cellrishi-codecellrishi-code/claude-skills-atlas/skills/computer-science/algorithm-learning/SKILL.mdalgorithm-learning
Skill6 starsChanged 39 days agoMITView on GitHub
Prefer understanding and verification over answer-only output.
In this repository36 other files it gives its agents.
- bioinformatics-planningSkill
- biology-literature-analysisSkill
- biology-paper-analysisSkill
- chemistry-research-analysisSkill
- skill-authorSkill
- codebase-explorerSkill
- codebase-understandingSkill
- code-reviewSkill
- debug-systematicallySkill
- implementation-plannerSkill
- implementation-reviewSkill
- ledger-tasks-yyloSkill
- test-and-verifySkill
- system-design-basicsSkill
- defensive-security-reviewSkill
- threat-modelingSkill
- data-analysisSkill
- tutorSkill
- experiment-analysisSkill
- model-evaluationSkill
- numerical-methodsSkill
- mathematics-problem-solverSkill
- proof-solvingSkill
- physics-research-analysisSkill
- data-cleaningSkill
- data-visualizationSkill
- evidence-checkerSkill
- evidence-comparisonSkill
- experimental-planningSkill
- literature-synthesizerSkill
- reproducibility-auditSkill
- research-questionSkill
- source-verificationSkill
- api-designSkill
- frontend-planningSkill
- technical-editorSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 18
Ask the learner to attempt before revealing a full solution when requested.
- Distinctive: line 20
Prefer understanding and verification over answer-only output.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Algorithms Learning
- State prerequisites.
- Explain the core idea and invariant.
- Walk through a small example.
- Give complexity.
- Provide a worked example.
- Give practice problems from easy to harder.
- Ask the learner to attempt before revealing a full solution when requested.
Prefer understanding and verification over answer-only output.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.