Como usar
- Copie o comando e verifique o pacote antes de instalar no seu ambiente de agente.
- Consulte no original as ferramentas, credenciais e ambientes necessários.
- Descreva os dados de entrada e a entrega esperada; confira a saída com as fontes.
npx skills add https://github.com/yogsoth-ai/de-anthropocentric-research-engine --skill detect-breakpointAntes de começar
- A instalação não inclui assinatura de modelo, franquia API ou acesso aos seus arquivos.
- Estrelas e instalações indicam atividade, não um teste independente de qualidade ou segurança.
Documento original do Skill
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
detect-breakpoint
Purpose
Locate the point or region where performance, validity, or a claim changes regime.
Input contract
yaml
required: [ordered_observations, target_metric, flip_or_regime_rule]
optional: [candidate_axis, smoothing_rule, uncertainty_bounds]
constraints: [ordering and metric direction are explicit]Procedure
- Sort observations along the declared axis and validate comparability.
- Detect abrupt flips, slope changes, saturation, or regime transitions.
- Estimate boundary region and attach uncertainty.
- Return witness observations and follow-up tests.
Output contract
yaml
produces: [breakpoint_region, regime_label, witness_observations, uncertainty, follow_up_tests]
delta_fields: [findings, uncertainties, open_questions]Quality gates
- A breakpoint requires observations on both sides or an explicit one-sided limitation.
- Metric direction and flip rule are declared before detection.
- Smoothing or interpolation never replaces raw witnesses.
Parameterization
Caller supplies observation schema, axis ordering, metric, flip/regime rule, smoothing, and uncertainty policy.
Failure and counterexamples
Reject boundaries inferred from unvalidated ordering, single noisy points, or hidden smoothing.
Provenance map
- concept: stress-test/breakpoint-detection
- concept: deep-insight/controlled-perturbation
