.claude Skill
Skills and conventions for an educational algorithms and data structures repository.
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Why use this skill
.claude is most useful when you want an agent workflow that is more structured than an ad-hoc prompt. Instead of restating the same expectations every time, a dedicated SKILL.md file gives the assistant a repeatable brief. In this case, the core value is clarity: the repo already frames the workflow around utility skills tasks, and the skill source gives you a portable starting point you can evaluate, adapt, and reuse. The inferred platform for this skill is Claude Code Skills, which helps you judge whether it is likely to feel native in your current agent ecosystem or whether it is better treated as a general reference.
That matters because AI assistants are better when the operating context is explicit. A good skill turns hidden team expectations into visible instructions. It can name preferred tools, describe failure modes, define what “done” looks like, and reduce the amount of corrective prompting you need after the first draft. For developers exploring the wider SKILL.md ecosystem, this page helps answer the practical question: is this skill specific and maintained enough to be worth trying?
How to evaluate and use it
Start with the source repo and the preview below. The preview tells you whether the instructions are actionable or just aspirational. Strong skills usually describe triggers, recommended tools, steps, and known pitfalls. Weak skills tend to stay generic. This one lives in williamfiset/Algorithms, which gives you a concrete repo context, update history, and direct ownership trail.
Once you confirm the scope looks right, test it on a small task before making it part of a larger workflow. If it improves consistency, keep it. If it is too broad, outdated, or conflicts with your own process, treat it as a reference rather than a drop-in rule. That is the healthiest way to use directory-discovered skills: not as magic plugins, but as reusable operational knowledge that still deserves judgment.
SKILL.md preview
Previewing the source is one of the fastest ways to judge whether a skill is truly useful. This snippet comes from the public file in the linked repository.
--- name: algorithms-education description: > Skills and conventions for an educational algorithms and data structures repository. Use this skill whenever working on algorithm implementations, data structure code, LeetCode-style problems, graph theory, dynamic programming, or any Java-based educational coding project. Trigger on mentions of: algorithms, data structures, graph theory, sorting, searching, trees, DP, BFS, DFS, linked lists, heaps, segment trees, union-find, or any request to write, refactor, document, or test educational code. Also trigger when the user asks to "clean up", "simplify", "document", "refactor" or "add tests" to algorithm code. --- # Algorithms Education Skills This skill defines the conventions and standards for an educational algorithms repository. The goal is to make every algorithm implementation clear, well-tested, and accessible to learners who may not have deep CS backgrounds. --- ## Skill 1: Code Documentation **Goal:** Every file should teach, not just implement. ### Method-Level Documentation Every public method gets a doc comment that explains: 1. **What** the method does (in plain English, one sentence) 2. **How** it works (brief description of the approach/algorithm) 3. **Parameters** — what each input represents 4. **Returns** — what the output means 5. **Time/Space complexity** — always include Big-O ```java /** * Finds the shortest path from a source node to all other nodes * using Bellman-Ford's algorithm. Unlike Dijkstra's, this handles * negative edge weights and detects negative cycles. * * @param g ...