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hyperagents-plugin

hyperagents-plugin enthält 6 gesammelte Skills von Zpankz, mit Repository-Berufsabdeckung und Skill-Detailseiten auf SkillsMP.

gesammelte Skills
6
Stars
2
aktualisiert
2026-03-24
Forks
0
Berufsabdeckung
3 Berufskategorien · 100% klassifiziert
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Skills in diesem Repository

domain-evaluation-harness
Datenwissenschaftler

Create and configure domain-specific evaluation harnesses for the HyperAgents evolution loop. Defines how tasks are loaded, agents are invoked, predictions are collected, and scores are computed. Triggers when setting up evaluation domains or creating custom fitness functions.

2026-03-24
evolutionary-archive-management
Softwareentwickler

Manage the HyperAgents evolutionary archive — an append-only log of all code generations with fitness scores, lineage tracking, and diff storage. Triggers when working with .hyperagents/ directory, archive.jsonl files, or generation metadata.

2026-03-24
fitness-evaluation-framework
Softwarequalitätssicherungsanalysten und -tester

Domain-agnostic fitness evaluation for evolved code generations. Defines evaluation harness interfaces, scoring contracts, and multi-domain aggregation. Triggers when evaluating code quality, running benchmarks, or scoring agent outputs.

2026-03-24
parent-selection-strategies
Datenwissenschaftler

Evolutionary parent selection algorithms for choosing which generation to mutate next. Implements random, best, score-proportional, and novelty-aware selection. Triggers when selecting parents, managing exploration/exploitation tradeoffs, or configuring evolution strategy.

2026-03-24
self-referential-self-improvement
Softwareentwickler

Apply HyperAgents' self-referential improvement pattern to any code artifact. Triggers when Claude is asked to 'improve', 'optimize', 'evolve', or 'self-improve' code, agents, skills, or prompts. Also triggers on repeated failures as an automatic recovery strategy.

2026-03-24
staged-evaluation
Softwarequalitätssicherungsanalysten und -tester

Two-phase evaluation strategy from HyperAgents — run a quick staged check on small samples first, only proceed to full evaluation if the staged eval passes. Saves 90%+ compute on broken mutations. Triggers when evaluating generations, running benchmarks, or optimizing evaluation cost.

2026-03-24