supabase
Handles the full Supabase workflow from schema changes to deployment, with built-in security guardrails that catch common traps like RLS...
fix-todos
Intelligent TODO resolution with context-aware implementation
Full skill instructions
I'll systematically find and resolve TODO comments in your codebase with intelligent understanding and continuity across sessions.
Arguments: $ARGUMENTS - files, directories, or specific TODO patterns to fix
Target: 60% reduction (3,000-5,000 → 1,200-2,000 tokens)
This skill implements aggressive optimization for TODO resolution workflows by minimizing file reads, leveraging session state, and using progressive fixing patterns.
1. Grep-First TODO Discovery (Primary: 60-70% savings)
# ALWAYS use Grep to find TODOs - never Read files for scanning
grep -rn "TODO|FIXME|HACK|XXX" --include="*.{js,ts,py,go}" .
grep "TODO:" src/ -C 2 # Get 2 lines context for understanding
# ANTI-PATTERN: Reading files to find TODOs
# Read each .js file # ❌ NEVER do this
2. Session-Based State Tracking (80% savings on resume)
fix-todos/
├── plan.md # All TODOs with resolution status
├── state.json # Current progress, decisions, context
└── resolutions/ # Completed TODO resolutions for reference
**Session state prevents re-scanning and re-analyzing:
fix-todos/ directory FIRST3. Git Diff for Changed Files with TODOs (70% savings)
# Find files with TODOs that changed recently
git diff --name-only HEAD~5 | xargs grep -l "TODO"
# Focus on changed files vs full codebase scan
git diff --name-only | grep -f todo-files.txt
4. Progressive TODO Resolution (one at a time)
5. Early Exit Patterns
# Check for session FIRST
ls fix-todos/state.json 2>/dev/null && echo "Session exists"
# Check for TODOs before scanning
grep -q "TODO" . || echo "No TODOs found"
# Skip fixed TODOs from state
grep "^FIXED:" fix-todos/plan.md | wc -l
START
├── Session exists? (check fix-todos/state.json)
│ ├── YES → Load state (200 tokens)
│ │ ├── Show progress summary
│ │ ├── Resume from last TODO
│ │ └── Fix next TODO (400-800 tokens)
│ └── NO → New session
│ ├── Grep for TODOs (400 tokens)
│ ├── No TODOs? → Early exit (300 tokens)
│ └── Create plan → Fix first TODO (1,200-1,500 tokens)
└── Specific file/pattern?
├── Grep in scope only (300 tokens)
└── Fix matching TODOs (800-1,200 tokens)
Initial Session (1,200-2,000 tokens):
Resume Session (400-800 tokens):
Specific File Focus (800-1,200 tokens):
Cache common TODO resolution templates:
// Error handling template
TODO_TEMPLATES = {
error_handling: "try { /* existing */ } catch (err) { logger.error(err); throw err; }",
validation: "if (!input) throw new ValidationError('Required');",
null_check: "if (value === null) return defaultValue;",
logging: "logger.debug('Operation:', { context });",
type_guard: "if (typeof x !== 'expected') throw new TypeError();"
}
// Use templates instead of analyzing patterns every time
Session Location: fix-todos/ (current project directory)
plan.md - TODO inventory with statusstate.json - Progress, decisions, current TODOresolutions/ - Completed fixes for pattern matchingCache Location: .claude/cache/fix-todos/
todo-inventory.json - All TODOs foundresolution-patterns.json - Your code patternscontext-snippets/ - Related code for each TODOCache Validity: Until session completed or user runs fix-todos new
Cache Sharing: /find-todos, /create-todos, /todos-to-issues can reuse TODO inventory
Example 1: Resume existing session
BEFORE optimization (3,500 tokens):
- Re-scan all files for TODOs: 1,200 tokens
- Re-categorize: 400 tokens
- Re-create plan: 500 tokens
- Find next TODO: 300 tokens
- Fix TODO: 1,100 tokens
AFTER optimization (500 tokens):
- Load state: 100 tokens
- Read next TODO file: 200 tokens
- Fix TODO: 200 tokens
Savings: 3,000 tokens (86%)
Example 2: Fix TODOs in specific file
BEFORE optimization (4,000 tokens):
- Read all project files: 2,000 tokens
- Find TODOs everywhere: 800 tokens
- Filter to target file: 200 tokens
- Fix TODOs: 1,000 tokens
AFTER optimization (1,000 tokens):
- Grep in specific file: 200 tokens
- Read file: 300 tokens
- Fix TODOs: 500 tokens
Savings: 3,000 tokens (75%)
Example 3: New session with no TODOs
BEFORE optimization (2,000 tokens):
- Read many files looking for TODOs: 1,800 tokens
- Report none found: 200 tokens
AFTER optimization (300 tokens):
- Grep entire codebase: 200 tokens
- Report none found (early exit): 100 tokens
Savings: 1,700 tokens (85%)
High-efficiency commands (400-800 tokens):
fix-todos resume - Continue existing sessionfix-todos src/api/auth.js - Specific filefix-todos status - Check progress onlyMedium-efficiency commands (1,200-1,500 tokens):
fix-todos - New session (first time)fix-todos src/ - Directory scopefix-todos "security" - Pattern filterAvoid if possible (2,000+ tokens):
fix-todos new when session already existsBefore TODO Discovery:
fix-todos/state.json FIRSTDuring TODO Resolution:
After TODO Resolution:
❌ Reading all files to find TODOs → Use Grep ❌ Re-scanning on every resume → Use session state ❌ Fixing all TODOs in one iteration → Progressive mode ❌ Not checking for existing session → Load state first ❌ Not using cached patterns → Apply templates ❌ Full codebase analysis per TODO → Incremental context
Token Usage:
Reduction: 60-75% (exceeds 60% target)
Optimization Status: ✅ Optimized (Phase 2 Batch 3D-F, 2026-01-26)
I'll maintain TODO resolution progress across sessions:
Session Files (in current project directory):
fix-todos/plan.md - All TODOs found and resolution statusfix-todos/state.json - Current progress and decisionsIMPORTANT: Session files are stored in a fix-todos folder in your current project directory
Auto-Detection:
resume, status, newMANDATORY FIRST STEPS:
fix-todos directory exists in current working directoryfix-todos/state.jsonfix-todos/plan.mdI'll find and categorize all TODOs:
TODO Detection:
Smart Categorization:
Based on analysis, I'll create a resolution plan:
Priority Order:
I'll write this plan to fix-todos/plan.md with:
I'll fix TODOs matching your code patterns:
Pattern Detection:
Resolution Strategies:
I'll resolve TODOs systematically:
Execution Process:
Progress Tracking:
After each resolution:
Session Resume:
When you return and run /fix-todos or /fix-todos resume:
Progress Example:
RESUMING TODO FIXES
├── Total TODOs: 47
├── Resolved: 23 (49%)
├── Current: src/api/auth.js:42
└── Next: src/utils/validation.js:15
Continuing resolution...
Start Fixing:
/fix-todos # Fix all TODOs
/fix-todos src/ # Focus on directory
/fix-todos "security" # Fix security TODOs
Session Control:
/fix-todos resume # Continue existing session
/fix-todos status # Check progress
/fix-todos new # Start fresh
Protection Measures:
Important: I will NEVER:
After resolving critical TODOs:
/test - To ensure fixes work correctly/commit - To save TODO resolutionsI'll maintain complete continuity between sessions, always resuming exactly where we left off with full context of previous resolutions.
Handles the full Supabase workflow from schema changes to deployment, with built-in security guardrails that catch common traps like RLS...
Comprehensive guides and best practices for Neon Serverless Postgres, covering setup, connection methods, authentication, and platform APIs.
Guide for setting up and using Firebase Authentication. Use this skill when the user's app requires user sign-in, user management, or secure data access using auth rules.
A skill to evaluate how secure Firestore security rules are. Use this when Firestore security rules are updated to ensure that the generated rules are extremely secure and robust.
Official skill for integrating Firebase AI Logic (Gemini API) into web applications. Covers setup, multimodal inference, structured output, and security.
Complete Better Auth server and client setup with database adapters, session management, plugins, and security configuration.
Deploy and manage projects on Vercel using token-based authentication. Use when working with Vercel CLI using access tokens rather than interactive login — e.g. "deploy to vercel", "set up vercel", "add environment variables to vercel".
Complete Cloudflare platform integration with decision trees for compute, storage, AI, networking, security, and infrastructure-as-code.
Run Azure compliance and security audits with azqr plus Key Vault expiration checks. Covers best-practice assessment, resource review, policy/compliance validation, and security posture checks. WHEN: compliance scan, security audit, BEFORE running azqr (compliance cli tool), Azure best practices, Key Vault expiration check, expired certificates, expiring secrets, orphaned resources, compliance assessment.
Send, read, and manage Gmail messages, drafts, labels, and account settings.
Comprehensive website auditing across 230+ rules in 21 categories including SEO, performance, security, and accessibility.
Shared authentication, CLI syntax, and output formatting patterns for gws Google Workspace commands.
Discover your celebrity doppelgänger with StarByFace!
GeoSpy: Pricing, Features, FAQs, and Alternatives for AI Teams
Detect AI-generated voices to protect against audio fraud.
Ensure Your Content's Originality with AI Plagiarism Checker
Upscale images by 400% without quality loss
Protect your social media from copyright disputes
Accurately Detect AI-Generated Content with TheChecker.AI
Chrome extension that detects and flags AI-generated content