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WAIT — Stale — last commit 2y ago

• 14 active contributors
• MIT licensed
• ⚠ Stale — last commit 2y ago
• ⚠ Concentrated ownership — top contributor handles 64% of recent commits
• ⚠ 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 greyireland/algorithm-pattern repo on your machine still matches what RepoPilot saw. If any fail, the artifact is stale — regenerate it at repopilot.app/r/greyireland/algorithm-pattern.

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| # | What we check | Why it matters | |---|---|---| | 1 | You're in greyireland/algorithm-pattern | Confirms the artifact applies here, not a fork | | 2 | License is still MIT | Catches relicense before you depend on it | | 3 | Default branch master exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 837 days ago | Catches sudden abandonment since generation |

#!/usr/bin/env bash
# RepoPilot artifact verification.
#
# WHAT IT RUNS AGAINST: a local clone of greyireland/algorithm-pattern. If you don't
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#
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#   cd algorithm-pattern
#
# 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
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fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "greyireland/algorithm-pattern(\\.git)?\\b" \\
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test -f "README.md" \\
  && ok "README.md" \\
  || miss "missing critical file: README.md"
test -f "SUMMARY.md" \\
  && ok "SUMMARY.md" \\
  || miss "missing critical file: SUMMARY.md"
test -f "introduction/quickstart.md" \\
  && ok "introduction/quickstart.md" \\
  || miss "missing critical file: introduction/quickstart.md"
test -f "basic_algorithm/dp.md" \\
  && ok "basic_algorithm/dp.md" \\
  || miss "missing critical file: basic_algorithm/dp.md"
test -f "data_structure/binary_tree.md" \\
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# 5. Repo recency
days_since_last=$(( ( $(date +%s) - $(git log -1 --format=%at 2>/dev/null || echo 0) ) / 86400 ))
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else
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fi

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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 structured algorithm learning resource in Go that provides templated solutions to LeetCode problems, organized by algorithm pattern (binary search, dynamic programming, backtracking, sliding window, etc.). It's designed as a curated study guide to progress from data structures fundamentals through advanced algorithms, with problem categorization and visual flowcharts to accelerate problem-solving speed. Single-directory monolith: root contains structured markdown files (introduction/, data_structure/, basic_algorithm/, advanced_algorithm/, practice_algorithm/) with theory and problem lists, plus a src/ folder with Go executable examples (e.g., sort implementations with tests). Images/ contains algorithm flowchart visuals referenced in markdown. No package/module split; Go code is minimal and example-focused.

Job-seeking software engineers and CS students preparing for technical interviews at tier-1 tech companies (BAT/TMD equivalent), specifically those wanting a systematic 6-8 week study path rather than randomly grinding LeetCode by problem ID.

Actively maintained educational resource with clear structure and GitBook publication (greyireland.gitbook.io/algorithm-pattern/). Based on the author's personal 1.5-month grind to 240 LeetCode problems leading to a successful hire. Repository has meaningful star count and ongoing documentation, though no formal CI/test infrastructure visible—it's documentation + example patterns, not a packaged library.

Single-author maintenance (greyireland) with no visible CI pipeline or automated tests. Content is educational rather than production code, so breakage risk is low, but commit recency and issue responsiveness unknown. Go version constraints and dependency counts are minimal (just src/main.go and Go stdlib), so technical debt is minimal.

Primary activity is documentation refinement. TODO.md is present but content unknown. Repo appears to be in steady-state as a published study guide—the focus is on curating and explaining existing algorithm patterns rather than adding new algorithms. GitBook is the main distribution channel.

Clone the repo: git clone https://github.com/greyireland/algorithm-pattern.git. Read online via GitBook: https://greyireland.gitbook.io/algorithm-pattern/. To run Go examples locally: cd src && go test ./... (or go run sort/heap_sort.go for specific implementations). No external dependencies to install.

Daily commands: Read locally: open src/main.go or view markdown files directly. Run tests: cd src && go test -v ./.... No dev server or build step needed; it's a static documentation repo with minimal Go executables.

• README.md — Entry point describing the algorithm template repository structure, learning path, and core curriculum sections—essential for understanding project scope and organization.
• SUMMARY.md — Gitbook table of contents defining all major algorithm topics and learning progression order—critical for navigating the template framework.
• introduction/quickstart.md — Quick-start guide explaining the methodology for using templates and recommended problem-solving approach—foundational for all contributors adding content.
• basic_algorithm/dp.md — Dynamic programming template core—one of the most referenced and studied algorithm patterns in the entire repository.
• data_structure/binary_tree.md — Binary tree fundamentals and traversal templates—foundation for understanding recursive and advanced algorithm patterns.
• src/main.go — Go language implementation entry point demonstrating template concepts in executable code form.
• advanced_algorithm/backtrack.md — Backtracking algorithm template—bridges basic and advanced algorithmic thinking required for complex problems.

• Data Structure Templates (data_structure/) — Defines core building blocks (trees, linked lists, stacks) with operations and use-case patterns

Add a New Algorithm Template

1. Create a new markdown file in the appropriate algorithm category (basic_algorithm/, advanced_algorithm/, or practice_algorithm/) (basic_algorithm/new_algorithm.md)
2. Follow the template structure: problem description, solution approach with diagrams, time/space complexity analysis, and practice problems (basic_algorithm/binary_search.md)
3. Add reference to SUMMARY.md in the appropriate section to include in Gitbook navigation (SUMMARY.md)
4. Implement concrete Go example in src/ directory with working code and tests (src/sort/heap_sort.go)

Add Practice Problems with Solutions

1. Append problem section to relevant template file (e.g., binary_tree.md) with LeetCode problem numbers (data_structure/binary_tree.md)
2. Reference images/ directory for solution diagrams illustrating the pattern application (images/tree_type.png)
3. Link to Gitbook online documentation which auto-publishes from markdown files (README.md)

Add a New Data Structure Template

1. Create markdown file in data_structure/ directory with fundamentals, operations, and use cases (data_structure/binary_tree.md)
2. Include ASCII diagrams or reference image assets showing data structure visualization (images/binary_search_template.png)
3. Create corresponding Go implementation with test cases in src/ subdirectory (src/sort/heap_sort_test.go)
4. Register new file in SUMMARY.md under Data Structure section (🐰 tier) (SUMMARY.md)

Add Introduction Content for New Learners

1. Create tutorial markdown in introduction/ directory targeting specific programming language or concept (introduction/golang.md)
2. Provide quick-start examples that match the template philosophy of problem-solving patterns (introduction/quickstart.md)
3. Reference this in README.md's core content section and SUMMARY.md introduction tier (🐶) (README.md)

• Markdown (.md files) — Lightweight, version-controllable, Gitbook-compatible documentation format for algorithm templates that can be studied offline and collaboratively edited
• Go (Golang) — Concise, modern language with clear syntax for demonstrating algorithm patterns; includes fast compilation and testing capabilities for validating templates
• Gitbook — Transforms markdown files into beautiful, searchable online documentation with navigation and cross-linking for optimal learning experience
• GitHub — Provides version control, collaborative editing, and public accessibility for community-driven algorithm template development
• PNG images — Visual reinforcement of complex concepts (tree traversal, DP memoization) and algorithm flow diagrams referenced throughout templates

• Template-based approach vs. problem-indexed approach

  • Why: Learners benefit from mastering patterns first, then applying to many problems, rather than memorizing individual problem solutions
  • Consequence: Slower initial progress on problem count but deeper understanding and faster problem-solving after template mastery

• Go as primary implementation language vs. multiple languages

  • Why: Go's simplicity and clarity make algorithm patterns obvious; reduces cognitive load for learners comparing multiple syntax styles
  • Consequence: Learners familiar only with Python/JavaScript must translate; however, algorithm logic remains language-agnostic

• Markdown documentation + external LeetCode reference vs. embedded problem solutions

  • Why: Keeps repository focused, avoids copyright issues, and ensures problems stay current with LeetCode updates
  • Consequence: Learners must switch between Gitbook and LeetCode; increased friction but maintained community goodwill and legal safety

• Linear learning progression (4 tiers: 🐶🐰🐮🦁) vs. topic-first randomized access

  • Why: Scaffolded approach prevents learners from tackling advanced patterns without foundational understanding
  • Consequence: Learners may perceive bottlenecks; requires strong self-discipline to follow progression rather than jumping to interesting topics

• Does not provide real-time coding interviews or interview simulation platform
• Does not include problem-specific solutions or code walkthroughs—only pattern templates
• Not a competitive programming encyclopedia; focuses on interview-level algorithm knowledge
• Does not track learner progress or provide adaptive learning paths
• Not a job application or hiring platform; only educational content
• Does not cover system design or infrastructure architecture patterns

No hidden traps—repo is deliberately simple. All content is markdown + minimal Go. No config files, env vars, or external service dependencies. Only gotcha: GitBook version may diverge from GitHub markdown if not synced; always check online version for latest updates. Go examples use only stdlib, so no version pinning issues.

• Binary Search Template — Core pattern for O(log n) problems; repo emphasizes template-first approach so variations (leftmost, rightmost, search range) become mechanical.
• Dynamic Programming Recurrence Relations — Central to repo's dp.md module; understanding state definition and transitions (memoization vs tabulation) unlocks 20%+ of interview questions.
• Backtracking / DFS Recursion Trees — Repo template (images/backtrack.png) visualizes decision trees for NP-hard problems (permutations, subsets, Sudoku); critical for constraint-satisfaction problems.
• Sliding Window Protocol — Advanced algorithm section isolates this O(n) technique for substring/subarray problems; represents optimization over brute-force enumeration.
• Binary Search Tree Traversal & Invariants — binary_search_tree.md template leverages BST property (left < node < right) to reduce search/insertion from O(n) to O(log n) in balanced trees.
• Linked List Two-Pointer Techniques — data_structure/linked_list.md likely covers fast/slow pointer pattern (referenced in images/fast_slow_linked_list.png) for cycle detection and midpoint finding.
• Heap (Priority Queue) Ordering — src/sort/heap_sort.go + images/heap.png demonstrate heap structure for efficient sorting and top-k problems in O(n log n).

• labuladong/fucking-algorithm — Chinese-language algorithm study guide with similar 'pattern-first' philosophy and LeetCode problem links; complementary approach to same problem domain.
• TheAlgorithms/Go — Catalog of algorithm implementations in Go with test coverage; can serve as reference implementations for extending algorithm-pattern's src/ examples.
• azl397985856/leetcode — JavaScript-based LeetCode solutions organized by category; mirrors algorithm-pattern's structure in a different language, useful for cross-language learning.
• halfrost/LeetCode-Go — Go-specific LeetCode solutions repository with detailed explanations; ideal companion for validating solutions written while following algorithm-pattern templates.
• CyC2018/CS-Notes — Comprehensive CS interview prep (systems, networking, database) that complements this repo's algorithms focus for full interview readiness.

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 comprehensive unit tests for src/sort implementations

The repo has src/sort/heap_sort.go and heap_sort_test.go, but lacks tests for other sorting algorithms mentioned in basic_algorithm/sort.md. This is a critical gap since sorting is a fundamental algorithm pattern that beginners need to verify works correctly. Adding tests ensures the code examples are reliable and maintainable.

• [ ] Review basic_algorithm/sort.md to identify all sorting algorithms covered (quicksort, mergesort, bubblesort, etc.)
• [ ] Create corresponding .go files in src/sort/ for missing algorithms (e.g., quick_sort.go, merge_sort.go, bubble_sort.go)
• [ ] Write comprehensive unit tests for each sorting algorithm in src/sort/*_test.go files covering edge cases (empty arrays, single element, duplicates, already sorted)
• [ ] Verify all tests pass and add a test summary to README.md showing code coverage

Add Go implementation files and tests for data_structure modules

The data_structure/ folder contains markdown documentation for binary_tree.md, linked_list.md, and stack_queue.md, but there are no corresponding Go implementation files in src/. Contributors can add practical, tested implementations that directly correspond to the theoretical documentation, making the learning resource executable.

• [ ] Create src/data_structure/ directory to mirror data_structure/ documentation
• [ ] Implement binary_tree.go with Node struct and basic tree operations (insert, traverse, search) referenced in data_structure/binary_tree.md
• [ ] Implement linked_list.go with Node struct and operations (append, delete, reverse) from data_structure/linked_list.md
• [ ] Implement stack_queue.go with both Stack and Queue implementations from data_structure/stack_queue.md
• [ ] Write comprehensive unit tests for each data structure in *_test.go files

Add GitHub Actions CI workflow for Go code validation

With Go source code in src/, there's no automated CI pipeline to ensure code quality and test reliability. This is critical for a learning resource where contributors will submit code. A CI workflow will catch bugs early and maintain quality standards.

• [ ] Create .github/workflows/go-test.yml with Go setup, build, and test steps
• [ ] Configure the workflow to run on push and pull requests for any changes to src/ directory
• [ ] Add go vet and gofmt checks to enforce code style consistency
• [ ] Update README.md with a build status badge linking to the workflow
• [ ] Document in CONTRIBUTING.md (create if missing) that all Go code must pass CI checks before merging

• Add test coverage for remaining algorithm implementations in src/ (e.g., implement and test binary search, merge sort, quicksort alongside existing heap sort) to match markdown templates.
• Create visual flowchart images for binary search (referenced in basic_algorithm/binary_search.md) and sliding window (advanced_algorithm/slide_window.md) analogous to existing images/backtrack.png and images/dp_*.png.
• Translate TODO.md into tracked issues or add execution checklist to README (e.g., '□ Complete basic_algorithm section in X weeks', '□ Solve all recommended LeetCode explore problems') to help users track progress.

This repository is an educational algorithm learning resource with minimal security risks. It is a documentation and code template repository focused on algorithm patterns and problem-solving techniques. No critical vulnerabilities were identified. The codebase contains markdown documentation, Go source code examples, and images. There are no external dependencies listed, no hardcoded credentials detected, no database interactions, and no web application attack surfaces. The main considerations are general best practices for open-source educational projects.

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.