「machine」の検索結果

Write technical documentation that matches the measured profile of professionally-written docs. Carries the craft rules derived from 114k words of pre-2022 documentation (SQLite, PostgreSQL, Python, Django, Rust, Go, Pro Git, nginx), the per-Diátaxis-type numeric targets, and the copywriting register for page leads. Use when drafting or rewriting any documentation prose, or when a page is factually right but reads as machine-written.

日本語の概要は準備中です。原文の説明を表示しています。

Standard operating procedure for producing a documentation page end to end — gather source material, classify, draft against the measured corpus profile, then run the reader-level and humanizer passes and both gates. Use for any page on a documentation surface, or when rewriting a page that reads as machine-written. Orchestrates technical-writing, reader-level and humanizer in a fixed order.

日本語の概要は準備中です。原文の説明を表示しています。

Propose the next dependency-ready sprint from the ALREADY-DECOMPOSED roadmap — standup's active sibling. Reads docs/roadmap-execution-plan.md + the memory backlog, computes per-task readiness from the roadmap's machine-readable status, orders with roadmap-planner's graph engine, and emits a proposed task-set (sprint-plan manifest shape) with per-feature done-criteria, excluding unready tasks and naming their unmet prerequisite. PROPOSES ONLY — the human confirms/edits before /triage routes it (typically to the `power` track). Distinct from `sprint-plan`, which decomposes a fresh vision.

日本語の概要は準備中です。原文の説明を表示しています。

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

日本語の概要は準備中です。原文の説明を表示しています。

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

日本語の概要は準備中です。原文の説明を表示しています。

Makes sessions durable across `/clear`, machine reboots, and IDE switches. SessionStart catchup pulls scratch + last MemPalace recall + planning files into context. Stop promotion writes a handoff note and runs memory-keeper. Manual `/mddesign:handoff` re-runs either at any time.

日本語の概要は準備中です。原文の説明を表示しています。

Build a throwaway prototype to flush out a design before committing to it. Routes between two branches — a runnable terminal app for state/business-logic questions, or several radically different UI variations toggleable from one route. Use when the user wants to prototype, sanity-check a data model or state machine, mock up a UI, explore design options, or says "prototype this", "let me play with it", "try a few designs".

日本語の概要は準備中です。原文の説明を表示しています。

Triage issues through a state machine driven by triage roles. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.

日本語の概要は準備中です。原文の説明を表示しています。

Import a self-contained NeatContext context bundle shared by another person, or reconcile a newer copy of a context already on this machine, leaving the source bundle unchanged. Use only when the user explicitly invokes this skill or asks to import a NeatContext bundle.

日本語の概要は準備中です。原文の説明を表示しています。

Report which extensions the connected NeatContext context expects, and whether this machine provides them. Use when the user asks what a context can reach, why an extension tool is missing, or how to connect one here.

日本語の概要は準備中です。原文の説明を表示しています。

Export a NeatContext context saved from a conversation into a self-contained bundle folder that can be shared or moved to another machine. Use only when the user explicitly invokes this skill or asks to export a NeatContext context.

日本語の概要は準備中です。原文の説明を表示しています。

Export a NeatContext context saved from a conversation into a self-contained bundle folder that can be shared or moved to another machine. Use only when the user explicitly invokes this skill or asks to export a NeatContext context.

日本語の概要は準備中です。原文の説明を表示しています。

Import a self-contained NeatContext context bundle shared by another person, or reconcile a newer copy of a context already on this machine, leaving the source bundle unchanged. Use only when the user explicitly invokes this skill or asks to import a NeatContext bundle.

日本語の概要は準備中です。原文の説明を表示しています。

Report which extensions the connected NeatContext context expects, and whether this machine provides them. Use when the user asks what a context can reach, why an extension tool is missing, or how to connect one here.

日本語の概要は準備中です。原文の説明を表示しています。

Summarizes uncommitted git changes in a concise machine-readable format. Use in CI pipelines, scripts, or headless invocations where the output will be piped or captured.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level computational biology covering molecular dynamics simulation, protein structure prediction, systems biology, network analysis, and machine learning in biology.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level linear algebra knowledge. Use when working with vectors, matrices, determinants, eigenvalues, linear transformations, vector spaces, inner products, decompositions, or applications in machine learning and physics. Also use when the user mentions 'matrix', 'determinant', 'eigenvalue', 'eigenvector', 'linear transformation', 'vector space', 'basis', 'orthogonality', 'SVD', 'PCA', 'least squares', 'null space', or 'rank'.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level bioinformatics covering sequence analysis, structural bioinformatics, database searching, phylogenetic analysis, NGS data processing, single-cell analysis, and machine learning in biology.

日本語の概要は準備中です。原文の説明を表示しています。

Expert-level mathematical logic knowledge. Use when working with propositional logic, first-order logic, proof theory, model theory, computability theory, Godel incompleteness theorems, set theory, type theory, or formal verification. Also use when the user mentions 'formal proof', 'Godel', 'incompleteness', 'Turing machine', 'decidability', 'Zermelo-Fraenkel', 'axiom of choice', 'model theory', 'completeness theorem', 'computability', 'formal language', or 'type theory'.

日本語の概要は準備中です。原文の説明を表示しています。

Move issues and external PRs through a state machine of triage roles — categorise, verify, grill if needed, and write agent-ready briefs.

日本語の概要は準備中です。原文の説明を表示しています。

Systematic testing methodology for exhaustive branch coverage, edge case identification, and production bug prevention. Use when PR review reveals incomplete test coverage, when tests pass but users report bugs, when code changes break existing features, when verifying all branches and edge cases before merge, when analyzing "it works on my machine" issues, when planning test strategy for new features, or when debugging flaky tests and race conditions.

日本語の概要は準備中です。原文の説明を表示しています。

Design leakage-safe machine learning experiments that mirror real deployment and support fair model comparisons. Use when defining prediction timing, feature eligibility, train-validation-test splits, baselines, metrics, or controlled model iterations; not for auditing whether raw labels are trustworthy.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated reviews.

日本語の概要は準備中です。原文の説明を表示しています。

Route data analysis and machine learning work to the right skill in this suite. Use when starting any analysis, modeling, validation, or reproducibility task and the matching specialized skill is not yet clear; not needed when one specific skill already clearly applies.

日本語の概要は準備中です。原文の説明を表示しています。