Finishing a Development Branch
obra/superpowers
Walks the last step of a branch: confirm tests pass, detect the git environment, ask how to integrate, carry out your choice and clean up the worktree.
Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy…
Install Computer Science Algorithms
$ npx skills add pproenca/dot-skills --skill computer-science-algorithms -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install pproenca/dot-skills computer-science-algorithms --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/pproenca/dot-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.experimental/computer-science-algorithms .claude/skills/computer-science-algorithms && 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 "computer-science-algorithms" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithms into .claude/skills/computer-science-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computer-science-algorithms", 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/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithmsType 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 pproenca/dot-skills --skill computer-science-algorithms -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install pproenca/dot-skills computer-science-algorithms --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.experimental/computer-science-algorithms .agents/skills/computer-science-algorithms && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "computer-science-algorithms" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithms into .agents/skills/computer-science-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computer-science-algorithms", 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 pproenca/dot-skills --skill computer-science-algorithms -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install pproenca/dot-skills computer-science-algorithms --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.experimental/computer-science-algorithms .cursor/skills/computer-science-algorithms && 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 "computer-science-algorithms" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithms into .cursor/skills/computer-science-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computer-science-algorithms", 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/pproenca/dot-skills.git --path skills/.experimental/computer-science-algorithms--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 pproenca/dot-skills --skill computer-science-algorithms -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install pproenca/dot-skills computer-science-algorithms --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.experimental/computer-science-algorithms .gemini/skills/computer-science-algorithms && 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 "computer-science-algorithms" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithms into .gemini/skills/computer-science-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computer-science-algorithms", 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 pproenca/dot-skills computer-science-algorithmsInstalls 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 pproenca/dot-skills --skill computer-science-algorithms -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.experimental/computer-science-algorithms .github/skills/computer-science-algorithms && 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 "computer-science-algorithms" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithms into .github/skills/computer-science-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computer-science-algorithms", 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 pproenca/dot-skills --skill computer-science-algorithms -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install pproenca/dot-skills computer-science-algorithms --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/pproenca/dot-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.experimental/computer-science-algorithms .opencode/skills/computer-science-algorithms && 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 "computer-science-algorithms" agent skill from https://github.com/pproenca/dot-skills/tree/master/skills/.experimental/computer-science-algorithms into .opencode/skills/computer-science-algorithms/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "computer-science-algorithms", 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
computer-science-algorithms- GitHub stars
- 214
- Token cost
- ~3.3k tokens
- SKILL.md length
- 886 words
- Files
- 56 (incl. references, assets)
- Skills in repo
- 182
- Repo updated
- First seen
- Licence
- MIT
At a glance
Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy…
- Works in 9 steps: Asymptotic Complexity & Algorithm… → Data Structure Selection (CRITICAL) → Sorting & Searching (HIGH) → …
- Tasks involving whats the right algorithm for…
- SKILL.md covers When to Apply, Rule Categories By Priority, Quick Reference and How to Use, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Computer Science Algorithms is an agent skill from pproenca/dot-skills. Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy algorithms, string/sequence algorithms, and the at-scale toolbox (Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, consistent hashing, external merge sort, Aho-Corasick, MinHash/LSH). Trigger on tasks involving "what's the right algorithm for…", performance-critical code, code with nested loops over the same…
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 58 other files, including reference files and assets (for example `AGENTS.md`, `assets/templates/_template.md` and `metadata.json`).
It sits in Development. The repository describes itself as: A collection of AI agent skills following the Agent Skills open format. The licence is MIT.
When your agent uses it
- Tasks involving whats the right algorithm for…
- Performance-critical code
- Code with nested loops over the same input
- Recursive solutions
Example prompts
- “s the right algorithm for…”
- “how do I do X at scale / on a stream / without enough RAM”
- “t explicitly mention”
- “/computer-science-algorithms”
Workflow steps
9 steps, taken from the step headings in SKILL.md.
- Asymptotic Complexity & Algorithm Selection (CRITICAL)
- Data Structure Selection (CRITICAL)
- Sorting & Searching (HIGH)
- Dynamic Programming (HIGH)
- Graph Algorithms (HIGH)
- Divide & Conquer / Recursion (MEDIUM-HIGH)
- Greedy Algorithms (MEDIUM)
- String & Sequence Algorithms (MEDIUM)
- Scale & Probabilistic Algorithms (MEDIUM)
What it can do on your machine
Read from SKILL.md and the folder at commit cf93c57. 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
Links to these hosts (documentation or services it may open):
cp-algorithms.comusaco.guide
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
Computer Science Algorithms loads about 3.3k tokens when it runs, and up to ~40k if it reads all its reference files. Until then it costs about 205 tokens; SKILL.md has 886 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 pproenca/dot-skills at commit cf93c57, republished under its MIT licence (© pproenca). 886 words, ~3,265 tokens.
Download SKILL.mdSave it as
.claude/skills/computer-science-algorithms/SKILL.md (or your agent's skills folder). This skill also uses 55 other files; get the full folder from GitHub.- name
- computer-science-algorithms
- description
- Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy algorithms, string/sequence algorithms, and the at-scale toolbox (Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, consistent hashing, external merge sort, Aho-Corasick, MinHash/LSH). Trigger on tasks involving "what's the right algorithm for…", performance-critical code, code with nested loops over the same input, recursive solutions, shortest-path / scheduling / matching / DP problems, code review for accidental O(n²) blowup, and any "how do I do X at scale / on a stream / without enough RAM" question — even if the user doesn't explicitly mention "algorithm" or "complexity."
Community Classical Computer Science Algorithms Best Practices
A practitioner-oriented reference for choosing and implementing classical algorithms and data structures correctly. Organized by execution-lifecycle impact: the earliest decisions (asymptotic class, data-structure choice) cascade through everything else, so the rules near the top of the table matter most.
Scope: the patterns that show up in everyday production code review, reasonable interview / contest problems, and the at-scale toolbox (sketches, streaming, distributed primitives) — not an exhaustive cover of CLRS. Topics intentionally outside the current version: network flow, modular arithmetic, Bellman-Ford and Floyd-Warshall as standalone rules, SCC (Tarjan/Kosaraju), computational geometry, FFT, Manacher / Z-function as standalone rules. They're flagged inline in the relevant rules.
Distilled from CLRS (Introduction to Algorithms, 4th ed.), Sedgewick & Wayne (Algorithms, 4th ed., Princeton), Skiena's Algorithm Design Manual, Laaksonen's Competitive Programmer's Handbook, cp-algorithms.com, and the USACO Guide.
When to Apply
Use these rules when:
- Choosing an algorithm or data structure for a new problem ("what's the right way to do X?")
- Reviewing code for hidden O(n²) blowup — repeated
in-checks on lists,pop(0)on lists, string concatenation in loops, naive substring search - Picking a DP state or recurrence, before writing the memoization
- Modeling a problem as a graph (BFS vs Dijkstra vs topological sort)
- Refactoring brute force / naive solutions that work on toy inputs but time out at scale
- Deciding whether greedy applies, or whether DP / branch-and-bound is required
Rule Categories By Priority
| # | Category | Prefix | Impact | Why it cascades |
|---|---|---|---|---|
| 1 | Asymptotic Complexity & Algorithm Selection | comp- | CRITICAL | Wrong O() class makes every other optimization irrelevant |
| 2 | Data Structure Selection | ds- | CRITICAL | The container determines which operations are cheap |
| 3 | Sorting & Searching | srch- | HIGH | Foundation for greedy, two-pointer, sweep-line, binary-search-on-the-answer |
| 4 | Dynamic Programming | dp- | HIGH | Exponential → polynomial transformations |
| 5 | Graph Algorithms | graph- | HIGH | Networks, dependencies, routing, scheduling all reduce to graphs |
| 6 | Divide & Conquer / Recursion | divide- | MEDIUM-HIGH | Logarithmic-factor speedups; stack-depth and recurrence traps |
| 7 | Greedy Algorithms | greedy- | MEDIUM | Fast when correct, silently wrong when not |
| 8 | String & Sequence Algorithms | str- | MEDIUM | Pattern matching, parsing, substring queries |
| 9 | Scale & Probabilistic Algorithms | scale- | MEDIUM | Sketches, streaming, distributed primitives — situational, decisive when they apply |
Quick Reference
1. Asymptotic Complexity & Algorithm Selection (CRITICAL)
comp-pick-algorithm-class-from-input-bound— Match O() to n before writing codecomp-amortize-instead-of-worst-casing— Total cost, not per-op worst casecomp-watch-for-quadratic-blowup-from-membership-in-list— Linearinchecks in loops are O(n²)comp-prefer-iterative-builders-over-string-concatenation— Join / buffers, not+=comp-derive-recurrences-via-master-theorem— Write the recurrence before coding recursioncomp-treat-space-complexity-as-first-class— Memory kills services before time does
2. Data Structure Selection (CRITICAL)
ds-hash-map-for-keyed-lookup— Build the index once, then O(1) lookupsds-set-for-uniqueness-and-membership— Dedup and "have I seen this?" in O(1)ds-heap-for-top-k-and-priority-queues— O(n log k), priority-queue idiomsds-deque-for-both-end-operations— O(1) pop-front for BFS queues and sliding windowsds-balanced-bst-or-sorted-container-for-range-queries— Predecessor / successor / range scands-union-find-for-dynamic-connectivity— Near-O(1) grouping and mergingds-prefix-sums-for-repeated-range-sums— O(1) range sum after O(n) preprocessingds-fenwick-or-segment-tree-for-mutable-range-queries— O(log n) updates + queries
3. Sorting & Searching (HIGH)
srch-use-builtin-sort-not-hand-rolled— Timsort / introsort beat any hand-rollsrch-binary-search-on-sorted-data—bisect, plus binary search on the answersrch-quickselect-for-k-th-element— O(n) average for k-th / mediansrch-counting-and-radix-sort-for-bounded-integer-keys— Beat O(n log n) for integer keyssrch-two-pointers-on-sorted-data— O(n) on sorted arrays, sliding window
4. Dynamic Programming (HIGH)
dp-memoize-overlapping-subproblems—@cachecollapses exponentialsdp-tabulate-when-recursion-depth-or-order-matters— Bottom-up + rolling arraysdp-define-state-precisely— Underspecified state = silent wrong answersdp-knapsack-pattern— 0/1 vs unbounded; loop direction is correctnessdp-bitmask-for-small-set-states— n! → 2ⁿ · poly for n ≤ ~20dp-prove-optimal-substructure-before-coding— DP requires substructure; verify before coding
Show full SKILL.md (353 more words)Show less
5. Graph Algorithms (HIGH)
graph-bfs-for-unweighted-shortest-path— O(V+E), no heap neededgraph-dijkstra-for-non-negative-weights— Lazy-deletion heap variantgraph-topological-sort-for-dependency-order— Kahn's algorithm + DAG DPgraph-represent-as-adjacency-list-not-matrix— Sparse graphs need listsgraph-detect-cycles-during-dfs— Three-colour scheme for directed graphsgraph-kruskal-or-prim-for-mst— MST with Union-Find or heap
6. Divide & Conquer / Recursion (MEDIUM-HIGH)
divide-merge-sort-pattern-for-counting-inversions— Piggy-back counting onto the merge stepdivide-watch-recursion-depth-and-stack— Iterate, or raise the stackdivide-meet-in-the-middle-for-subset-problems— 2ⁿ → 2^(n/2)divide-quickselect-vs-quicksort-partitioning— Random pivots; 3-way Dutch flag
7. Greedy Algorithms (MEDIUM)
greedy-prove-exchange-argument-before-using— Greedy needs a correctness proofgreedy-sort-by-the-right-key-for-scheduling— Finish time, deadline, value/weightgreedy-interval-merge-and-sweep-line— Events + sort + linear sweepgreedy-huffman-and-priority-queue-greedies— Heap-based "pick smallest repeatedly"
8. String & Sequence Algorithms (MEDIUM)
str-kmp-or-builtin-find-not-naive-search— Linear worst-case substring searchstr-trie-for-prefix-queries— Autocomplete in O(|query|)str-rolling-hash-for-multiple-substring-comparisons— Two independent hashes, pleasestr-suffix-array-or-automaton-for-substring-queries— Heavy-duty substring tooling
9. Scale & Probabilistic Algorithms (MEDIUM)
The "unusual but valuable at scale" toolbox — sketches that trade tiny accuracy loss for orders-of-magnitude memory wins, streaming primitives for inputs that don't fit in RAM, and distributed structures that survive sharding changes.
scale-bloom-filter-for-probabilistic-membership— 1 bit/element vs 8 bytes, 1% false-positive ratescale-hyperloglog-for-cardinality-estimation— Count distinct over billions in ~12 KBscale-count-min-sketch-for-frequency-estimation— Heavy hitters and frequency queries in fixed memoryscale-reservoir-sampling-for-streams— Uniform k-sample from a stream of unknown lengthscale-consistent-hashing-for-distributed-sharding— Remap k/n keys (not all keys) on node changesscale-external-merge-sort-for-out-of-memory-data— Sort 1 TB on 8 GB of RAMscale-aho-corasick-for-multi-pattern-search— Find all of m patterns in one pass over textscale-minhash-lsh-for-near-duplicate-detection— Near-duplicate pairs in O(n), not O(n²)
How to Use
Start with the category that matches the question:
- "What's the right algorithm for n = 10⁶?" →
comp-(input-bound) - "I'm looking things up in a list inside a loop" →
ds-hash-map-for-keyed-lookuporcomp-watch-for-quadratic-blowup-from-membership-in-list - "My recursion is slow" →
dp-memoize-overlapping-subproblemsandcomp-derive-recurrences-via-master-theorem - "Shortest path / connectivity / ordering tasks" →
graph- - "Choose items to maximize value" → start with
greedy-prove-exchange-argument-before-using; fall back todp-knapsack-pattern - "Find / match strings" →
str- - "Memory is the constraint, not time" / "Sample / count / deduplicate at scale" / "Sharding" →
scale-
Code examples are in Python (most readable across audiences). The reasoning generalizes to any language — equivalent stdlib primitives are listed where they differ.
Reference Files
| File | Description |
|---|---|
| references/_sections.md | Category definitions and ordering |
| assets/templates/_template.md | Template for new rules |
| metadata.json | Version and reference information |
| AGENTS.md | Auto-built TOC navigation |
Related Skills
complexity-optimizer— Static analysis that finds the patterns these rules diagnose
© pproenca, 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 55 other files (references, assets) in skills/.experimental/computer-science-algorithms of pproenca/dot-skills.
- SKILL.md
- AGENTS.md
- assets/templates/_template.md
- metadata.json
- references/_sections.md
- references/comp-amortize-instead-of-worst-casing.md
- references/comp-derive-recurrences-via-master-theorem.md
- references/comp-pick-algorithm-class-from-input-bound.md
- references/comp-prefer-iterative-builders-over-string-concatenation.md
- references/comp-treat-space-complexity-as-first-class.md
- references/comp-watch-for-quadratic-blowup-from-membership-in-list.md
- references/divide-meet-in-the-middle-for-subset-problems.md
- references/divide-merge-sort-pattern-for-counting-inversions.md
- references/divide-quickselect-vs-quicksort-partitioning.md
- references/divide-watch-recursion-depth-and-stack.md
- references/dp-bitmask-for-small-set-states.md
- references/dp-define-state-precisely.md
- references/dp-knapsack-pattern.md
- … and 38 more
Open the folder on GitHubat commit cf93c57
Compare with similar skills
Computer Science Algorithms 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 |
|---|---|---|---|---|---|---|
| Computer Science Algorithms this skillpproenca/dot-skills | 214 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Finishing a Development Branchobra/superpowers | 296k | 5 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Typescript Advanced Typesrolling-scopes/rsschool-app | 10k | 24 repos | ~4.2k | Automated safety check: Pass | MPL-2.0 | |
| PR Babysitteropeninterpreter/openinterpreter | 69k | 3 repos | ~4.2k | Automated safety check: Pass | Apache-2.0 | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT | |
| Greplooponyx-dot-app/onyx | 32k | 4 repos | ~3.3k | Automated safety check: Pass | MIT |
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Categories
Questions about Computer Science Algorithms
What does Computer Science Algorithms do?
Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy…. Computer Science Algorithms is an agent skill from pproenca/dot-skills. Choosing or implementing an algorithm or data structure — asymptotic complexity, data-structure selection, sorting & searching, dynamic programming, graph algorithms, divide & conquer, greedy algorithms, string/sequence algorithms, and the at-scale toolbox (Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, consistent hashing, external merge sort, Aho-Corasick, MinHash/LSH).
When should I use Computer Science Algorithms?
Computer Science Algorithms fits situations like: tasks involving whats the right algorithm for…; performance-critical code; code with nested loops over the same input; recursive solutions.
How do I install Computer Science Algorithms in Claude Code?
Run `npx skills add pproenca/dot-skills --skill computer-science-algorithms -a claude-code`. Or copy the skill folder (skills/.experimental/computer-science-algorithms in pproenca/dot-skills) into .claude/skills/computer-science-algorithms in your project. Claude Code loads it when a task matches its description.
How do I install Computer Science Algorithms in Codex?
Run `npx skills add pproenca/dot-skills --skill computer-science-algorithms -a codex`. Or copy the skill folder (skills/.experimental/computer-science-algorithms in pproenca/dot-skills) into .agents/skills/computer-science-algorithms in your project. Codex loads it when a task matches its description.
Can I use Computer Science Algorithms 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 pproenca/dot-skills --skill computer-science-algorithms -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/computer-science-algorithms, .gemini/skills/computer-science-algorithms, .github/skills/computer-science-algorithms and .opencode/skills/computer-science-algorithms in your project.
What does Computer Science Algorithms need to run?
SKILL.md names no scripts, command-line tools or credentials: Computer Science Algorithms is instructions for the agent only.
Does Computer Science Algorithms access the network?
SKILL.md names 2 domains. As links in the text: cp-algorithms.com and usaco.guide. This is read from the text; nothing was executed.
Is Computer Science Algorithms 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 Computer Science Algorithms use?
Computer Science Algorithms is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Computer Science Algorithms use?
About 3.3k tokens (SKILL.md is roughly 13k 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 37k tokens, read only when the agent opens those files.
What are the alternatives to Computer Science Algorithms?
Skills that share tags, products or a category with Computer Science Algorithms: Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars) and Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Computer Science Algorithms?
pproenca (a GitHub user) maintains it in pproenca/dot-skills, which has 214 GitHub stars. The repository holds 182 skills in this directory. The repository was last updated on August 15, 2026.
Source: pproenca/dot-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.