「checkpoint」の検索結果

Use when preparing or publishing a local project as a clean GitHub repository. Covers new repositories, private staging releases, public README cleanup, updates to existing remotes, and research-code packaging without datasets, checkpoints, papers, or generated artifacts.

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。

Break complex work into bounded Codex delegation tasks with clear ownership, expected outputs, and integration 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。

Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human 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。

Fixed-topology, discovery-only extension for Gaia curation. Atomically checkpoints each candidate transition, retries only the failed field, and stops at human L4 review.

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。

Dispatch and execute implementation plans with TDD and checkpoints. Use when plan is ready. Parallel by default for independent tasks.

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。

Generates a comprehensive milestone progress review including feature completeness, quality metrics, risk assessment, and go/no-go recommendation. Use at milestone checkpoints or when evaluating readiness for a milestone deadline.

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。

Unity level design-to-code translation. Trigger & event architecture, encounter design contracts, checkpoint & respawn, cinematic & scripted sequences, environmental storytelling hooks, level streaming & loading seams. DESIGN INTENT format: INTENT/WRONG/RIGHT/SCAFFOLD/ DESIGN HOOK. Based on Unity 6.3 LTS.

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Use when a task needs the judgment of a Cartographer/Photogrammetrist — designing an aerial or LiDAR acquisition flight plan against a stated accuracy class, running an NSSDA checkpoint accuracy test on a delivered orthophoto or DTM, choosing a horizontal/vertical datum and geoid model for a georeferencing project, diagnosing why a state-plane grid distance disagrees with a field-surveyed ground distance, or specifying LiDAR point-density and classification requirements for a bare-earth deliverable.

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 task needs the judgment of a chiropractor — screening a spinal or musculoskeletal complaint for red flags before manipulation, choosing a manipulation technique given a patient's risk profile, designing a care plan with a defined reassessment checkpoint, or deciding whether to continue, modify, or discharge a course of care.

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。

Flagship comprehensive SEO audit combining Ahrefs and GSC data in sequential waves with checkpoint saves. Use when user says "site audit pro", "full audit", "comprehensive audit", "audit with live data", "deep audit", "pro audit", "complete SEO audit", "run a full site audit", or "audit everything for this domain". Requires domain param. Optional: site param for GSC overlay.

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。

Generate images via the Stable Diffusion WebUI / Forge HTTP API (AUTOMATIC1111-compatible `/sdapi/v1/*`). Use when the user wants to (1) discover or pick a model / extra module (TE/VAE) / sampler / scheduler / style preset from a running sd-webui server, (2) generate an image with a given prompt (txt2img), (3) check generation progress, (4) cancel/interrupt an in-flight generation, (5) inspect or change a global sd-webui option (e.g. active checkpoint), or (6) test connectivity. This skill talks to a *generic* sd-webui-compatible server (AUTOMATIC1111, Forge, reForge, sd-webui-forge-classic). Do NOT trigger for requests that are purely writing the prompt itself.

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。

Keep long conversations coherent — summarization checkpoints, entity tracking, topic stacks, and graceful context handoffs. Use when dialogues run long or span sessions and the agent keeps losing the thread.

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。

Smallest personal narrated-explainer-video pipeline (spoken narration over visuals, not an audio podcast), fully tool-agnostic and autonomous by default — topic → research ∥ asset collection → script → TTS → video → 4K render ∥ publish info + cover. The skill defines the pipeline logic and self-verified checkpoints; any TTS backend and any video tool (Remotion, HyperFrames, CapCut, ...) work, and how much human oversight to apply is set by the working project's AGENTS.md/CLAUDE.md, not here. Use when the user wants a quick personal narrated video with minimal steps, whether or not they name the tool stack. Do NOT trigger for audio-only podcasts, written episodic content, or heavy multi-format production.

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 working on SAM 3 video segmentation, vaila_sam.py, checkpoint handling, or GPU memory issues.

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。

Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and config files, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or fully POSIX-compliant shared file systems (Filestore, Managed Lustre).

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 self-retrospective on a SKMTC-related session — generator authoring, CLI configuration, debugging, or any sustained interaction involving `@skmtc/core`, `@skmtc/cli`, or `@skmtc/gen-*` packages. Captures three distinct outputs: (1) friction entries (mistakes, surprises, overridden defaults, multi-cycle struggles), (2) a "knowledge acquired" block recording facts the agent learned during the session that weren't in its training — the primary signal for doc gaps — and (3) a closing priority list ranking the top 2-3 findings for docs/skills triage. Wins are logged only when they identify a pattern worth codifying that isn't already in a skill. Use this skill when the user asks to "retro", "skmtc retro", "log friction", "reflect on this session", "what did we learn", "run a retrospective", or after substantive SKMTC work (generator authoring, multi-step debugging, non-trivial CLI configuration) where capturing observations would help improve skills, docs, or SKMTC itself. Distinct from the `skmtc-cli` and `skmtc-generator` skills — those guide *doing* the work; this skill captures observations *about* the work. Do not invoke this skill during ordinary work; it is end-of- session (or mid-session-checkpoint) only.

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 you have a written implementation plan to execute in a separate session 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。

Use when you have a written implementation plan to execute in a separate session 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。

Transparent, rigorous research with full methodology — not a black-box API wrapper. Conducts exhaustive investigation through mandated 2-cycle research per theme, APA 7th citations, evidence hierarchy, and 3 user checkpoints. Self-contained using native OpenClaw tools (web_search, web_fetch, sessions_spawn). Use for literature reviews, competitive intelligence, or any research requiring academic rigor and reproducibility.

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Structured memory system for OpenClaw agents. Context death resilience (checkpoint/recover), structured storage, Obsidian-compatible markdown, local semantic search, and session transcript repair.

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Contract governing the --advice flag: supervision checkpoints, caps, packets, dossier, receipts, and the boundary that keeps counsel from approving anything. Load only when --advice is present.

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Relentlessly interview the user about THEIR plan, one question at a time, until shared understanding. Checkpoints answers so progress survives a reset. Do NOT use to propose solutions.

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Relentlessly interview the user about THEIR plan or design — one question at a time — resolving each branch of a decision tree until shared understanding. Claude is the interviewer; the user is the interviewee. Triggers on "grill me", "stress-test this plan", "get grilled on my design", "interrogate my plan". Checkpoints every answer to docs/knowledge/<slug>.md so progress survives a context-window reset. Flags knowledge the user cannot answer as a gap to chase. Do NOT use to PROPOSE solutions or compare approaches (→ mk:brainstorming). Do NOT use to validate "is this worth building?" (→ mk:office-hours). Do NOT use when Claude should ANSWER questions about the repo (→ mk:ask-me). Do NOT use for a one-shot review of an existing plan (→ mk:plan-ceo-review). Do NOT use to apply named reasoning lenses to findings (→ mk:elicit).

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 Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.

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。