Best use case
code-template-manager is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Manage and generate competitive programming templates
Teams using code-template-manager should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/code-template-manager/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How code-template-manager Compares
| Feature / Agent | code-template-manager | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Manage and generate competitive programming templates
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Code Template Manager Skill
## Purpose
Manage and generate competitive programming code templates for various algorithms, data structures, and common patterns across multiple languages.
## Capabilities
- Store and retrieve algorithm templates (C++, Python, Java)
- Fast I/O templates for different languages
- Data structure templates (segment tree, DSU, etc.)
- Template customization and versioning
- Generate problem-specific boilerplate
- Maintain personal template library
## Target Processes
- cp-library-creation
- codeforces-contest
- algorithm-implementation
- atcoder-contest
## Template Categories
1. **I/O Templates**: Fast input/output for each language
2. **Data Structures**: Segment tree, Fenwick tree, DSU, Treap, etc.
3. **Graph Algorithms**: DFS, BFS, Dijkstra, Bellman-Ford, etc.
4. **Number Theory**: Modular arithmetic, prime sieve, FFT/NTT
5. **String Algorithms**: KMP, Z-function, Suffix array, Hashing
6. **Geometry**: Point, Line, Polygon primitives
## Input Schema
```json
{
"type": "object",
"properties": {
"action": {
"type": "string",
"enum": ["get", "list", "create", "update", "generateBoilerplate"]
},
"templateName": { "type": "string" },
"language": { "type": "string", "enum": ["cpp", "python", "java"] },
"category": { "type": "string" },
"code": { "type": "string" }
},
"required": ["action"]
}
```
## Output Schema
```json
{
"type": "object",
"properties": {
"success": { "type": "boolean" },
"template": { "type": "string" },
"templates": { "type": "array" },
"error": { "type": "string" }
},
"required": ["success"]
}
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