「search debug」の検索結果

Pythonの実行を途中で止め、変数や処理の流れを調べて不具合の原因を探ります。pdbとdebugpyを使い分け、失敗したテストや稼働中のプロセスも調査します。

• 失敗したテストの変数を調べたいとき
• 関数内のコレクションの変化を追う
• 例外発生地点の変数の確認

Use only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.

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。

Search runtime and scene management: verify queries, inspect scenes, debug app readiness, and diagnose recall or scene-config 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。

This skill should be used when the user asks for "deep research", "research team", "comprehensive analysis", "research report", "investigate thoroughly", "compare X vs Y in depth", or needs synthesis across multiple sources with verification. It spawns a coordinated team of researcher agents across multiple rounds, with the lead triaging findings and creating targeted follow-up tasks. Scales from Focused (2 researchers, 1-2 rounds) to Comprehensive (4 researchers, 3-4 rounds with cross-verification). Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.

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 only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.

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。

Automate setup, configuration, execution, monitoring, and troubleshooting of AutoResearchClaw — the 23-stage autonomous research pipeline that generates conference-grade papers. Use when the user mentions ResearchClaw, wants to write a research paper autonomously, needs to set up or debug the pipeline, or says research paper, autonomous research, or paper generation.

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。

Pure API reference for reddapi.dev - authentication, all endpoints (vector search, semantic search, trends, subreddit lookup), request parameters, response schemas, and error codes, with no research-workflow framing. Use when the user wants raw endpoint documentation, is debugging a reddapi.dev integration, needs exact request/response field names, or asks for 'reddapi API reference', 'reddapi.dev endpoints', or 'reddapi error codes'. For guided research workflows and query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads.

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。

Multi-AI research using available external providers (Double Diamond Discover phase) - Questions about best practices, patterns, or ecosystem research PRIORITY TRIGGERS (always invoke): "octo research", "octo discover", "co-research", "co-discover" DO NOT use for: simple file searches (use Read/Grep), questions Claude can answer directly, debugging issues (use skill-debug), or "what are my options" for decision support.

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 the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

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 a researcher step by step into an unfamiliar research field, or decode a paper, abstract, figure caption, or referee comment they cannot parse. Builds understanding in rungs (motivation, vocabulary, core framework, methods, frontier), anchored to what the user already knows, with a checkpoint before each advance. Use when the user says they are new to a field, asks what a research area or method is, says an explanation was too technical, asks to be walked through something step by step, asks for a reading path, or supplies dense research text. Trigger even when the user only names an unfamiliar field or pastes an abstract without asking to be taught. Do not trigger for a one-line summary, a short overview request, or a direct factual answer; do not use it for translation, editing, search, debugging, or specialist questions inside the user's own field. Also trigger in other languages, including Chinese such as 入门, 一步一步讲, 看不懂, 这篇论文讲什么, 帮我理解这个领域.

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 decision needs deep multi-source technical research with cited evidence — technology evaluation, ecosystem comparison, standards/spec fact-finding, "how do others solve X". Triggers on "research X", "deep dive on X", "evaluate X vs Y", "find best practices for X". NOT for library/API doc lookup (see mk:docs-finder); NOT for one-shot URL fetch (see mk:web-to-markdown); NOT for internal codebase discovery (see mk:scout); NOT for project-only Q&A (see mk:ask-me); NOT for comparing solution designs (see mk:brainstorming); NOT for root-cause debugging (see mk:investigate); NOT for wiki ingestion of sources (see mk:wiki-research).

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。

Conduct enterprise-grade research with multi-source synthesis, citation tracking, and verification. Use when user needs comprehensive analysis requiring 10+ sources, verified claims, or comparison of approaches. Triggers include "deep research", "comprehensive analysis", "research report", "compare X vs Y", or "analyze trends". Do NOT use for simple lookups, debugging, or questions answerable with 1-2 searches.

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Designs, reviews, and debugs DynamoDB data layers from design axioms — enumerates access patterns, chooses partition/sort keys and GSIs, decides single-table vs. multi-table, configures Streams, Global Tables, TTL, vector indexes for similarity search, and zero-ETL integrations to OpenSearch/Redshift/SageMaker Lakehouse, and produces a defensible data-layer design with a monthly cost estimate and optional live validation. Applies whenever a user is designing, reviewing, or refactoring anything backed by DynamoDB — schemas, access patterns, GSIs, single- vs. multi-table choices, Streams consumers, transactional outboxes, Global Tables, zero-ETL pipelines, or storing embeddings and running semantic/vector similarity search with SearchVectors on items already in DynamoDB — even when they don't say "axioms" or "design review." Also applies when debugging hot partitions, throttling, unbounded Scans, LWW conflicts, or surprise bills on DynamoDB workloads.

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 the user needs multi-source research with citation tracking, evidence persistence, and structured report generation. Triggers on "deep research", "comprehensive analysis", "research report", "compare X vs Y", "analyze trends", or "state of the art". Not for simple lookups, debugging, or questions answerable with 1-2 searches.

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。

Search GitHub, Stack Exchange, Chinese technical communities, official documentation, and general developer web sources for software errors, compatibility problems, API usage questions, and real-world workarounds. Use for debugging and discovery only; results are not paper-citation 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。

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

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 bug, failing test, or unexpected behaviour needs investigation, especially across sessions or context resets. Isolates it in a debugger subagent with persistent .planning/debug state, unlike skills/systematic-debugging.

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。

The PRIMARY development workflow for the Archon project (remote-coding-agent). Use this skill instead of any PRP skills when working on Archon code. Routes to 10 specialized cookbooks based on what the user is trying to do: RESEARCH — "how does the orchestrator work?", "where is session state defined?", "trace the workflow execution flow", "what is IWorkflowStore?" INVESTIGATE — "should we use Drizzle or Prisma?", "what's the best way to add WebSockets?", "can we migrate to Turso?", "how do other projects handle rate limiting?" PRD — "write a PRD for dark mode", "spec out the notification feature", "product requirements for webhook retry" PLAN — "plan the auth refactor", "design the caching layer", "create an implementation plan for #42" IMPLEMENT — "implement the plan", "execute .claude/archon/plans/auth.plan.md", "build the feature from the plan", "code this up" REVIEW — "review PR #123", "review my changes", "code review the diff" DEBUG — "debug the failing test", "why is streaming broken?", "root cause analysis on the timeout issue" COMMIT — "commit these changes", "commit the auth refactor" PR — "create a PR", "open a pull request for this branch" ISSUE — "report this to gh", "create a gh issue", "log it in github", "file a bug for this", "create a feature request" This skill triggers on ANY development task: researching, investigating, planning, building, reviewing, debugging, committing, or shipping code. NOT for: Running Archon CLI workflows in worktrees (use /archon instead).

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。

Node.jsの処理を途中で止め、変数や関数の呼び出し経路から不具合を調べるスキル。端末でのステップ実行、状態収集の自動化、性能調査を支援します。

• Node.jsテストの途中の状態確認
• 関数の呼び出し経路と変数の調査
• 非同期処理の停止箇所を調べたいとき

Put your AI on a Performance Improvement Plan. Forces exhaustive problem-solving with Western big-tech performance culture rhetoric and structured debugging. Trigger when: (1) task failed 2+ times or stuck tweaking same approach; (2) about to say 'I cannot', suggest manual work, or blame environment without verifying; (3) being passive—not searching, not reading source, just waiting; (4) user frustration: 'try harder', 'stop giving up', 'figure it out', 'again???', or similar. Also for complex debugging, env issues, config/deployment failures. All task types: code, config, research, writing, deployment, infra, API. Do NOT trigger on first-attempt failures or when a known fix is executing.

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。

Visualize training metrics, debug models with histograms, compare experiments, visualize model graphs, and profile performance with TensorBoard - Google's ML visualization toolkit

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。

LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI 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。

Interact with Obsidian vaults using the Obsidian CLI to read, create, search, and manage notes, tasks, properties, and more. Also supports plugin and theme development with commands to reload plugins, run JavaScript, capture errors, take screenshots, and inspect the DOM. Use when the user asks to interact with their Obsidian vault, manage notes, search vault content, perform vault operations from the command line, or develop and debug Obsidian plugins and themes.

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。

This skill covers reproducible research pipelines and replication packages. Use when the user is setting up a research project directory structure, configuring workflow managers (Make, Snakemake, DVC), managing computational environments, preparing replication packages for journal submission, or debugging reproducibility failures. Triggers on "reproducible", "replication package", "Makefile", "Snakemake", "DVC", "pipeline", "workflow manager", "data versioning", "conda environment", "Docker", "seed management", "AEA data editor", "replication", "project structure", or "submission checklist".

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。