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/geekxh/hello-algorithm 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 3y ago

• 3 active contributors
• ⚠ Stale — last commit 3y ago
• ⚠ Small team — 3 contributors active in recent commits
• ⚠ Single-maintainer risk — top contributor 85% 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 geekxh/hello-algorithm repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/geekxh/hello-algorithm.

What it runs against: a local clone of geekxh/hello-algorithm — 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 geekxh/hello-algorithm | 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 ≤ 1090 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of geekxh/hello-algorithm. If you don't
# have one yet, run these first:
#
#   git clone https://github.com/geekxh/hello-algorithm.git
#   cd hello-algorithm
#
# 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 geekxh/hello-algorithm and re-run."
  exit 2
fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "geekxh/hello-algorithm(\\.git)?\\b" \\
  && ok "origin remote is geekxh/hello-algorithm" \\
  || miss "origin remote is not geekxh/hello-algorithm (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 "PART_1_算法基础/README.md" \\
  && ok "PART_1_算法基础/README.md" \\
  || miss "missing critical file: PART_1_算法基础/README.md"
test -f "PART_2_力扣图解/README.md" \\
  && ok "PART_2_力扣图解/README.md" \\
  || miss "missing critical file: PART_2_力扣图解/README.md"
test -f "PART_1_算法基础/1_数据结构知识框架/数据结构知识框架.pdf" \\
  && ok "PART_1_算法基础/1_数据结构知识框架/数据结构知识框架.pdf" \\
  || miss "missing critical file: PART_1_算法基础/1_数据结构知识框架/数据结构知识框架.pdf"
test -f "PART_1_算法基础/2_基础知识/linked-list/README.md" \\
  && ok "PART_1_算法基础/2_基础知识/linked-list/README.md" \\
  || miss "missing critical file: PART_1_算法基础/2_基础知识/linked-list/README.md"
test -f "PART_1_算法基础/2_基础知识/tree/README.md" \\
  && ok "PART_1_算法基础/2_基础知识/tree/README.md" \\
  || miss "missing critical file: PART_1_算法基础/2_基础知识/tree/README.md"

# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
if [ "$days_since_last" -le 1090 ]; then
  ok "last commit was $days_since_last days ago (artifact saw ~1060d)"
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/geekxh/hello-algorithm"
  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).

hello-algorithm is a Chinese-language algorithm training curriculum for beginners, organized into 4 parts: foundational data structures (PART_1), LeetCode problem solutions with visual explanations (PART_2), big tech interview questions (PART_3), and CS knowledge summaries (PART_4). It provides structured learning from basics through interview-level problems, with diagrams, PDFs, and mind maps to make algorithms accessible. Hierarchical directory structure: PART_1_算法基础 contains data structure READMEs and PDFs organized by type (linked-list/, stack/, heap/, graph/, etc.); PART_2_力扣图解 contains markdown problem explanations paired with numbered image assets (sourcefile/[problem_number]/). No code compilation or build system needed.

Junior engineers and computer science students preparing for tech interviews at large companies, who need structured algorithm training with visual explanations and real LeetCode problem walkthroughs rather than abstract textbooks.

Actively maintained educational resource with significant community adoption (visible from star count and README prominence). The project is stable and well-organized into 4 clear parts, but it's primarily a curated learning guide rather than a live codebase—no CI/CD, tests, or code artifacts visible in the file structure.

Very low technical risk as a documentation/curriculum project. No external dependencies to manage, no critical runtime code, and no deployment concerns. Main risk is maintainer bandwidth for keeping problem solutions and explanations current with LeetCode updates; single-author dependency for content quality.

Project appears to be in steady-state maintenance mode with a complete foundational curriculum established. Recent activity likely involves: adding new LeetCode problem solutions to PART_2, updating interview question collections in PART_3, and expanding the CS knowledge summary in PART_4. No visible active issues or PRs in provided data.

Clone the repository: git clone https://github.com/geekxh/hello-algorithm.git && cd hello-algorithm. No build step required—open any .md file with a markdown viewer (GitHub automatically renders them) or download the PDF versions from the links in README.md. Start with PART_1_算法基础/README.md for foundational concepts.

Daily commands: This is not a runnable application. To use: (1) browse locally with cd PART_2_力扣图解/sourcefile && cat '10.排序类题目/701.md' to read problem explanations; (2) view images with any image viewer in numbered subdirectories; (3) download complete PDF from link in README for offline study.

• PART_1_算法基础/README.md — Entry point for algorithm fundamentals covering data structures and core concepts that underpin all subsequent learning materials.
• PART_2_力扣图解/README.md — Organizes LeetCode problem solutions with visual explanations; critical reference for understanding problem-solving patterns used throughout the codebase.
• PART_1_算法基础/1_数据结构知识框架/数据结构知识框架.pdf — Core visual framework document that maps all data structure concepts; essential foundation before exploring specific implementations.
• PART_1_算法基础/2_基础知识/linked-list/README.md — Foundational data structure tutorial used as reference in many algorithm solutions; critical for understanding pointer-based data manipulation patterns.
• PART_1_算法基础/2_基础知识/tree/README.md — Essential tree structure documentation covering recursive algorithms and traversal patterns applicable to multiple problem categories.
• PART_2_力扣图解/sourcefile/1.学习须知/01.md — Learning guidelines and methodology for using the LeetCode visual solutions; sets expectations for approaching problems systematically.

Add a New LeetCode Problem Solution

1. Create a new markdown file in the appropriate category (e.g., sorting, bit manipulation, binary search) with naming convention NNN.md where NNN is the problem number (PART_2_力扣图解/sourcefile/10.排序类题目/701.md (example template))
2. Create corresponding directory NNN/ in same category folder to store problem visualizations (PART_2_力扣图解/sourcefile/10.排序类题目/701/ (example template))
3. Add numbered JPG files (1.jpg, 2.jpg, etc.) for static diagrams and GIF files for animated explanations showing algorithm steps (PART_2_力扣图解/sourcefile/10.排序类题目/701/1.jpg)
4. In the markdown file, reference images in format and provide problem description, approach explanation, complexity analysis, and code solution (PART_2_力扣图解/sourcefile/10.排序类题目/701.md)
5. Update the category README to index the new problem number if one exists, or create category README following PART_2_力扣图解/README.md structure (PART_2_力扣图解/README.md)

Add a New Data Structure Tutorial

1. Create a new folder in PART_1_算法基础/2_基础知识/ with the data structure name (e.g., 'binary-search-tree') (PART_1_算法基础/2_基础知识/linked-list/ (example structure))
2. Create README.md in the new folder with explanation of the data structure, operations, use cases, time/space complexity, and implementation notes (PART_1_算法基础/2_基础知识/linked-list/README.md (example))
3. Include diagrams or reference visual concepts similar to 数据结构知识框架.pdf organization (PART_1_算法基础/1_数据结构知识框架/数据结构知识框架.pdf)
4. Update PART_1_算法基础/README.md to list the new data structure in the 2_基础知识 section (PART_1_算法基础/README.md)

Add a New Algorithm Category

1. Create new numbered folder in PART_2_力扣图解/sourcefile/ (e.g., '13.动态规划系列') following existing numbering pattern (PART_2_力扣图解/sourcefile/10.排序类题目/ (example structure))
2. Create learning prerequisites markdown file (e.g., 1.学习须知/04.md) explaining category concepts and recommended approach (PART_2_力扣图解/sourcefile/1.学习须知/01.md (example))
3. Add problem solutions following the NNN.md and NNN/ directory pattern established in other categories (PART_2_力扣图解/sourcefile/12.二分法系列/901.md (example))
4. Update PART_2_力扣图解/README.md to list the new category with problem count and difficulty overview (PART_2_力扣图解/README.md)

• Markdown (MD) + PDF — Lightweight, version-control friendly, renders natively on GitHub, supports embedded image references for documentation
• Static Images (JPG/PNG) + Animated GIFs — Provides visual learning for complex algorithm flows; GIFs show step-by-step transformations; no external dependencies or build required
• Hierarchical Directory Structure — Organizes content by learning progression (Part 1-4) and by topic/problem type; allows scalable addition of new problems without restructuring

• Static markdown documentation over interactive web app

  • Why: Simplicity, zero infrastructure overhead, easy collaboration via Git, offline accessibility, longevity of content
  • Consequence: Requires manual image creation and updates; no real-time feedback or progress tracking; cannot dynamically generate solutions

• Problem-based organization (by category) rather than concept-based

  • Why: Aligns with LeetCode practice methodology; learners can identify problems by type quickly
  • Consequence: Cross-cutting concepts may be fragmented; learners must reference PART_1 foundations separately; harder to trace dependencies

• Multiple delivery formats (Markdown + PDF + potential ebook)

  • Why: Reaches different learner preferences and enables offline study; supports multiple distribution channels
  • Consequence: Requires syncing multiple formats; maintenance overhead; potential version conflicts between formats

• Does not provide interactive coding environment or runtime execution
• Does not track learner progress or provide adaptive difficulty sequencing
• Does not include automated test suites or solution validation
• Not designed as a substitute for actual LeetCode practice; references LeetCode problems but does not host solutions code inline
• Does not support real-time collaboration or comments (static content)
• Not a real-time system; optimized for self-paced learning, not live courses

No hidden traps in traditional sense (no build configuration, env vars, or service dependencies). However: (1) markdown image paths are relative to the numbered folder structure—moving images breaks links; (2) LeetCode problem numbers in filenames (701, 801, 802) must match actual LeetCode problem IDs; (3) Chinese filename encoding may cause issues on Windows without UTF-8 locale; (4) offline PDF version may lag behind latest online content.

• Linked List Traversal & Reversal — Foundational pointer manipulation pattern used in 30+ LeetCode problems; mastering linked list operations unlocks solutions to recursive problems, cycle detection, and reordering tasks
• Stack-based Recursion Simulation — Core technique in tree traversal, expression parsing, and backtracking problems; understanding manual stack frames helps optimize depth-first search solutions
• Heap Priority Queue Operations — Essential for top-K problems, median-finding, and Dijkstra's algorithm; knowing heapify, insertion, and extraction complexity (O(log n)) is critical for interview optimization
• Two-Pointer Technique — Ubiquitous in sorted array problems, linked list manipulation, and sliding window patterns; appears in 15+ problems in PART_2 with efficiency gains from O(n²) to O(n)
• Trie (Prefix Tree) — Optimal data structure for autocomplete, word search, and IP routing problems; understanding trie construction and DFS traversal enables O(m) word lookup instead of O(n·m) with hashtables
• Graph Traversal: DFS vs BFS — Fundamental algorithms for cycle detection, shortest path (BFS), and connected component analysis; choosing the right traversal directly impacts time/space complexity on PART_2 graph problems
• Bitwise Operations & Bitmasks — PART_2 section 11 (位运算系列) covers this; bitmask DP and XOR tricks reduce space complexity and improve performance on subset enumeration and parity problems

• TheAlgorithms/Java — Curated collection of algorithm implementations in Java with tests; complements this repo's visual learning with runnable, tested code examples
• labuladong/fucking-algorithm — Similar Chinese-language algorithm learning guide with detailed problem breakdowns; alternative approach to teaching same LeetCode problem set with different visual explanations
• azl397985856/leetcode — Another comprehensive Chinese LeetCode solution guide with categorized problems and hints; parallel resource for same target audience
• krahets/hello-algorithm — English-language beginner-friendly algorithm tutorial with interactive visualizations; English-speaking equivalent to this repo for algorithm learning flow
• jiayimeng123/awesome-interview — Tech interview preparation repo covering company-specific questions and behavioral rounds; natural companion for PART_3 big tech interview content

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.

Add interactive code examples and LeetCode solution implementations for PART_2

The PART_2_力扣图解 directory contains markdown files with images explaining LeetCode problems (e.g., 801.md, 802.md), but there are no actual code solutions. Adding executable code solutions in multiple languages (Python, Java, C++) for each problem would dramatically increase the repository's value for learners. This is a concrete gap: files exist (701.md, 801.md, etc.) but lack corresponding solution code files.

• [ ] Create PART_2_力扣图解/solutions/ directory with language subdirectories (python/, java/, cpp/)
• [ ] For each problem markdown (e.g., 801.md), add corresponding solution files (e.g., 801.py, 801.java, 801.cpp)
• [ ] Include time/space complexity analysis in code comments for each solution
• [ ] Create a solutions/README.md documenting how to run and understand the solutions

Create comprehensive test suites for data structure implementations in PART_1

PART_1_算法基础/2_基础知识 contains README.md files for each data structure (linked-list, stack, queue, heap, tree, graph, trie, hash-table, priority-queue, doubly-linked-list) but no test files or implementation code. Adding unit tests would allow contributors to verify correctness and provide learning value through test-driven development examples.

• [ ] Create tests/ directory at PART_1_算法基础/2_基础知识/ level
• [ ] Add test files for each data structure (test_linked_list.py, test_stack.py, test_heap.py, etc.)
• [ ] Include test cases for edge cases (empty structures, single elements, duplicates)
• [ ] Create implementation files alongside tests in a src/ directory matching the test structure

Develop automated scripts to validate and cross-reference PART_2 problem solutions with LeetCode

PART_2_力扣图解 has 100+ problems organized in numbered subdirectories (like 801/, 802/, etc.) with images but lacks a validation mechanism. Creating a script to ensure all referenced LeetCode problem IDs are correct, images load properly, and markdown formatting is consistent would improve maintainability and catch broken content.

• [ ] Create scripts/validate_leetcode_links.py to parse all markdown files in PART_2_力扣图解/sourcefile/
• [ ] Verify each problem number corresponds to a valid LeetCode problem (using LeetCode API or known problem ranges)
• [ ] Check that all image references in markdown files exist in their corresponding directories
• [ ] Add a GitHub Actions workflow (.github/workflows/validate-content.yml) to run validation on pull requests

• Add English translations for PART_1_算法基础 data structure READMEs (currently Chinese-only) to match the english branch mentioned in README—start with linked-list/README.md and stack/README.md
• Create missing visual diagrams for PART_2 sorting problems: add 3-5 JPG images showing bubble sort, merge sort, and quicksort step-by-step execution (similar to existing 701 example) in a new sourcefile/10.排序类题目/[problem_num]/ folder
• Expand PART_4 CS knowledge summaries by adding markdown files for system design topics (caching strategies, distributed consensus, load balancing) with links from main PART_2 interview questions that reference them

This repository is primarily a documentation and educational resource for algorithm training with minimal executable code. No hardcoded secrets, injection vulnerabilities, or critical misconfigurations were detected in the provided structure. The main security concerns are low-severity issues related to environment-specific files and external resource references. The codebase follows good practices by being primarily documentation-based rather than containing complex executable code with common vulnerability vectors. For any executable components not shown in this analysis, standard secure coding practices and dependency management should be applied.

• Low · Missing .gitignore entries for macOS system files — .DS_Store, .gitignore. The repository contains .DS_Store files which are macOS system files that should not be committed to version control. While present in the file listing, proper .gitignore configuration should exclude these automatically. Fix: Ensure .gitignore includes '*.DS_Store' and '.DS_Store' entries to prevent accidental commits of macOS system files.
• Low · No dependency management file detected — Root directory - No package/dependency files. The codebase appears to be a documentation/educational repository with no package.json, requirements.txt, pom.xml, or similar dependency management files provided. While this reduces dependency-related vulnerabilities, it also indicates limited executable code requiring security scanning. Fix: If this repository will contain executable code, implement proper dependency management and keep dependencies updated. Use tools like npm audit, pip audit, or equivalent for your technology stack.
• Low · External reference to third-party website — README.md - External links section. The README contains references to external websites (geekxh.com, cxyhub.com) for PDF downloads and content hosting. Users downloading from external sources face potential security risks if those sites are compromised or serve malicious content. Fix: Host critical resources directly in the repository or verified CDN. Implement integrity checking (SRI hashes) if linking to external resources. Document trust model clearly.

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.