research-routes-run
De la autor
Execute an approved Research Routes context across software, research-exploration, or hybrid journeys with concurrent route owners, shared control-plane resources, CounterPilot read-only boundaries, explicit evidence and provenance, lifecycle recovery, and HumanDecision seams. Use when implementation or experiments must proceed without treating AI inference as fact or silently changing canonical state.
Instalare
npx skills add zenx0x/allinluna --skill research-routes-run -a claude-code
Se instalează în .claude/skills/research-routes-run/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
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Verificări de siguranță pe SKILL.md
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De ce 80
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- research-routeszenx0x/allinlunaCompare multiple research routes with explicit Claims, Evidence, unknowns, failure regimes, and reversible probes. Use when a person needs route-neutral exploration before implementation, experiment authorization, HumanDecision, or canonical-state promotion.Știință★ 63
- research-routes-explorezenx0x/allinlunaExplore open-ended scientific or product questions through multiple concurrent Research Routes branches, probes, experiments, observations, evidence polarity, failure regimes, unknowns, and mature-method comparators. Use for research exploration or hybrid discovery from any starting point while keeping a route-neutral terrain map separate from claims, authorization, implementation, and canonical state.Știință★ 63
- research-routes-planzenx0x/allinlunaPlan software, research-exploration, or hybrid work from a repository, question, problem, frame, or arbitrary starting point. Use when Codex needs a route-neutral map, shared backbone, concurrent routes and branches, explicit claims and hypotheses, provenance, evidence polarity, lifecycle recovery, or a plan that keeps candidate inference separate from HumanDecision and canonical state.Știință★ 63
- autoresearchgithub/awesome-copilotAutonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping/discarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.Testare★ 40.4K
- execute-planbrycewang-stanford/Auto-Empirical-Research-SkillsUse when a research implementation plan exists in docs/superpapers/plans/ and the user is ready to execute it — collecting data, running analysis, producing outputs, writing the paper. Orchestrates task execution with replication-driven verification and two-stage review at phase boundaries.Planificare★ 4.8K
- explore-runlllllllama/RigorPilot-SkillsRigor Improve / Rigor Explore run leaf skill for bounded exploratory evidence in deep learning research repositories. Use when the researcher explicitly authorizes exploratory runs such as small-subset validation, short-cycle guess-and-check, batch sweeps, idle-GPU search, or quick transfer-learning trials, with fair-comparison caveats and no-overclaim summaries in `explore_outputs/`. Do not use for end-to-end exploration orchestration on top of `current_research`, trusted baseline execution, conservative training verification, default routing, verified SOTA claims, or implicit experimentation.Știință★ 484
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