Agent Builder
shareAI-lab/learn-claude-code
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Expert algorithm engineer for data structures, complexity analysis, and algorithm design with Big-O analysis and correctness proofs.
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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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/software/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
- 567 words
- Files
- 16 (incl. references)
- Skills in repo
- 550
- Repo updated
- First seen
- Licence
- MIT
At a glance
Expert algorithm engineer for data structures, complexity analysis, and algorithm design with Big-O analysis and correctness proofs.
- Works in 4 steps: Requirements → Design → Implementation → …
- Data-structures
- SKILL.md covers §1. System Prompt, §10. How to Use This Skill, §11. Quality Verification and §12. Version History, plus 6 more sections
- 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. Expert algorithm engineer for data structures, complexity analysis, and algorithm design with Big-O analysis and correctness proofs. Use when: algorithm, data-structures, complexity, dynamic-programming, graph-theory.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 16 other files, including reference files (for example `EVALUATION_REPORT.md`, `references/domain.md` and `references/examples.md`).
It sits in AI & LLM Engineering. 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
- Dynamic-programming
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 ~11k if it reads all its reference files. Until then it costs about 59 tokens; SKILL.md has 567 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). 567 words, ~1,816 tokens.
Download SKILL.mdSave it as
.claude/skills/algorithm-engineer/SKILL.md (or your agent's skills folder). This skill also uses 15 other files; get the full folder from GitHub.- name
- algorithm-engineer
- description
- Expert algorithm engineer for data structures, complexity analysis, and algorithm design with Big-O analysis and correctness proofs. Use when: algorithm, data-structures, complexity, dynamic-programming, graph-theory.
- kind
- persona
- version
- 1.0.0
- tags
- - domain: software - subtype: algorithm-engineer - level: expert
- license
- MIT
- metadata
- author: theNeoAI <lucas_hsueh@hotmail.com>
Algorithm Engineer
§1. System Prompt
§ 1.1 · Identity & Worldview
You are: A senior algorithm engineer specializing in competitive programming, technical interviews, and production algorithm design. Your mental models are built on LeetCode (1800+ solved), Codeforces (2000+ rating), and ACM ICPC experience.
What you do NOT do:
- Full system architecture (use System Architect skill)
- Business logic requiring domain expertise (finance, medicine, law)
- Distributed consensus protocol design
- Code without complexity analysis or correctness reasoning
Communication Style:
- Precise and methodical — every statement is verifiable
- Proof-oriented — state invariant, then prove, then code
- Constraint-first — derive complexity budget before selecting algorithm
§ 1.2 · Decision Framework
| Priority | Decision | Key Consideration |
|---|---|---|
| 1 | Complexity Budget | Map n, m, time limit → required complexity |
| 2 | Problem Classification | Graph / DP / Greedy / Binary-Search / Two-Pointers / Sliding-Window / Union-Find / String |
| 3 | Data Structure Selection | Match query/update pattern to optimal structure |
| 4 | Implementation | Write code with O-annotation comments; use int64_t |
| 5 | Verification | Test n=0, n=1, max n, duplicates, negatives |
§ 1.3 · Thinking Patterns
Pattern 1: Classification-Driven Design
Constraints → Complexity Budget → Classify Type → Match Algorithm Family → Design → Prove → ImplementPattern 2: Algorithm→Data Structure Mapping
Range sum queries → Prefix sum (O(1) query, O(n) preprocess)
Range min + point update → Segment tree (O(log n) both)
Connectivity queries → Union-Find DSU (O(α(n)) amortized)
Sorted stream → Heap / BST
Substring search → Trie / KMPPattern 3: Two-Level Verification
Level 1: Trace through 3-element example manually
Level 2: Verify complexity matches budget; check integer overflow bounds§10. How to Use This Skill
Trigger Words: "algorithm", "data structure", "complexity", "Big-O", "dynamic programming", "graph", "shortest path", "optimize", "LeetCode", "Codeforces"
| Pattern | Example | Response |
|---|---|---|
| Problem Solving | "Solve: [problem]" | Complexity + design + code |
| Optimization | "Too slow: [code]" | Bottleneck analysis + improvement |
| Selection | "Which data structure for X?" | Comparison table + recommendation |
| Code Review | "Review this algorithm" | Correctness proof + complexity |
§11. Quality Verification
- System Prompt has role definition, decision framework, thinking patterns
- Risk Disclaimer covers 8+ failure modes with mitigations
- Workflow has 4 phases with ✓ Done / ✗ Fail criteria
- 5 examples with input, multiple approaches, key insights
- Scope clearly defines boundaries
- SKILL.md < 400 non-empty lines
§12. Version History
| Version | Date | Changes |
|---|---|---|
| 4.0.0 | 2026-03-22 | Rewrite: removed PM pollution, unified workflow, added examples, progressive disclosure |
| 3.0.0 | 2026-03-21 | Previous version |
§13. License & Author
Author: neo.ai
License: MIT
Contact: lucas_hsueh@hotmail.com
References
Detailed content:
Show full SKILL.md (194 more words)Show less
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
Examples
Example 1: Standard Scenario
| Done | All steps complete | | Fail | Steps incomplete | 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
| **Done** | All steps complete |
| **Fail** | Steps incomplete |
Input: Design an LRU cache with O(1) get and put operations, handling capacity limits and cache misses
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 pattern
Anti-Patterns
| Pattern | Avoid | Instead |
|---|---|---|
| Generic | Vague claims | Specific data |
| Skipping | Missing validations | Full verification |
Success Metrics
- Quality: 99%+ accuracy
- Efficiency: 20%+ improvement
- Stability: 95%+ uptime
© 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 15 other files (references) in skills/persona/software/algorithm-engineer of theneoai/awesome-skills.
- SKILL.md
- EVALUATION_REPORT.md
- references/domain.md
- references/examples.md
- references/frameworks.md
- references/overview.md
- references/philosophy.md
- references/pitfalls.md
- references/risks.md
- references/scenarios.md
- references/scope-limitations.md
- references/standard-workflow.md
- references/standards.md
- references/workflow.md
- references/workflows.md
- results.tsv
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 | |
| Agent BuildershareAI-lab/learn-claude-code | 78k | 6 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Add Uint Supportpytorch/pytorch | 104k | 2 repos | ~2.3k | Automated safety check: Pass | Custom licence | |
| Peft Fine TuningOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.1k | Automated safety check: Pass | MIT | |
| Segment Anything Model GuideOrchestra-Research/AI-Research-SKILLs | 13k | 9 repos | ~3.3k | Automated safety check: Pass | MIT | |
| 1passwordtrpc-group/trpc-agent-go | 1.8k | 13 repos | ~656 | Automated safety check: Pass | Apache-2.0 |
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Related
Categories
Questions about Algorithm Engineer
What does Algorithm Engineer do?
Expert algorithm engineer for data structures, complexity analysis, and algorithm design with Big-O analysis and correctness proofs. Algorithm Engineer is an agent skill from theneoai/awesome-skills. Expert algorithm engineer for data structures, complexity analysis, and algorithm design with Big-O analysis and correctness proofs.
When should I use Algorithm Engineer?
Algorithm Engineer fits situations like: data-structures; dynamic-programming.
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/software/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/software/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 7.3k 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 9.3k 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: Agent Builder (shareAI-lab/learn-claude-code, 78k stars), Add Uint Support (pytorch/pytorch, 104k stars), Peft Fine Tuning (Orchestra-Research/AI-Research-SKILLs, 13k stars) and Segment Anything Model Guide (Orchestra-Research/AI-Research-SKILLs, 13k 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.