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/probabl-ai/skore-skills --skill research-ml-practiceAntes 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.
Renderizado do repositório de origem, preservando títulos, exemplos, código, tabelas, links e imagens.
Research ML Practice
Worker skill. Callers own the stage turn and the user-facing summary. Do not git end-turn.
Human-facing prose
Details: setup-workspace references/human_facing_prose.md. Chat stays path + one or two sentences of the finding. Do not narrate the skills framework or the wrapper CLI.
Sequence
- Intake. Infer modality from dtypes / JOURNAL when
obvious; use a stated domain and stage (data_analysis | model) if the caller named them. Then pick a mode (references/search.md):
- Caller passed a named concern (not the canned EDA
extra-analysis question) → depth. Do not re-ask. Do not run the canned survey first.
- Caller passed the canned extra-analysis survey or
“survey extra analyses” → survey.
- Neither → if JOURNAL and an EDA report exist, run the
canned survey; else AskUserQuestion for a named concern, stating the modality and stage inferred this turn and that the answer only drives a literature search, no code. A file link is an addition, never the context. Do not start a depth search with an empty concern.
- Abstract the problem class before any query
(references/search.md). JOURNAL and EDA are context, not the answer list and not search keywords for the table’s proper name.
- Run the matching search loop (
references/search.md).
Fetch primary pages. Follow up per promising extra if the first pass is thin or single-sourced.
- Write
scratch/research/using the matching structure in
references/search.md (survey: survey-<slug>.md; depth: <slug>.md with lanes). There is no templates/ directory in this skill — copy the markdown skeleton from that reference. Gitignored.
- Return to the caller: scratch path and a one- or two-sentence
finding. Name that candidates are laned (measure / declare / evaluate / confirm). Chat is path + those sentences only — no pasted headings, tables, or “Depth note — …” body. That is the complete user-facing deliverable even when a harness says to put the full answer in chat. If tools cannot search or write, stop there: still only path + sentences (name the intended scratch/research/ path). No hypotheses, diagnostics, planned-query bullets, or template headings in chat (that is the paste). Do not write data_analysis.md, the design note, or data/. The caller asks which extras to add.
Stop conditions
- Do not drop, impute, or remove outliers. Do not change the split.
- Do not pick the final learner or architecture.
- Do not treat a single blog as ground truth; say when sources
disagree. Thresholds need two independent sources.
- Do not
pixi add/uv add/env add. If code needs a
library, name add-python-package and return.
- Do not run
api getas a substitute for literature (symbols
still go through api get in the caller).
- Missing skill from a caller → that caller one-line skips.
- Do not copy Open questions / EDA findings onto the extras
list without a source.
- Do not search the dataset proper name,
sklearn.datasets,
a Kaggle slug, or “baseline pipeline”. Do not return learners / Pipeline steps as EDA extras.
- Never answer from memory when search ran. Do not ask the
user to go look something up.
- Do not paste the scratch markdown into chat (no “Depth
note —”, no survey body). Path + 1–2 sentences only. If tools cannot search or write, stop after that. A no-tools harness does not license pasting the note.
