「checkpoint」の検索結果

Generate the post-EXECUTE closeout packet for a plan or phase. Includes archive-readiness classification, drift signal scoring, commit checkpoint recommendation, and move-on next-state recommendation.

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。

Save the current business state to disk so future Claude Code sessions can pick up where you left off. Captures confirmed conclusions, ruled-out directions, open hypotheses, and the next recommended skill. Use when the user has just reached a conclusion in /money-discover, /money-strategy, /money-diagnose, or any pipeline phase, and wants to lock the state in. Also triggered by: 'save this', 'checkpoint', 'remember this', 'lock it in', '保存', '存档', '记下来', '这个结论留着'.

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 large archive into the vault in controlled batches: triage what is worth ingesting, process oldest first, checkpoint after every batch, and keep cost visible. Use this skill whenever the user wants to import years of bookmarks, an export from another notes app, a downloads folder, a chat history dump, or says "backfill", "bulk import", "process my archive". Do NOT use for ingesting one or a few new sources, which is second-brain-ingest.

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。

Collect and persist structured presentation requirements for Codex Slides, including scenario choice, audience, page count, language, format, research mode, visual direction, source-file roles, and clarification checkpoints. Use when a new deck request is ambiguous or scenario-led.

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Verify that Codex Slides changes are durably reflected in project state and visibly reflected in the exact Codex Browser slide, panel, version, checkpoint, or presentation surface. Use after creation, rendering, editing, restyling, mark edits, speaker-note changes, version restores, or export.

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。

Diagnose performance issues on Amazon SageMaker HyperPod clusters — uneven NCCL bandwidth across nodes and poor filesystem throughput. Read-only. Surfaces host-side signals (Xid, ECC, NVLink, EFA reachability, FSx saturation) and routes to the appropriate sibling skill (hyperpod-node-debugger, hyperpod-nccl, hyperpod-version-checker, hyperpod-issue-report) for any remediation. Triggers on uneven NCCL across nodes, straggler node, FSx slow, checkpoint slow, dataloader slow, filesystem bottleneck, FSx throughput, cross-AZ latency, topology mismatch.

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 resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like: lambda durable functions, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, and reliable serverless applications.

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 creating, improving, finding, or auditing agent skills - the user says 'create a skill', 'do I have a skill for X', 'improve the X skill', 'which skill should I use', asks whether a skill exists for a task, or wants to validate, test, evaluate, package, or health-check skills. Also use for skill ecosystem maintenance (duplicate detection, stale skills, trigger collisions) and advisor 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。

Run the 12-Part engagement workflow from intake to growth plan, checkpointed. "start a new engagement"

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。

Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a custom task, or run an arena-style evaluation.

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。

After scaffolded practice, run an unassisted check with no AI help. Use to distinguish supported performance from what the learner can currently do independently.

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。

Structure a student's self-determined project with mentoring checkpoints, competency alignment, and reflection. Use when guiding student-led projects within a competency-based framework.

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 a diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.

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 metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

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 patterns for racing mechanics, checkpoint tracking, and ghost recording/playback in Godot 4. Use when building racing games, speed-run platformers, or arcade trials.

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 blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.

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 Lightricks LTX-2 / LTX-2.3 video workflows covering text-to-video, image-to-video, GGUF and bundled checkpoints, distilled model, camera control LoRAs, synchronized audio, two-stage upscaling, and swapping alternate/GGUF base models

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 Z-Image txt2img workflows. RedCraft checkpoint, Z-Image Turbo/Base LoRAs, ControlNet, and sampler presets

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Curated download URLs and target directories, organized by family (Flux, WAN, LTX, Qwen, Z-Image, SD15/SDXL), for every model the comfyui-mcp skills reference, covering checkpoints, VAEs, text encoders, and LoRAs. Use when downloading models with download_model (action:"download" / action:"download_civitai"), when a workflow fails with a missing-model error, or when setting up a new machine.

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。

Discover Civitai models with the BUILT-IN download_model action:"search_civitai" and install/generate them locally. Find a checkpoint/LoRA/embedding on Civitai, download it into ComfyUI, and use its trigger words. Optionally pair the official Civitai MCP for community features (images browsing, posting, collections).

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。

Cooperatively pause unfinished work in the current session, leave a precise continuation checkpoint, and resume from it when the user later says continue. Use only when the user explicitly requests this pause workflow. Do not use for crashes, app shutdown, new-session handoffs, or ordinary task summaries.

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。

Execute detailed plans in batches with review 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。

Quality gates - 11 commit readiness gates, build/test protocol, validation checkpoints, and quality metrics

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Use when an ad account HIERARCHY needs designing or cleaning up — campaign, ad set, and ad structure by objective, a naming convention that keeps data readable, CBO versus ABO by stage, how many ad sets and creatives to run, and the minimum daily budget an ad set needs to exit learning. Trigger on 'account structure', 'campaign structure', 'naming convention', 'how many ad sets should I run', 'CBO or ABO', 'our account is a mess of duplicated campaigns'. Not for — scoring an existing account against 84 checkpoints, see `21-ads-audit-global`; pixel and conversion setup, see `53-tracking-setup-global`; budget across channels, see `54-media-plan-global`; scaling a winner, see `55-scaling-ads-global`.

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。