「checkpoints」の検索結果

機能追加や不具合修正などの開発を、規模に応じて調査・計画・テスト・レビューへ振り分けます。計画承認とコミット確認を組み込む、関連スキル共通の手順です。

• 機能追加と不具合修正の手順を揃えたいとき
• 変更規模に合わせて工程を選びたいとき
• 設計書からMVP開発を進めたいとき

Break large features into independently verifiable, human-reviewable slices under a feature directory in specs/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints), [refactor-clean](../refactor-clean/SKILL.md) (review the materialized spec so the plan describes one-owner architecture, not the feature bolted on), [screenshot-critique](../../visual/screenshot-critique/SKILL.md) and [compare-screenshots](../../visual/compare-screenshots/SKILL.md) (the visual gates), and a code-review pass (audit each slice before it lands).

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。

Use when a skill is brittle, drifting, repeatedly failing, or needs a Reliable Skill Contract. Trigger for phrases like skill lifeguard, reliable skill, self-maintaining skill, negative examples, verification checkpoints, drift signals, replay hooks, or failure log to skill patch. Audits or patches skills so high-value workflows include explicit forbidden behaviors, checkpoints, machine-checkable done conditions, replay or smoke hooks, and drift detection.

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。

Ultimate MCP server for Civitai — search models, browse top images with prompts, download LoRAs/Checkpoints, analyze trends. Use when user asks about AI models, LoRAs, checkpoints, Stable Diffusion, Flux, image generation prompts, or Civitai.

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。

Run a Duck Critic producer-critic loop: you (main) keep producing the plan/code/tests and gate your own work at checkpoints with a different-model critic, revising until it passes. Use when asked for rubber duck, ラバーダック, 別モデルレビュー, second opinion, critic, or a different-model code review, design review, plan critique, or review by another model/agent harness. Not for readability refactoring or code cleanup alone.

Autonomer Langlauf-Modus ohne laufende Rückfragen — Startbedingungen (Ziel, Scope, Budgets, Checkpoints), Entscheidungsstufen und Abbruchregeln für unbeaufsichtigte Läufe; erhöht die Ausdauer, nicht die Berechtigung. Nutzen, wenn ausdrücklich ein längerer autonomer Lauf gewünscht ist.

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。

Guide for the Tinker CLI — managing training runs, checkpoints, downloading weights, and publishing to HuggingFace. Use when the user asks about CLI commands, listing runs, managing checkpoints from the terminal, or uploading to HF Hub.

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。

Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.

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。

機能追加やコード整理の後に、ビルド・型・lint・テスト・秘密情報の検索・差分を6段階で確認するスキル。合否と修正事項をまとめ、PRを作成できる状態か報告します。

• 機能追加後のコードを検証したいとき
• リファクタリング後の不具合確認
• PR作成前の準備状況確認

正常に動いている既存機能を新しい仕様へ変更するスキル。テストを先に更新し、実装の修正、レビュー、承認を挟んだコミットまで順に進めます。

• 既存の通知しきい値を変えたいとき
• 動作中の機能を新仕様に合わせたいとき
• 振る舞いの変更前にテストを更新したいとき

コードの動作を保ちながら、重複削除やモジュール分割で構造を整えるスキル。変更前と各段階のテスト、計画の承認、レビュー、コミット前の確認まで進行を管理します。

• 動作を変えずにモジュールを分割したいとき
• 重複コードや不要コードの削除
• 入れ子の削減と名前の整理

大規模言語モデルの知識・推論・コード生成を共通の課題で評価するスキル。複数モデルの比較表や学習途中の成績推移を作り、評価結果と個別の回答を保存します。

• MMLU・GSM8Kでのモデル評価
• 複数モデルの比較表作成
• 学習途中のモデルの成績追跡

コード実装や不具合修正、PRレビューをBlackbox AIに委任します。複数モデルの結果を評価して選び、長時間の作業の監視や保存地点からの再開も扱うスキル。

• 機能実装や不具合修正の委任
• PRの不具合とセキュリティ確認
• 複数AIモデルの実装比較

Track long-horizon objectives across multiple sessions with milestone checkpoints, progress persistence, and drift detection

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。

Formats and structurally validates local treatment-plan documentation after clinical decisions have already been supplied and verified by authorized licensed professionals. Use for source traceability, clinician-authored intervention records, goals and checkpoints, shared-decision records, reconciliation handoffs, and release gates—not for clinical decision-making.

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。

Supports work with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format.

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。

Builds and validates PyHealth clinical machine-learning pipelines for EHR, signals, imaging, and medical codes. Use for PyHealth dataset loading, MIMIC-III/IV, eICU or OMOP prediction tasks, patient-level evaluation, mortality/readmission/length-of-stay modeling, medication recommendation, sleep staging, Trainer checkpoints, and ICD/ATC/NDC/RxNorm mapping.

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。

Use when an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.

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。

Use when asked to ship a SaaS MVP, audit application security, build an AI agent, run browser QA, or design a domain model with multiple skills and verified checkpoints.

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。

Masterclass lecturing — one continuous explanation carried by slides, with dense pages, long narration that builds an argument across several real cases, and checkpoints that are rare and deliberate. Use when the ask is a lecture, a masterclass, a systematic walkthrough of a subject, 「大师课」「系统讲解」「讲透」, or any topic whose payoff is understanding rather than practice; also use when the user names this style. Not for hands-on training (that is `workshop-style`) and not for a course whose point is manipulating a mechanism (that is `deep-interactive`).

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。

Gate fine-tuned checkpoints with drift budgets, paired comparison, and forgetting checks before promotion. Use after a training run produces a checkpoint, when deciding whether a tuned model ships, or when a promoted model needs re-gating against updated goldens.

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。

Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.

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。

Shipyard's governed delivery pipeline — converge the mission, synthesize a durable spec, decompose vertical-slice tickets with blocking edges, run the frontier in parallel via team, close with verification, and report with a full decision log. Two entry gates — the yard gate (drydock audit) and the fog gate (an effort whose destination is unclear is routed to /ask-navigator before this pipeline starts). Humans own the checkpoints where there is no unique answer or the error cost is severe; agents continuously run everything repeatable and acceptable-by-evidence.

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。

Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).

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。