GitHub
code-evolution
Autonomous multi-agent code evolution system for optimization problems. Use when solving complex optimization problems (packing, geometry, scheduling, search) through evolutionary approaches with multiple indep...
hanialshater AI Skills
GitHub ↗Explorez les skills IA publiés de hanialshater et consultez leurs instructions source.
Statistiques de l’auteur
Skills indexés
7Étoiles des dépôts des skills indexés
1Forks du dépôt
0Moyenne des étoiles par skill indexé
0Les étoiles d’un dépôt peuvent se répéter entre ses skills.
Catégories populaires
Indexation récente
Explorer
GitHub
edu-demo-evaluator
Evaluate educational demos using Chrome tools for E2E testing. Executes test cases from test_cases.json, captures screenshots, verifies learning outcomes. Scores QUALITY relative to benchmark. Uses real browser...
GitHub
edu-demo-evaluator-tests
Execute test_cases.json against educational demos. Systematic testing via Chrome E2E. Single tab, sequential agents. Output: agent_X_tests.json with PASS/FAIL/PARTIAL results. Captures screenshots and identifie...
GitHub
edu-demo-builder
Build educational demos with excellent UX. Use when spawned by orchestrator to create or improve interactive visualizations. Focus on: obvious next action, no scrolling, persistent state display. You don't see...
GitHub
edu-demo-orchestrator
Orchestrate evolution of educational demos through multi-agent exploration. NEW WORKFLOW: First develop test cases, then spawn builders, then evaluate with Chrome E2E testing. Test cases define LEARNING outcome...
GitHub
edu-demo-evaluator-free
Watch educational demo like a learner (BLIND evaluation). No test cases. No benchmark. No rubric. Honest assessment of: impression, what works, what doesn't, learner impact, recommendation. Output: agent_X_free...
GitHub
edu-demo-test-cases
Develop educational test cases for demos. Creates test_cases.json with specific scenarios that verify LEARNING outcomes, not just UI interactions. Test cases define what a learner should experience and verify....
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.