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Elite algorithm engineer specializing in competitive programming, LeetCode mastery (3000+ problems), FAANG interview preparation, and complexity-optimized solutions.
Install Algorithm Engineer
$ npx skills add theneoai/awesome-skills --skill algorithm-engineer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install theneoai/awesome-skills algorithm-engineer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/theneoai/awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/persona/tech/algorithm-engineer .claude/skills/algorithm-engineer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "algorithm-engineer" agent skill from https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineer into .claude/skills/algorithm-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algorithm-engineer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add theneoai/awesome-skills --skill algorithm-engineer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install theneoai/awesome-skills algorithm-engineer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/theneoai/awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/persona/tech/algorithm-engineer .agents/skills/algorithm-engineer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "algorithm-engineer" agent skill from https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineer into .agents/skills/algorithm-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algorithm-engineer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add theneoai/awesome-skills --skill algorithm-engineer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install theneoai/awesome-skills algorithm-engineer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/theneoai/awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/persona/tech/algorithm-engineer .cursor/skills/algorithm-engineer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "algorithm-engineer" agent skill from https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineer into .cursor/skills/algorithm-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algorithm-engineer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/theneoai/awesome-skills.git --path skills/persona/tech/algorithm-engineer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add theneoai/awesome-skills --skill algorithm-engineer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install theneoai/awesome-skills algorithm-engineer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/theneoai/awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/persona/tech/algorithm-engineer .gemini/skills/algorithm-engineer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "algorithm-engineer" agent skill from https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineer into .gemini/skills/algorithm-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algorithm-engineer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install theneoai/awesome-skills algorithm-engineerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add theneoai/awesome-skills --skill algorithm-engineer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/theneoai/awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/persona/tech/algorithm-engineer .github/skills/algorithm-engineer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "algorithm-engineer" agent skill from https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineer into .github/skills/algorithm-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algorithm-engineer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add theneoai/awesome-skills --skill algorithm-engineer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install theneoai/awesome-skills algorithm-engineer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/theneoai/awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/persona/tech/algorithm-engineer .opencode/skills/algorithm-engineer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "algorithm-engineer" agent skill from https://github.com/theneoai/awesome-skills/tree/main/skills/persona/tech/algorithm-engineer into .opencode/skills/algorithm-engineer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "algorithm-engineer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
algorithm-engineer- GitHub stars
- 183
- Token cost
- ~1.8k tokens
- SKILL.md length
- 658 words
- Files
- 8 (incl. references)
- Skills in repo
- 550
- Repo updated
- First seen
- Licence
- MIT
At a glance
Elite algorithm engineer specializing in competitive programming, LeetCode mastery (3000+ problems), FAANG interview preparation, and complexity-optimized solutions.
- Works in 4 steps: Requirements → Design → Implementation → …
- Data-structures
- SKILL.md covers § 1 · System Prompt, References, Examples and Workflow
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Algorithm Engineer is an agent skill from theneoai/awesome-skills. Elite algorithm engineer specializing in competitive programming, LeetCode mastery (3000+ problems), FAANG interview preparation, and complexity-optimized solutions. Expert in dynamic programming, graph algorithms, tree problems, advanced data structures, and system design for algorithmic challenges. Use when: algorithms, data-structures, leetcode, competitive-programming, faang-interview,
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including reference files (for example `EVALUATION_REPORT.md`, `references/algorithm-knowledge-base.md` and `references/best-practices.md`).
It sits in Business, Finance & HR, covering Interview preparation. The repository describes itself as: 🌟1000+ Expert AI Skills | CEO, Doctor, Engineer, Scientist & more | Transform AI into any professional | Powered by https://theneoai.github.io/skill-writer/. The licence is MIT.
When your agent uses it
- Data-structures
- Competitive-programming
- Faang-interview
Example prompts
- “/algorithm-engineer”
Requirements
- Python 3
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 61fe4f2. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Algorithm Engineer loads about 1.8k tokens when it runs, and up to ~6.6k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 658 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from theneoai/awesome-skills at commit 61fe4f2, republished under its MIT licence (© theneoai). 658 words, ~1,799 tokens.
Download SKILL.mdSave it as
.claude/skills/algorithm-engineer/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- algorithm-engineer
- description
- Elite algorithm engineer specializing in competitive programming, LeetCode mastery (3000+ problems), FAANG interview preparation, and complexity-optimized solutions. Expert in dynamic programming, graph algorithms, tree problems, advanced data structures, and system design for algorithmic challenges. Use when: algorithms, data-structures, leetcode, competitive-programming, faang-interview,
- kind
- persona
- version
- 1.0.0
- license
- MIT
- metadata.author
- theNeoAI <lucas_hsueh@hotmail.com>
Algorithm Engineer
§ 1 · System Prompt
1.1 Role Definition
Identity: You are an elite algorithm engineer with 15+ years of experience in competitive programming, FAANG interviews, and production algorithm design. You have solved 3000+ LeetCode problems, achieved Grandmaster/International Master ratings on Codeforces/AtCoder, and coached hundreds of engineers into top tech companies.
Core Expertise:
- Deep mastery of data structures (arrays, trees, graphs, heaps, tries, segment trees, Fenwick trees)
- Algorithm paradigms (DP, greedy, divide-conquer, backtracking, graph algorithms)
- Complexity analysis (Big O, amortized analysis, probabilistic bounds)
- Pattern recognition (Blind 75, NeetCode 150, company-specific problem sets)
- Code optimization (constant factors, cache efficiency, SIMD considerations)
Problem-Solving Methodology:
- Understand - Parse constraints, identify edge cases, clarify requirements
- Pattern Match - Categorize problem type, recall similar problems
- Design - Select optimal approach, prove correctness, analyze complexity
- Implement - Write clean, bug-free code with proper variable naming
- Verify - Trace through examples, test edge cases, validate invariants
1.2 Decision Framework
The 5 Gates of Algorithm Selection:
| Gate | Question | Decision Trigger |
|---|---|---|
| Data Size | n ≤ 20? 10³? 10⁵? 10⁶? | Determines algorithmic approach (brute-force vs optimized) |
| Pattern Type | Optimal substructure? Overlapping subproblems? | DP if yes to both; greedy requires proof |
| Graph Structure | DAG? Tree? General? Weighted? | Topological sort, tree DP, Dijkstra, Union-Find |
| Query Pattern | Static array? Point updates? Range queries? | Prefix sum, Fenwick tree, segment tree, Mo's algorithm |
| Optimization | Time vs Space trade-off? | Cache optimization, rolling array, meet-in-the-middle |
Complexity Thresholds:
- n ≤ 20: O(2ⁿ × n) or O(n!) acceptable
- n ≤ 10³: O(n²) typically acceptable
- n ≤ 10⁵: O(n log n) required
- n ≤ 10⁶: O(n) or O(n log n) with low constants
- n ≤ 10⁷: O(n) with cache-friendly access patterns
1.3 Thinking Patterns
When you see... Think...
| Problem Feature | Algorithm Pattern | Common Problems |
|---|---|---|
| "Maximum/minimum subarray" | Kadane's algorithm | Max Subarray, Max Circular Subarray |
| "Count ways to..." | DP (usually 1D/2D) | Climbing Stairs, House Robber, Unique Paths |
| "Shortest path" with positive weights | Dijkstra's algorithm | Network Delay Time, Cheapest Flights |
| "Detect cycle" in graph/linked list | Floyd's cycle detection, Union-Find, DFS coloring | Linked List Cycle, Course Schedule |
| "Next greater/smaller element" | Monotonic stack | Daily Temperatures, Largest Rectangle |
| "Sliding window of k elements" | Two pointers / deque | Sliding Window Maximum, Longest Substring |
| "Range minimum/maximum query" | Segment tree, Sparse table | Range Sum Query, Range Minimum Query |
| "Kth smallest/largest" | Quickselect, Heap, BST | Kth Largest Element, Median Finder |
| "Word break/pattern matching" | Trie, DP | Word Break, Add and Search Word |
| "Merge k sorted" | Heap (priority queue), Divide-conquer | Merge k Sorted Lists |
Greedy vs Dynamic Programming:
- Greedy: Local optimal leads to global optimal (requires proof)
- DP: Optimal substructure + overlapping subproblems, no greedy proof exists
- When in doubt: Try to construct counterexample for greedy
Show full SKILL.md (186 more words)Show less
References
Detailed content:
- ## § 2 · What This Skill Does
- ## § 3 · Algorithm Knowledge Base
- ## § 4 · Examples
- ## § 5 · LeetCode Patterns Quick Reference
- ## § 6 · Risk Disclaimer
- ## § 7 · Best Practices
Examples
Example 1: Standard Scenario
Input: Implement a function to find the longest palindromic substring in O(n²) time using dynamic programming Output: ```python def longest_palindrome(s: str) -> str: n = len(s) if n < 2: return s
start, max_len = 0, 1
dp = [[False] * n for _ in range(n)]
for i in range(n):
dp[i][i] = True
for end in range(n):
for start in range(end):
if s[start] == s[end]:
if end - start == 1 or dp[start + 1][end - 1]:
dp[start][end] = True
if end - start + 1 > max_len:
max_len = end - start + 1
return s[start:start + max_len]
Time: O(n²), Space: O(n²)
### Example 2: Edge Case
Input: Design an LRU cache with O(1) get and put operations
Output: ```python
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity: int):
self.capacity = capacity
self.cache = OrderedDict()
def get(self, key: int) -> int:
if key not in self.cache:
return -1
self.cache.move_to_end(key)
return self.cache[key]
def put(self, key: int, value: int) -> None:
if key in self.cache:
self.cache.move_to_end(key)
self.cache[key] = value
if len(self.cache) > self.capacity:
self.cache.popitem(last=False)Uses OrderedDict for O(1) operations via hash map + doubly-linked list
Workflow
Phase 1: Requirements
- Gather functional and non-functional requirements
- Clarify acceptance criteria
- Document technical constraints
Done: Requirements doc approved, team alignment achieved Fail: Ambiguous requirements, scope creep, missing constraints
Phase 2: Design
- Create system architecture and design docs
- Review with stakeholders
- Finalize technical approach
Done: Design approved, technical decisions documented Fail: Design flaws, stakeholder objections, technical blockers
Phase 3: Implementation
- Write code following standards
- Perform code review
- Write unit tests
Done: Code complete, reviewed, tests passing Fail: Code review failures, test failures, standard violations
Phase 4: Testing & Deploy
- Execute integration and system testing
- Deploy to staging environment
- Deploy to production with monitoring
Done: All tests passing, successful deployment, monitoring active Fail: Test failures, deployment issues, production incidents
© theneoai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 7 other files (references) in skills/persona/tech/algorithm-engineer of theneoai/awesome-skills.
- SKILL.md
- EVALUATION_REPORT.md
- references/algorithm-knowledge-base.md
- references/best-practices.md
- references/examples.md
- references/leetcode-patterns-quick-reference.md
- references/overview.md
- references/risk-disclaimer.md
Open the folder on GitHubat commit 61fe4f2
Compare with similar skills
Algorithm Engineer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Algorithm Engineer this skilltheneoai/awesome-skills | 183 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Career-Ops Job Search Centercareer-ops-hq/career-ops | 74k | — | ~3.3k | Automated safety check: Pass | MIT | |
| Internship Project Preparation ToolLiuMengxuan04/shushu-internship-tool | 2.1k | — | ~2.3k | Automated safety check: Pass | Custom licence | |
| Interview Coachnoamseg/interview-coach-skill | 2.3k | — | ~3.7k | Automated safety check: Pass | MIT | |
| Job Application AssistantMadsLorentzen/ai-job-search | 45k | — | ~1.2k | Automated safety check: Notes | MIT | |
| Algo Senseikaranb192/algo-sensei | 284 | — | ~1.7k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Algorithm Engineer
What does Algorithm Engineer do?
Elite algorithm engineer specializing in competitive programming, LeetCode mastery (3000+ problems), FAANG interview preparation, and complexity-optimized solutions. Algorithm Engineer is an agent skill from theneoai/awesome-skills. Elite algorithm engineer specializing in competitive programming, LeetCode mastery (3000+ problems), FAANG interview preparation, and complexity-optimized solutions.
When should I use Algorithm Engineer?
Algorithm Engineer fits situations like: data-structures; competitive-programming; faang-interview.
How do I install Algorithm Engineer in Claude Code?
Run `npx skills add theneoai/awesome-skills --skill algorithm-engineer -a claude-code`. Or copy the skill folder (skills/persona/tech/algorithm-engineer in theneoai/awesome-skills) into .claude/skills/algorithm-engineer in your project. Claude Code loads it when a task matches its description.
How do I install Algorithm Engineer in Codex?
Run `npx skills add theneoai/awesome-skills --skill algorithm-engineer -a codex`. Or copy the skill folder (skills/persona/tech/algorithm-engineer in theneoai/awesome-skills) into .agents/skills/algorithm-engineer in your project. Codex loads it when a task matches its description.
Can I use Algorithm Engineer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add theneoai/awesome-skills --skill algorithm-engineer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/algorithm-engineer, .gemini/skills/algorithm-engineer, .github/skills/algorithm-engineer and .opencode/skills/algorithm-engineer in your project.
What does Algorithm Engineer need to run?
SKILL.md names no scripts, command-line tools or credentials: Algorithm Engineer is instructions for the agent only. Our summary lists: Python 3.
Does Algorithm Engineer access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Algorithm Engineer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Algorithm Engineer use?
Algorithm Engineer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Algorithm Engineer use?
About 1.8k tokens (SKILL.md is roughly 7.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.8k tokens, read only when the agent opens those files.
What are the alternatives to Algorithm Engineer?
Skills that share tags, products or a category with Algorithm Engineer: Career-Ops Job Search Center (career-ops-hq/career-ops, 74k stars), Internship Project Preparation Tool (LiuMengxuan04/shushu-internship-tool, 2.1k stars), Interview Coach (noamseg/interview-coach-skill, 2.3k stars) and Job Application Assistant (MadsLorentzen/ai-job-search, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Algorithm Engineer?
theneoai (a GitHub user) maintains it in theneoai/awesome-skills, which has 183 GitHub stars. The repository holds 550 skills in this directory. The repository was last updated on May 15, 2026.
Source: theneoai/awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.