queries: {\n      staleTime: 5 * 60 * 1000, \u002F\u002F 5 minutes\n      retry: 2,\n      refetchOnWindowFocus: false,\n    },\n  },\n});\n```\n\n**Edge Cases:**\n- Handle race conditions with `cancelQueries` on component unmount\n- Implement retry logic for transient network failures\n- Consider offline support with `persistQueryClient`\n\n**References:**\n- https:\u002F\u002Ftanstack.com\u002Fquery\u002Flatest\u002Fdocs\u002Freact\u002Fguides\u002Foptimistic-updates\n- https:\u002F\u002Ftkdodo.eu\u002Fblog\u002Fpractical-react-query\n```\n\nNEVER CODE! Just research and enhance the plan.\n","## Arguments\n[path to plan file]\n\n# Deepen Plan - Power Enhancement Mode\n\n## Introduction\n\n**Note: The current year is 2026.** Use this when searching for recent documentation and best practices.\n\nThis command takes an existing plan (from `\u002Fworkflows-plan`) and enhances each section with parallel research agents. Each major element gets its own dedicated research sub-agent to find:\n- Best practices and industry patterns\n- Performance optimizations\n- UI\u002FUX improvements (if applicable)\n- Quality enhancements and edge cases\n- Real-world implementation examples\n\nThe result is a deeply grounded, production-ready plan with concrete implementation details.\n\n## Plan File\n\n\u003Cplan_path> #$ARGUMENTS \u003C\u002Fplan_path>\n\n**If the plan path above is empty:**\n1. Check for recent plans: `ls -la docs\u002Fplans\u002F`\n2. Ask the user: \"Which plan would you like to deepen? Please provide the path (e.g., `docs\u002Fplans\u002F2026-01-15-feat-my-feature-plan.md`).\"\n\nDo not proceed until you have a valid plan file path.\n\n## Main Tasks\n\n### 1. Parse and Analyze Plan Structure\n\n\u003Cthinking>\nFirst, read and parse the plan to identify each major section that can be enhanced with research.\n\u003C\u002Fthinking>\n\n**Read the plan file and extract:**\n- [ ] Overview\u002FProblem Statement\n- [ ] Proposed Solution sections\n- [ ] Technical Approach\u002FArchitecture\n- [ ] Implementation phases\u002Fsteps\n- [ ] Code examples and file references\n- [ ] Acceptance criteria\n- [ ] Any UI\u002FUX components mentioned\n- [ ] Technologies\u002Fframeworks mentioned (Laravel, Vue.js, Nuxt, Python, TypeScript, etc.)\n- [ ] Domain areas (data models, APIs, UI, security, performance, etc.)\n\n**Create a section manifest:**\n```\nSection 1: [Title] - [Brief description of what to research]\nSection 2: [Title] - [Brief description of what to research]\n...\n```\n\n### 1.1 Validate Execution Readiness\n\n\u003Cthinking>\nCheck if the plan has sufficiently structured execution chunks for the subagent orchestration model in \u002Fworkflows-work. Plans need per-task success criteria, test commands, and file lists.\n\u003C\u002Fthinking>\n\n**Scan each implementation task\u002Fphase for these required fields:**\n\n- [ ] **Files:** List of files to create or modify\n- [ ] **Depends on:** Dependencies on other tasks\n- [ ] **Success criteria:** Testable checkboxes defining \"done\"\n- [ ] **Test command:** Exact command to verify completion\n\n**Expected task format:**\n\n```markdown\n##### Task N.1: [Task Name]\n**Files:** `path\u002Fto\u002Ffile1.php`, `path\u002Fto\u002Ffile2.php`\n**Depends on:** Task N-1.2 (or \"None\")\n**Success criteria:**\n- [ ] Criterion 1\n- [ ] Criterion 2\n**Test command:** `command to run`\n```\n\n**Scoring:**\n\nCount how many implementation tasks have all four fields. Report:\n\n```\nExecution Readiness: X\u002FY tasks have complete structure (Z%)\n```\n\n**Actions based on score:**\n\n| Score | Action |\n|-------|--------|\n| 80-100% | Plan is execution-ready. Proceed with deepening. |\n| 50-79% | Flag incomplete tasks. During deepening, add missing fields. |\n| 0-49% | Plan needs significant restructuring. Add an \"Execution Readiness\" enhancement pass that decomposes vague phases into structured tasks with all required fields. **Note:** `\u002Fworkflows-work` will refuse to execute plans that lack this structure. |\n\n**For tasks missing structure, the deepening process should:**\n\n1. Break vague phases into specific, scoped tasks\n2. Identify which files each task will create or modify\n3. Write concrete success criteria (not vague goals)\n4. Determine the test command (look at existing test patterns in the codebase)\n5. Map dependencies between tasks\n\n**This validation ensures the plan is ready for `\u002Fworkflows-work`'s subagent orchestration model**, where each task is delegated to a focused subagent with clear scope and termination criteria.\n\n### 1.5 Re-fetch Source Documents (if available)\n\nCheck the plan's YAML frontmatter for `source_docs:`. If present, re-fetch the original documents for deeper analysis:\n\n**For each source doc URL in `source_docs.tickets`, `source_docs.docs`, `source_docs.figma`:**\n\nLaunch parallel subagents to re-read the full documents (not just summaries this time):\n\n```\nTask general-purpose: \"Re-read this source document in full detail for plan deepening.\n\nURL: [url]\nType: [tickets|docs|figma]\n\nFetch the complete document content using this strategy:\n1. Try ToolSearch to find any relevant MCP tools available\n2. If MCP tools found, use them to fetch the document\n3. If no MCP tools, try WebFetch on the URL\n4. Last resort: output 'MANUAL_INPUT_NEEDED: Could not access [url]. Ask user to paste content.'\n\nFocus on extracting:\n- Detailed acceptance criteria and edge cases\n- Technical constraints not captured in the summary\n- Dependencies and integration points\n- Any updates since the plan was created (check timestamps)\n\nReturn the FULL content, not a summary. This will be used to ground the plan in source-of-truth documents.\"\n```\n\nFeed the full document contents to all subsequent deepening agents as additional context.\n\n### 2. Discover and Apply Available Skills\n\n\u003Cthinking>\nDynamically discover all available skills and match them to plan sections. Don't assume what skills exist - discover them at runtime.\n\u003C\u002Fthinking>\n\n**Step 1: Discover ALL available skills from ALL sources**\n\n```bash\n# 1. Project-local skills (highest priority - project-specific)\nls .github\u002Fskills\u002F\n\n# 2. User's global skills (~\u002F.copilot\u002F)\nls ~\u002F.copilot\u002Fskills\u002F\n\n# 3. compound-engineering plugin skills\nls ~\u002F.copilot\u002Fplugins\u002Fcache\u002F*\u002Fcompound-engineering\u002F*\u002Fskills\u002F\n\n# 4. ALL other installed plugins - check every plugin for skills\nfind ~\u002F.copilot\u002Fplugins\u002Fcache -type d -name \"skills\" 2>\u002Fdev\u002Fnull\n\n# 5. Also check installed_plugins.json for all plugin locations\ncat ~\u002F.copilot\u002Fplugins\u002Finstalled_plugins.json\n```\n\n**Important:** Check EVERY source. Don't assume compound-engineering is the only plugin. Use skills from ANY installed plugin that's relevant.\n\n**Step 2: For each discovered skill, read its SKILL.md to understand what it does**\n\n```bash\n# For each skill directory found, read its documentation\ncat [skill-path]\u002Fskill.md\n```\n\n**Step 3: Match skills to plan content**\n\nFor each skill discovered:\n- Read its SKILL.md description\n- Check if any plan sections match the skill's domain\n- If there's a match, spawn a sub-agent to apply that skill's knowledge\n\n**Step 4: Spawn a sub-agent for EVERY matched skill**\n\n**CRITICAL: For EACH skill that matches, spawn a separate sub-agent and instruct it to USE that skill.**\n\nFor each matched skill:\n```\nTask general-purpose: \"You have the [skill-name] skill available at [skill-path].\n\nYOUR JOB: Use this skill on the plan.\n\n1. Read the skill: cat [skill-path]\u002Fskill.md\n2. Follow the skill's instructions exactly\n3. Apply the skill to this content:\n\n[relevant plan section or full plan]\n\n4. Return the skill's full output\n\nThe skill tells you what to do - follow it. Execute the skill completely.\"\n```\n\n**Spawn ALL skill sub-agents in PARALLEL:**\n- 1 sub-agent per matched skill\n- Each sub-agent reads and uses its assigned skill\n- All run simultaneously\n- 10, 20, 30 skill sub-agents is fine\n\n**Each sub-agent:**\n1. Reads its skill's SKILL.md\n2. Follows the skill's workflow\u002Finstructions\n3. Applies the skill to the plan\n4. Returns whatever the skill produces (code, recommendations, patterns, reviews, etc.)\n\n**Example spawns:**\n```\nTask general-purpose: \"Use the laravel-conventions skill at ~\u002F.copilot\u002Fplugins\u002F...\u002Flaravel-conventions. Read SKILL.md and apply it to: [Laravel sections of plan]\"\n\nTask general-purpose: \"Use the frontend-design skill at ~\u002F.copilot\u002Fplugins\u002F...\u002Ffrontend-design. Read SKILL.md and apply it to: [UI sections of plan]\"\n\nTask general-purpose: \"Use the agent-native-architecture skill at ~\u002F.copilot\u002Fplugins\u002F...\u002Fagent-native-architecture. Read SKILL.md and apply it to: [agent\u002Ftool sections of plan]\"\n\nTask general-purpose: \"Use the security-patterns skill at ~\u002F.copilot\u002Fskills\u002Fsecurity-patterns. Read SKILL.md and apply it to: [full plan]\"\n```\n\n**No limit on skill sub-agents. Spawn one for every skill that could possibly be relevant.**\n\n### 3. Discover and Apply Learnings\u002FSolutions\n\n\u003Cthinking>\nCheck for documented learnings from \u002Fworkflows-compound. These are solved problems stored as markdown files. Spawn a sub-agent for each learning to check if it's relevant.\n\u003C\u002Fthinking>\n\n**LEARNINGS LOCATION - Check these exact folders:**\n\n```\ndocs\u002Fsolutions\u002F           \u003C-- PRIMARY: Project-level learnings (created by \u002Fworkflows-compound)\n├── performance-issues\u002F\n│   └── *.md\n├── debugging-patterns\u002F\n│   └── *.md\n├── configuration-fixes\u002F\n│   └── *.md\n├── integration-issues\u002F\n│   └── *.md\n├── deployment-issues\u002F\n│   └── *.md\n└── [other-categories]\u002F\n    └── *.md\n```\n\n**Step 1: Find ALL learning markdown files**\n\nRun these commands to get every learning file:\n\n```bash\n# PRIMARY LOCATION - Project learnings\nfind docs\u002Fsolutions -name \"*.md\" -type f 2>\u002Fdev\u002Fnull\n\n# If docs\u002Fsolutions doesn't exist, check alternate locations:\nfind .github\u002Fdocs -name \"*.md\" -type f 2>\u002Fdev\u002Fnull\nfind ~\u002F.copilot\u002Fdocs -name \"*.md\" -type f 2>\u002Fdev\u002Fnull\n```\n\n**Step 2: Read frontmatter of each learning to filter**\n\nEach learning file has YAML frontmatter with metadata. Read the first ~20 lines of each file to get:\n\n```yaml\n---\ntitle: \"N+1 Query Fix for Briefs\"\ncategory: performance-issues\ntags: [eloquent, n-plus-one, eager-loading, with]\nmodule: Briefs\nsymptom: \"Slow page load, multiple queries in logs\"\nroot_cause: \"Missing eager loading with() on relationship\"\n---\n```\n\n**For each .md file, quickly scan its frontmatter:**\n\n```bash\n# Read first 20 lines of each learning (frontmatter + summary)\nhead -20 docs\u002Fsolutions\u002F**\u002F*.md\n```\n\n**Step 3: Filter - only spawn sub-agents for LIKELY relevant learnings**\n\nCompare each learning's frontmatter against the plan:\n- `tags:` - Do any tags match technologies\u002Fpatterns in the plan?\n- `category:` - Is this category relevant? (e.g., skip deployment-issues if plan is UI-only)\n- `module:` - Does the plan touch this module?\n- `symptom:` \u002F `root_cause:` - Could this problem occur with the plan?\n\n**SKIP learnings that are clearly not applicable:**\n- Plan is frontend-only → skip `database-migrations\u002F` learnings\n- Plan is Python → skip `laravel-specific\u002F` learnings\n- Plan has no auth → skip `authentication-issues\u002F` learnings\n\n**SPAWN sub-agents for learnings that MIGHT apply:**\n- Any tag overlap with plan technologies\n- Same category as plan domain\n- Similar patterns or concerns\n\n**Step 4: Spawn sub-agents for filtered learnings**\n\nFor each learning that passes the filter:\n\n```\nTask general-purpose: \"\nLEARNING FILE: [full path to .md file]\n\n1. Read this learning file completely\n2. This learning documents a previously solved problem\n\nCheck if this learning applies to this plan:\n\n---\n[full plan content]\n---\n\nIf relevant:\n- Explain specifically how it applies\n- Quote the key insight or solution\n- Suggest where\u002Fhow to incorporate it\n\nIf NOT relevant after deeper analysis:\n- Say 'Not applicable: [reason]'\n\"\n```\n\n**Example filtering:**\n```\n# Found 15 learning files, plan is about \"Laravel API caching\"\n\n# SPAWN (likely relevant):\ndocs\u002Fsolutions\u002Fperformance-issues\u002Fn-plus-one-queries.md      # tags: [eloquent] ✓\ndocs\u002Fsolutions\u002Fperformance-issues\u002Fredis-cache-stampede.md    # tags: [caching, redis] ✓\ndocs\u002Fsolutions\u002Fconfiguration-fixes\u002Fredis-connection-pool.md  # tags: [redis] ✓\n\n# SKIP (clearly not applicable):\ndocs\u002Fsolutions\u002Fdeployment-issues\u002Fheroku-memory-quota.md      # not about caching\ndocs\u002Fsolutions\u002Ffrontend-issues\u002Fvue-reactivity-issue.md       # plan is API, not frontend\ndocs\u002Fsolutions\u002Fauthentication-issues\u002Fjwt-expiry.md           # plan has no auth\n```\n\n**Spawn sub-agents in PARALLEL for all filtered learnings.**\n\n**These learnings are institutional knowledge - applying them prevents repeating past mistakes.**\n\n### 4. Launch Per-Section Research Agents\n\n\u003Cthinking>\nFor each major section in the plan, spawn dedicated sub-agents to research improvements. Use the Explore agent type for open-ended research.\n\u003C\u002Fthinking>\n\n**For each identified section, launch parallel research:**\n\n```\nTask Explore: \"Research best practices, patterns, and real-world examples for: [section topic].\nFind:\n- Industry standards and conventions\n- Performance considerations\n- Common pitfalls and how to avoid them\n- Documentation and tutorials\nReturn concrete, actionable recommendations.\"\n```\n\n**Also use Context7 MCP for framework documentation:**\n\nFor any technologies\u002Fframeworks mentioned in the plan, query Context7:\n```\nmcp__plugin_compound-engineering_context7__resolve-library-id: Find library ID for [framework]\nmcp__plugin_compound-engineering_context7__query-docs: Query documentation for specific patterns\n```\n\n**Use WebSearch for current best practices:**\n\nSearch for recent (2024-2026) articles, blog posts, and documentation on topics in the plan.\n\n### 5. Discover and Run ALL Review Agents\n\n\u003Cthinking>\nDynamically discover every available agent and run them ALL against the plan. Don't filter, don't skip, don't assume relevance. 40+ parallel agents is fine. Use everything available.\n\u003C\u002Fthinking>\n\n**Step 1: Discover ALL available agents from ALL sources**\n\n```bash\n# 1. Project-local agents (highest priority - project-specific)\nfind .github\u002Fagents -name \"*.md\" 2>\u002Fdev\u002Fnull\n\n# 2. User's global agents (~\u002F.copilot\u002F)\nfind ~\u002F.copilot\u002Fagents -name \"*.md\" 2>\u002Fdev\u002Fnull\n\n# 3. compound-engineering plugin agents (all subdirectories)\nfind ~\u002F.copilot\u002Fplugins\u002Fcache\u002F*\u002Fcompound-engineering\u002F*\u002Fagents -name \"*.md\" 2>\u002Fdev\u002Fnull\n\n# 4. ALL other installed plugins - check every plugin for agents\nfind ~\u002F.copilot\u002Fplugins\u002Fcache -path \"*\u002Fagents\u002F*.md\" 2>\u002Fdev\u002Fnull\n\n# 5. Check installed_plugins.json to find all plugin locations\ncat ~\u002F.copilot\u002Fplugins\u002Finstalled_plugins.json\n\n# 6. For local plugins (isLocal: true), check their source directories\n# Parse installed_plugins.json and find local plugin paths\n```\n\n**Important:** Check EVERY source. Include agents from:\n- Project `.github\u002Fagents\u002F`\n- User's `~\u002F.copilot\u002Fagents\u002F`\n- compound-engineering plugin (but SKIP workflow\u002F agents - only use review\u002F, research\u002F, design\u002F, docs\u002F)\n- ALL other installed plugins (agent-sdk-dev, frontend-design, etc.)\n- Any local plugins\n\n**For compound-engineering plugin specifically:**\n- USE: `agents\u002Freview\u002F*` (all reviewers)\n- USE: `agents\u002Fresearch\u002F*` (all researchers)\n- USE: `agents\u002Fdesign\u002F*` (design agents)\n- USE: `agents\u002Fdocs\u002F*` (documentation agents)\n- SKIP: `agents\u002Fworkflow\u002F*` (these are workflow orchestrators, not reviewers)\n\n**Step 2: For each discovered agent, read its description**\n\nRead the first few lines of each agent file to understand what it reviews\u002Fanalyzes.\n\n**Step 3: Launch ALL agents in parallel**\n\nFor EVERY agent discovered, launch a Task in parallel:\n\n```\nTask [agent-name]: \"Review this plan using your expertise. Apply all your checks and patterns. Plan content: [full plan content]\"\n```\n\n**CRITICAL RULES:**\n- Do NOT filter agents by \"relevance\" - run them ALL\n- Do NOT skip agents because they \"might not apply\" - let them decide\n- Launch ALL agents in a SINGLE message with multiple Task tool calls\n- 20, 30, 40 parallel agents is fine - use everything\n- Each agent may catch something others miss\n- The goal is MAXIMUM coverage, not efficiency\n\n**Step 4: Also discover and run research agents**\n\nResearch agents (like `best-practices-researcher`, `framework-docs-researcher`, `git-history-analyzer`, `repo-research-analyst`) should also be run for relevant plan sections.\n\n### 6. Wait for ALL Agents and Synthesize Everything\n\n\u003Cthinking>\nWait for ALL parallel agents to complete - skills, research agents, review agents, everything. Then synthesize all findings into a comprehensive enhancement.\n\u003C\u002Fthinking>\n\n**Collect outputs from ALL sources:**\n\n1. **Skill-based sub-agents** - Each skill's full output (code examples, patterns, recommendations)\n2. **Learnings\u002FSolutions sub-agents** - Relevant documented learnings from \u002Fworkflows-compound\n3. **Research agents** - Best practices, documentation, real-world examples\n4. **Review agents** - All feedback from every reviewer (architecture, security, performance, simplicity, etc.)\n5. **Context7 queries** - Framework documentation and patterns\n6. **Web searches** - Current best practices and articles\n\n**For each agent's findings, extract:**\n- [ ] Concrete recommendations (actionable items)\n- [ ] Code patterns and examples (copy-paste ready)\n- [ ] Anti-patterns to avoid (warnings)\n- [ ] Performance considerations (metrics, benchmarks)\n- [ ] Security considerations (vulnerabilities, mitigations)\n- [ ] Edge cases discovered (handling strategies)\n- [ ] Documentation links (references)\n- [ ] Skill-specific patterns (from matched skills)\n- [ ] Relevant learnings (past solutions that apply - prevent repeating mistakes)\n\n**Deduplicate and prioritize:**\n- Merge similar recommendations from multiple agents\n- Prioritize by impact (high-value improvements first)\n- Flag conflicting advice for human review\n- Group by plan section\n\n### 7. Enhance Plan Sections\n\n\u003Cthinking>\nMerge research findings back into the plan, adding depth without changing the original structure.\n\u003C\u002Fthinking>\n\n**Enhancement format for each section:**\n\n```markdown\n## [Original Section Title]\n\n[Original content preserved]\n\n### Research Insights\n\n**Best Practices:**\n- [Concrete recommendation 1]\n- [Concrete recommendation 2]\n\n**Performance Considerations:**\n- [Optimization opportunity]\n- [Benchmark or metric to target]\n\n**Implementation Details:**\n```[language]\n\u002F\u002F Concrete code example from research\n```\n\n**Edge Cases:**\n- [Edge case 1 and how to handle]\n- [Edge case 2 and how to handle]\n\n**References:**\n- [Documentation URL 1]\n- [Documentation URL 2]\n```\n\n### 8. Add Enhancement Summary\n\nAt the top of the plan, add a summary section:\n\n```markdown\n## Enhancement Summary\n\n**Deepened on:** [Date]\n**Sections enhanced:** [Count]\n**Research agents used:** [List]\n\n### Key Improvements\n1. [Major improvement 1]\n2. [Major improvement 2]\n3. [Major improvement 3]\n\n### New Considerations Discovered\n- [Important finding 1]\n- [Important finding 2]\n```\n\n### 9. Update Plan File\n\n**Write the enhanced plan:**\n- Preserve original filename\n- Add `-deepened` suffix if user prefers a new file\n- Update any timestamps or metadata\n\n## Output Format\n\nUpdate the plan file in place (or if user requests a separate file, append `-deepened` after `-plan`, e.g., `2026-01-15-feat-auth-plan-deepened.md`).\n\n## Quality Checks\n\nBefore finalizing:\n- [ ] All original content preserved\n- [ ] Research insights clearly marked and attributed\n- [ ] Code examples are syntactically correct\n- [ ] Links are valid and relevant\n- [ ] No contradictions between sections\n- [ ] Enhancement summary accurately reflects changes\n- [ ] Implementation tasks have execution-ready structure (files, success criteria, test commands, dependencies)\n\n## Post-Enhancement Options\n\nAfter writing the enhanced plan, use the **AskUserQuestion tool** to present these options:\n\n**Question:** \"Plan deepened at `[plan_path]`. What would you like to do next?\"\n\n**Options:**\n1. **View diff** - Show what was added\u002Fchanged\n2. **Run `\u002Ftechnical_review`** - Get feedback from reviewers on enhanced plan\n3. **Start `\u002Fworkflows-work`** - Begin implementing this enhanced plan\n4. **Deepen further** - Run another round of research on specific sections\n5. **Revert** - Restore original plan (if backup exists)\n\nBased on selection:\n- **View diff** → Run `git diff [plan_path]` or show before\u002Fafter\n- **`\u002Ftechnical_review`** → Call the \u002Ftechnical_review command with the plan file path\n- **`\u002Fworkflows-work`** → Call the \u002Fworkflows-work command with the plan file path\n- **Deepen further** → Ask which sections need more research, then re-run those agents\n- **Revert** → Restore from git or backup\n\n## Example Enhancement\n\n**Before (from \u002Fworkflows-plan):**\n```markdown\n## Technical Approach\n\nUse React Query for data fetching with optimistic updates.\n```\n\n**After (from \u002Fworkflows-deepen-plan):**\n```markdown\n## Technical Approach\n\nUse React Query for data fetching with optimistic updates.\n\n### Research Insights\n\n**Best Practices:**\n- Configure `staleTime` and `cacheTime` based on data freshness requirements\n- Use `queryKey` factories for consistent cache invalidation\n- Implement error boundaries around query-dependent components\n\n**Performance Considerations:**\n- Enable `refetchOnWindowFocus: false` for stable data to reduce unnecessary requests\n- Use `select` option to transform and memoize data at query level\n- Consider `placeholderData` for instant perceived loading\n\n**Implementation Details:**\n```typescript\n\u002F\u002F Recommended query configuration\nconst queryClient = new QueryClient({\n  defaultOptions: {\n    queries: {\n      staleTime: 5 * 60 * 1000, \u002F\u002F 5 minutes\n      retry: 2,\n      refetchOnWindowFocus: false,\n    },\n  },\n});\n```\n\n**Edge Cases:**\n- Handle race conditions with `cancelQueries` on component unmount\n- Implement retry logic for transient network failures\n- Consider offline support with `persistQueryClient`\n\n**References:**\n- https:\u002F\u002Ftanstack.com\u002Fquery\u002Flatest\u002Fdocs\u002Freact\u002Fguides\u002Foptimistic-updates\n- https:\u002F\u002Ftkdodo.eu\u002Fblog\u002Fpractical-react-query\n```\n\nNEVER CODE! 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