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Apply integrations to existing projects with tanstack add, including add-on id resolution, dependency chains, option prompts, and .cta.json project metadata preconditions.

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。

Reapply and repair Windows Codex Desktop after Store upgrades, including custom provider models hidden by Statsig available_models filtering, the dependent blue-purple Power slider and its Ultra toggle, Fast Mode request/UI gates, locale i18n, plugin UI gates, Chrome/browser_use gates, Goal command gates, Windows Computer Use availability gates and plugin/runtime repair, phone remote-control pairing under third-party/API-key main app usage, Desktop dynamicTools/inputSchema thread-start schema drift, local conversation visibility recovery after model_provider switches, restored-conversation missing-cwd continuation repair, ASAR integrity repair, signing/installing patched MSIX packages, SDK cleanup, Fast Mode wire verification, local plugin marketplace registration, and optional custom model_instructions_file setup (the bundled system prompt, also called 听话水).

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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 positioning an AISTATS submission against AI, machine-learning, statistics, and uncertainty literature, including arXiv preprints, workshop versions, concurrent submissions, prior conference versions, PMLR archival status, and the two-community citation coverage that AISTATS reviewers expect.

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 strengthening AISTATS reproducibility evidence, including the official reproducibility checklist, statistical assumptions, proofs, datasets, hyperparameters, random seeds, compute, uncertainty estimates, baselines, code/data release statements, and checklist-to-claim consistency audits.

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 preparing AISTATS supplementary material, appendices, proof details, code/data archives, simulation scripts, additional tables, and anonymized artifacts under deadline, size, anonymity, and reviewer-discretion constraints, including how to split a theory-plus-experiments paper between body and supplement.

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 designing or auditing figures for an Advanced Materials manuscript, including the lead figure, multi-panel characterization figures, and the Table-of-Contents graphic, where visual quality is a house expectation. Designs figure strategy; does not run the analysis or write prose.

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 strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

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 preparing ACM CCS appendices and supplementary material, including the ethics considerations appendix, protocol transcripts, formal proofs, measurement tables, and anonymized supporting material under the 12-page body limit, anonymity, and reviewer-discretion constraints for a security paper.

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 positioning an ACM CCS submission against the security big-four and specialist literature, including arXiv preprints, prior CCS/S&P/USENIX/NDSS papers, concurrent disclosures, CVE and advisory records, and the attack-and-defense lineage that a SIGSAC program committee expects to see credited.

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 explaining or planning around ACM CCS peer review, the two-cycle HotCRP workflow, decision categories including minor revision, the rebuttal window, the adversarial reviewer pool, ethics and disclosure scrutiny, meta-review dynamics, and ACM proceedings outcomes.

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 strengthening AAMAS reproducibility evidence for interaction claims, including proofs and game definitions, opponent and population sets, self-play protocols, random seeds, compute, uncertainty on strategic outcomes, baselines, and consistency between what the paper claims about agents and what the artifact can actually show.

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 preparing AAMAS supplementary material - proofs, extended game and mechanism definitions, extra multiagent experiments, opponent-set details, and the anonymized artifact zip - under the size cap, anonymity, and reviewer-discretion limits, including how to split a game-theory-plus-experiments paper across the 8-page body and the supplement.

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 positioning an AAMAS submission against multiagent, game-theory, and reinforcement-learning literature spread across AAMAS, AAAI, IJCAI, NeurIPS, ICML, EC, and JAAMAS, including arXiv and workshop versions, concurrent submissions, prior conference versions, and the cross-community citation coverage AAMAS reviewers expect.

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 organizing AAAI supplementary material, including the technical appendix, multimedia appendix, and code/data ZIPs, while respecting that AAAI supplements are due with the paper, treated as immutable after submission, must stay double-blind, and should never hide main-paper-critical evidence from reviewers who skim the appendix.

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 preparing an accepted AAAI paper for camera-ready source submission to AAAI Press, including proceedings page limits, two-column template compliance, copyright transfer, purchased extra technical pages, deanonymization, registration, oral or poster presentation, and final public artifact release.

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 designing or auditing AAAI experiments for the broad-AI program committee, including baselines, ablations, statistical significance, robustness, human evaluation, AI-for-Social-Impact and alignment/safety evidence, compute and cost reporting, and reproducibility-checklist alignment for Phase-1 survival.

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。

Produce a decision-ready brief of current brand, product, category, and competitor conversations across social platforms, including sentiment drivers, questions, risks, and growth opportunities.

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 Meta Ads campaign performance and account gaps using Meta's actual system mechanics — including customer-journey coverage, Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue. Use for performance diagnosis, account audits, full-funnel or TOF/MOF/BOF gap analysis, deciding what to test or create next, and producing novice-friendly recommendations without forcing every campaign or ad into a funnel stage.

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。

Manages Fabric Dataflow Gen2 items, including creation, M editing, connections, output destinations, getDefinition and updateDefinition inspection, executeQuery, refresh history, and Gen1-to-Gen2 save-as upgrades via saveAsNativeArtifact. Use for Dataflow items or Power Query M definitions. For pipeline JSON use pipeline-migration; for Spark or Warehouse transformations use their own skills.

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。

Manages Fabric deployment pipelines for ALM promotion across dev, test, and prod stages, including stage creation, workspace assignment, selective forward or backward deploys, operation polling, stage role assignments, and the pipeline and workspace permissions each action requires. For Git sync use git-integration-operations-cli.

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。

Manages Fabric Variable Library items, including definitions, libraryVariables and valueSets overrides. Covers which consumers can reference a variable and with what syntax -- pipelines, notebooks via notebookutils variableLibrary, Dataflow Gen2, Copy Jobs, shortcuts, UDFs and Plan items -- the active value set per workspace or deployment stage, and Git serialization for CI/CD.

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 Project Osmos for long-running Fabric and OneLake data-engineering outcomes, including task status, messages, follow-ups, cancellation, and deletion. Osmos tasks are remote service tasks, not local background agents. In Fabric portal Copilot, direct interaction is unsupported; point users to GitHub Copilot CLI. Use spark-cli for direct non-Osmos Spark work. Triggers: "Project Osmos", "Project Osmos task status", "check my Osmos task".

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。

Manages Power BI semantic models, including tables, columns, measures, relationships, field parameters, DAX, Import, DirectQuery and Direct Lake designs, sources, connections, permissions, refresh, and deployment. Load this skill before acting on a semantic model, unless it is part of publishing a PBIP project, which powerbi-report-cli owns. For natural-language data questions use fabriciq; for visuals use powerbi-report-cli.

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。