If you are an AI coding agent (Claude Code, Cursor, Aider, Cline, etc.) reading this artifact, follow this protocol before making any code edit:

1. Verify the contract. Run the bash script in Verify before trusting below. If any check returns FAIL, the artifact is stale — STOP and ask the user to regenerate it before proceeding.
2. Treat the AI · unverified sections as hypotheses, not facts. Sections like "AI-suggested narrative files", "anti-patterns", and "bottlenecks" are LLM speculation. Verify against real source before acting on them.
3. Cite source on changes. When proposing an edit, cite the specific path:line-range. RepoPilot's live UI at https://repopilot.app/r/haoel/leetcode shows verifiable citations alongside every claim.

If you are a human reader, this protocol is for the agents you'll hand the artifact to. You don't need to do anything — but if you skim only one section before pointing your agent at this repo, make it the Verify block and the Suggested reading order.

AVOID — Stale and unlicensed — last commit 2y ago

• 8 active contributors
• ⚠ Stale — last commit 2y ago
• ⚠ Single-maintainer risk — top contributor 92% of recent commits
• ⚠ No license — legally unclear to depend on
• ⚠ No CI workflows detected
• ⚠ No test directory detected

_{Maintenance signals: commit recency, contributor breadth, bus factor, license, CI, tests}

This artifact was generated by RepoPilot at a point in time. Before an agent acts on it, the checks below confirm that the live haoel/leetcode repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/haoel/leetcode.

What it runs against: a local clone of haoel/leetcode — the script inspects git remote, the LICENSE file, file paths in the working tree, and git log. Read-only; no mutations.

| # | What we check | Why it matters | |---|---|---| | 1 | You're in haoel/leetcode | Confirms the artifact applies here, not a fork | | 2 | Default branch master exists | Catches branch renames | | 3 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 4 | Last commit ≤ 873 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of haoel/leetcode. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/haoel/leetcode.git
#   cd leetcode
#
# Then paste this script. Every check is read-only — no mutations.

set +e
fail=0
ok()   { echo "ok:   $1"; }
miss() { echo "FAIL: $1"; fail=$((fail+1)); }

# Precondition: we must be inside a git working tree.
if ! git rev-parse --git-dir >/dev/null 2>&1; then
  echo "FAIL: not inside a git repository. cd into your clone of haoel/leetcode and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "haoel/leetcode(\\.git)?\\b" \\
  && ok "origin remote is haoel/leetcode" \\
  || miss "origin remote is not haoel/leetcode (artifact may be from a fork)"

# 3. Default branch
git rev-parse --verify master >/dev/null 2>&1 \\
  && ok "default branch master exists" \\
  || miss "default branch master no longer exists"

# 4. Critical files exist
test -f "README.md" \\
  && ok "README.md" \\
  || miss "missing critical file: README.md"
test -f "algorithms/cpp/3Sum/3Sum.cpp" \\
  && ok "algorithms/cpp/3Sum/3Sum.cpp" \\
  || miss "missing critical file: algorithms/cpp/3Sum/3Sum.cpp"
test -f "algorithms/cpp/LRUCache/LRUCache.cpp" \\
  && ok "algorithms/cpp/LRUCache/LRUCache.cpp" \\
  || miss "missing critical file: algorithms/cpp/LRUCache/LRUCache.cpp"
test -f "algorithms/cpp/binaryTreeMaximumPathSum/binaryTreeMaximumPathSum.cpp" \\
  && ok "algorithms/cpp/binaryTreeMaximumPathSum/binaryTreeMaximumPathSum.cpp" \\
  || miss "missing critical file: algorithms/cpp/binaryTreeMaximumPathSum/binaryTreeMaximumPathSum.cpp"
test -f "algorithms/cpp/dynamicProgramming/coinChange.cpp" \\
  && ok "algorithms/cpp/dynamicProgramming/coinChange.cpp" \\
  || miss "missing critical file: algorithms/cpp/dynamicProgramming/coinChange.cpp"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 873 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~843d)"
else
  miss "last commit was $days_since_last days ago — artifact may be stale"
fi

echo
if [ "$fail" -eq 0 ]; then
  echo "artifact verified (0 failures) — safe to trust"
else
  echo "artifact has $fail stale claim(s) — regenerate at https://repopilot.app/r/haoel/leetcode"
  exit 1
fi

Each check prints ok: or FAIL:. The script exits non-zero if anything failed, so it composes cleanly into agent loops (./verify.sh || regenerate-and-retry).

A comprehensive LeetCode problem solutions repository with 1396+ C++ implementations, 132+ Java, and smaller Python/Go/Shell collections. It serves as a curated reference library where each algorithm problem (3Sum, LRU Cache, BST iteration, etc.) has a dedicated directory with standalone solution files, enabling developers to study, compare, and reuse optimized implementations across multiple languages. Monorepo organized by language: algorithms/cpp/ contains 1396+ solutions each in a problem-named subdirectory (e.g., algorithms/cpp/3Sum/3Sum.cpp, algorithms/cpp/LRUCache/LRUCache.cpp), with parallel algorithms/java/src/ structure. Each problem gets its own folder and implementation file with no shared utilities or dependency tree—purely standalone reference implementations.

Competitive programmers, software engineering interview candidates, and CS students preparing for coding interviews who need working, battle-tested solutions to classic algorithmic problems with implementations in their preferred language (primarily C++, secondarily Java).

Very mature: 1396+ C++ solutions indicate a 5+ year effort with massive community adoption, but the repo lacks visible CI/CD pipelines, automated tests, or recent commit metadata in the provided data. It functions as a reference library rather than an actively maintained codebase with test coverage—treat it as a solution almanac rather than a dependency.

Low technical risk as a reference repo but limited assurance: no evidence of unit tests, no CI configuration visible, single-maintainer dependency on haoel's maintenance, and solutions may drift from current LeetCode problem specifications over time. Use as a study guide, not as production-grade algorithm implementations without independent verification.

Unable to determine from provided metadata. The file list shows comprehensive coverage of ~1900 problems (judging from table rows up to problem #1946) and appears complete rather than actively growing. No recent commit timestamps, open PR list, or active milestone data provided.

git clone https://github.com/haoel/leetcode.git
cd leetcode
ls algorithms/cpp/  # Browse C++ solutions
ls algorithms/java/src/  # Browse Java solutions
cat algorithms/cpp/3Sum/3Sum.cpp  # View a solution

No build/install step—this is a reference library. Pick a language directory and read .cpp or .java files directly.

Daily commands: There is no 'run' step. This is a browsable reference library. To test a specific C++ solution: g++ -std=c++17 algorithms/cpp/3Sum/3Sum.cpp -o test && ./test. For Java: javac algorithms/java/src/3Sum/ThreeSum.java && java ThreeSum. Each solution is standalone and meant to be studied or integrated into your own project.

• README.md — Documents the entire LeetCode problem set index with 1900+ problems, solution links, and difficulty ratings—essential reference for navigating the repository structure.
• algorithms/cpp/3Sum/3Sum.cpp — Foundational two-pointer algorithm pattern used throughout classic array/string problems; understand this to grasp the repository's solution approach.
• algorithms/cpp/LRUCache/LRUCache.cpp — Core data structure design pattern combining hash map and doubly-linked list; demonstrates the codebase's approach to cache/complex data structures.
• algorithms/cpp/binaryTreeMaximumPathSum/binaryTreeMaximumPathSum.cpp — Advanced tree traversal with DFS; represents the recursive pattern strategy used across all tree-based problems in the repository.
• algorithms/cpp/dynamicProgramming/coinChange.cpp — Classic DP formulation with state transition; key reference for understanding bottom-up and memoization patterns across 100+ DP solutions.
• algorithms/cpp/burstBalloons/BurstBalloons.cpp — Interval DP variant; demonstrates advanced problem-solving patterns that differentiate medium from hard-tier solutions.

• Problem Solution Files (.cpp, .java) (C++11/14/17, Java 8+) — Implement a single LeetCode problem using optimal or instructive algorithms; include helper classes/functions; standalone compilation
  • Failure mode: Compilation errors if syntax invalid; runtime errors if algorithm logic flawed; TLE if exponential complexity on large inputs
• Folder Hierarchy (Algorithm Categories) (Filesystem conventions) — Organize solutions by technique (arrays, trees, dynamic programming, graphs, strings, etc.); enable learners to study related problems
  • Failure mode: User confusion if folder names inconsistent or mislabeled; search/indexing tools may fail
• README Index Table (Markdown) — Map problem IDs (1–1946+) to solution files, difficulty ratings, and LeetCode links; serve as master reference
  • Failure mode: Dead links if files moved/deleted; stale entries if not updated; manual maintenance errors
• Version Control (.gitignore, git metadata) (Git) — Track solution additions/changes; enable collaboration and history audit
  • Failure mode: Merge conflicts if multiple contributors add solutions to same file; binaries committed if .gitignore incomplete

• LeetCode Problem Statement → Solution File (.cpp/.java) — Developer reads problem, implements algorithm, stores in repository
• Solution File → README.md Table — Each solution gets indexed with problem ID, difficulty, and link in master table
• README.md Table → Developer Browser — User searches README table for problem ID or difficulty; clicks link to solution or LeetCode page
• Algorithm Category Folder → undefined — undefined

Add a new problem solution

1. Create a new folder under algorithms/cpp/ named in camelCase matching the problem name (e.g., algorithms/cpp/myNewProblem/) (algorithms/cpp/)
2. Write the solution in a .cpp file with PascalCase naming (e.g., MyNewProblem.cpp), including your algorithm implementation and helper functions (algorithms/cpp/myNewProblem/MyNewProblem.cpp)
3. Update the README.md table by adding a new row with LeetCode problem ID, problem title link, solution file link, and difficulty level (README.md)
4. For multi-part problems (e.g., variant I/II), add separate .cpp files in the same folder (e.g., MyProblem.I.cpp, MyProblem.II.cpp) (algorithms/cpp/myNewProblem/)
5. If creating a Java solution, mirror the folder structure under algorithms/java/src/ and add cross-language link to README table (algorithms/java/src/)

Implement a new algorithm pattern (e.g., graph traversal variant)

1. Identify the algorithm category (BFS, DFS, topological sort, union-find, etc.) and check if algorithms/cpp/[category]/ folder exists (algorithms/cpp/)
2. Create the solution file following the naming convention of existing files in that category (e.g., BipartiteGraphCheck.cpp) (algorithms/cpp/[algorithmCategory]/)
3. Include inline comments explaining the key insight (time/space complexity trade-offs, edge cases, pattern variants) (algorithms/cpp/[algorithmCategory]/[ProblemName].cpp)
4. Add entry to README.md table with problem ID, difficulty, and both C++ and Java solution links if both exist (README.md)

Add a cross-language solution pair

1. Complete the C++ solution in algorithms/cpp/[problemName]/[ProblemName].cpp (algorithms/cpp/[problemName]/[ProblemName].cpp)
2. Create matching Java file at algorithms/java/src/[ProblemName]/[problemName].java with identical algorithm logic but Java syntax (algorithms/java/src/[ProblemName]/[problemName].java)
3. Update README.md table entry to include both language links in the Solution column, formatted as [C++](...), [Java](...) (README.md)

• C++ primary, Java secondary — LeetCode's native languages; C++ preferred for performance-critical algorithms; Java for enterprise patterns and GC behavior study
• Folder-based organization by problem category — Mirrors LeetCode's problem taxonomy (arrays, trees, graphs, DP); enables learners to study pattern families rather than isolated problems
• Markdown README with indexed table — Provides O(1) problem lookup by ID; enables sorting by difficulty and quick link access to official LeetCode statements
• Plain-text solutions (no framework/build system) — Zero dependencies; solutions compile standalone with standard compiler; focus on algorithms, not infrastructure

• No test framework or harness

  • Why: Simplicity and universality; each solution is self-contained and compiler-executable
  • Consequence: Manual verification required; no automated regression testing; users must implement their own test drivers

• Inconsistent folder naming (camelCase, PascalCase, Roman numerals for variants)

  • Why: Grew organically with 600+ files; pragmatic approach to accommodate LeetCode's naming inconsistencies
  • Consequence: Harder to script bulk operations; requires human navigation; conventions documented only by example

• Solutions range from simple brute-force to advanced techniques in same problem set

  • Why: Real LeetCode constraints; shows progression and multiple valid approaches
  • Consequence: Novices may be overwhelmed; requires prior algorithm knowledge to appreciate trade-offs

• README table is manually maintained (not auto-generated)

  • Why: Allows human curation of descriptions and difficulty ratings; integrates problem IDs with GitHub links
  • Consequence: Prone to staleness; requires manual updates when solutions added or removed

• Does not provide interactive execution or online judge integration—solutions are reference implementations only
• Does not include problem statements or explanations—assumes user has access to official LeetCode problem descriptions
• Does not enforce consistent coding style or documentation standards across 600 files
• Does not support real-time collaboration or version-locked problem updates for courses
• Does not include performance benchmarks or comparative analysis between solutions
• Does not generate visualizations or step-through debugging tools

No environment variables, build config, or runtime dependencies required. Key gotcha: file naming inconsistencies—some directories use camelCase (e.g., addAndSearchWord), others PascalCase (e.g., 3Sum), and some have problems number prefixes. When searching for a problem, check README.md table first rather than guessing the directory name. Solutions are not guaranteed to compile out-of-the-box; they may use C++17 features without explicit flag documentation, or rely on implicit LeetCode type definitions (TreeNode, ListNode) not included in files.

• Two-pointer / sliding window technique — Core pattern in this repo appearing in 3Sum, container-with-most-water, and backspace string compare; essential for O(n) solutions to array/string problems.
• Hash map / unordered_map collision handling and load factor — Underlies LRUCache, group anagrams, and many other solutions; understanding hash table internals explains why C++ unordered_map sometimes underperforms std::map.
• Tree traversal patterns (inorder, preorder, postorder, level-order) — Recursive and iterative approaches appear in 6+ binary tree solutions in this repo; mastering these patterns unlocks most tree problems.
• Dynamic programming state design and memoization — Problems like best-time-to-buy-and-sell-stock (variants I–IV) and arithmetic-slices require DP; understanding state transitions and recurrence relations is critical.
• Union-find (disjoint-set) data structure — Appears in accountsMerge and graph connectivity problems; elegant solution for detecting cycles and connected components in near-O(1) amortized time.
• Trie (prefix tree) data structure — Underlying addAndSearchWord and other string search problems; O(m) lookup where m is string length, beating hash maps for prefix queries.
• Greedy algorithm correctness proofs — Problems like minimize-maximum-pair-sum-in-array require greedy choices; this repo includes solutions but often lacks proofs—critical to understand when greedy works.

• TheAlgorithms/Java — Parallel multi-algorithm reference library organized by concept (sorting, searching, DP) rather than by LeetCode problem ID; useful for understanding algorithm families beyond contest problems.
• labuladong/fucking-algorithm — Chinese-origin algorithm guide with detailed explanations, diagrams, and solution patterns organized by problem categories; complements this repo by teaching the 'why' behind solutions.
• greyireland/algorithm-pattern — LeetCode solutions with problem-solving pattern classification (backtracking, DP, sliding window) and detailed notes; same goal as haoel/leetcode but with pedagogical structure.
• donnemartin/system-design-primer — Companion resource for engineers studying interview problems; covers system design and scalability beyond algorithmic LeetCode problems.
• EbookFoundation/free-programming-books — Meta-repository linking to algorithm learning resources, textbooks, and courses; helps contextualize where competitive programming fits in broader CS education.

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Standardize C++ solution file naming convention across algorithms directory

The repo has inconsistent C++ file naming: some use PascalCase (e.g., 3Sum.cpp, LRUCache.cpp), others use camelCase (e.g., addBinary.cpp, addDigits.cpp), and directory names don't always match filenames. This creates friction for contributors and makes navigation harder. Establishing and enforcing a naming standard (e.g., PascalCase for all files matching PascalCase directories) would improve consistency and reduce onboarding friction.

• [ ] Create a CONTRIBUTING.md file documenting the naming convention (e.g., directories and files must both use PascalCase)
• [ ] Audit algorithms/cpp/ directory and identify all naming inconsistencies (camelCase files vs PascalCase directories)
• [ ] Create a PR that renames ~30-50 files to match the standard (e.g., addBinary.cpp → AddBinary.cpp)
• [ ] Add a pre-commit hook or GitHub Action to enforce naming consistency on future contributions

Create solution documentation template for each algorithm folder

Most algorithm folders lack README files explaining the approach, complexity, and trade-offs. The repo has 1000+ problems but no structured guidance for contributors on what to include. Adding a template README.md for each algorithm (starting with high-value problems like LRUCache, accountsMerge, binaryTreeMaximumPathSum) would help contributors understand expected documentation quality and improve discoverability.

• [ ] Create a SOLUTION_TEMPLATE.md in the root documenting expected sections: Problem Link, Approach, Time/Space Complexity, Key Insights, Edge Cases
• [ ] Add README.md files to 10-15 high-difficulty/popular problems (3Sum, LRUCache, accountsMerge, binaryTreeMaximumPathSum, etc.) following the template
• [ ] Update CONTRIBUTING.md to require README.md for all new algorithm submissions
• [ ] Create a GitHub Issue template requesting README.md for algorithms that lack them

Add language-specific build/test configuration for C++ solutions

The repo has algorithms in C++ but no build system, compiler flags, or test infrastructure defined. Contributors can't easily verify their C++ submissions compile or run correctly. Adding CMakeLists.txt with a basic build setup and a GitHub Actions workflow for C++ compilation would catch syntax errors and ensure code quality without requiring manual testing.

• [ ] Create a CMakeLists.txt in algorithms/cpp/ that discovers and compiles all .cpp files
• [ ] Add a .github/workflows/cpp-build.yml GitHub Action that compiles all C++ solutions on every PR
• [ ] Document in CONTRIBUTING.md: how to build locally (cmake), expected compiler version, and C++ standard (C++11/14/17)
• [ ] Fix any existing C++ compilation errors discovered by the new workflow

• Add unit test files for the 50 most fundamental problems (e.g., algorithms/cpp/3Sum/3Sum_test.cpp) using a lightweight framework like GoogleTest or Catch2, establishing a testing convention for the repo.
• Create a SOLUTION_TEMPLATE.md file in the root documenting problem statement, approach, time/space complexity, and edge cases for 10 representative solutions (3Sum, LRUCache, etc.) to serve as a style guide for new contributions.
• Audit and fix file naming inconsistencies across algorithms/cpp/ and algorithms/java/src/ (e.g., standardize on PascalCase directory names matching LeetCode problem titles) and update README.md links accordingly.

This is a LeetCode algorithm solutions repository with minimal security risk. It contains no external dependencies, hardcoded credentials, injection vectors, or infrastructure configurations typical of web applications. The codebase consists of isolated algorithm implementations in C++ and Java without network I/O, database access, or user input handling. The primary security considerations are standard secure coding practices within individual algorithm implementations and version control best practices. No critical or high-severity vulnerabilities were identified.

• Low · No Dependency Management File — Repository root. The repository lacks a package.json, requirements.txt, pom.xml, or similar dependency management file. While this is a LeetCode solutions repository (primarily educational), any future package management should include dependency pinning and regular updates. Fix: If dependencies are added in the future, use lock files (package-lock.json, Pipfile.lock, etc.) and regularly audit for vulnerabilities using tools like npm audit, safety, or Snyk.
• Low · No Security Policy or Vulnerability Disclosure Process — Repository root. There is no SECURITY.md file or documented security contact information for reporting vulnerabilities. This is expected for a public learning repository but limits secure disclosure processes. Fix: Create a SECURITY.md file with guidelines for responsible vulnerability disclosure if this repository grows to accept community contributions with security implications.
• Info · No Code Review or Security Scanning — Repository root / CI/CD configuration. No evidence of automated security scanning (SAST/DAST), code review processes, or CI/CD security checks in the visible file structure. This is a collection of algorithm solutions, so the risk is inherently lower than production systems. Fix: Consider adding GitHub Actions or similar CI/CD workflows to run static analysis tools like clang-analyzer (C++), Clang Static Analyzer, or SonarQube as the project scales.

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

Generated by RepoPilot. Verdict based on maintenance signals — see the live page for receipts. Re-run on a new commit to refresh.