「compute」の検索結果

Profile, audit, and optimize frontend page performance with emphasis on animation work, memory-leak risks, long-session slowdowns, CSS animations, canvas/WebGL requestAnimationFrame loops, marquees, skeletons, GSAP/Three/Matter effects, timers, listeners, and observers. Use when the user asks to make animations performant, pause offscreen animations, look for memory leaks, profile pages that slow the computer over time, fix janky scrolling, reduce CPU/GPU use, or repeat the "only play in view" optimization on React/Vite/Next/frontend pages using Codex Browser.

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。

Statistical data analysis skill — use when the user asks to analyze numbers, compute statistics, or summarize datasets.

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 user asks what SenseNova Team Harness is, wants an introduction or overview, is getting started, or is deciding whether it fits — a self-hosted workspace where people and local AI agents collaborate on conversations, projects, work items, and artifacts. Covers its core concepts (Workspace, Project, Conversation, Agent, WorkItem, Resource, Artifact, Local Computer), main capabilities, how to run it, and the basic usage flow.

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。

Client-side request-signing and anti-bot token reversal for bug bounty — when a request carries a sign/sig/hmac/token/nonce/timestamp/X-Sensor header that Burp Repeater cannot replay, recover the signer just enough to reproduce the request outside the client. Packet-first staging (capture real request → prove replay works → only reverse if replay fails) across the locate→recover→runtime→validation→replay spine. Covers tracing backward from the signature field (writer→builder→entry→source), isolating user-mutable sign inputs (timestamp/nonce/deviceId/body) vs constants (secret key), hooking fetch/XHR in DevTools, JS deobfuscation basics (webpack/wasm/JSVMP), and the bounty payoff: reach the protected API to then hunt IDOR/auth/business-logic. Use when Burp/mitmproxy replay of a signed or anti-bot-gated request fails and you suspect a client-computed field is blocking you.

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 Peekaboo for macOS screenshots, Accessibility inspection, and background app/window/UI automation, including fallback computer use when the primary tool cannot handle an authorized 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。

Hunt cloud / infrastructure misconfigurations. AWS: public S3 buckets (s3:GetObject anonymous), permissive bucket policies (PutObjectAcl public-write), exposed CloudFront origin, public Lambda function URL, public RDS snapshot, IAM credentials in JS bundles, AWS metadata accessible via SSRF. GCP: public GCS buckets, exposed Cloud Run services, leaked service account JSON. Azure: public blob containers, exposed Function App. (Kubernetes/Docker exposure is owned by hunt-k8s; CI/CD pipeline attacks by hunt-cicd; post-credential IAM escalation by cloud-iam-deep.) Detection: targeted dorking, certificate transparency, JS bundle secret extraction, port scan for known service ports. Validate: actual data read / write / RCE. Use when hunting cloud-native storage and compute misconfig (S3/GCS/Blob, IMDS-via-SSRF, serverless, public managed services).

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。

Hunt NTLM/Negotiate information disclosure on internet-reachable IIS/SharePoint/Exchange. Anonymous NTLM Type-2 challenge capture leaks NetBIOS domain, internal DNS forest, computer name, AD timestamp via AV_PAIRS structure. Default Windows-installer hostnames (WIN-XXXXXXXXXXX pattern) signal lazy provisioning. Use when target advertises `WWW-Authenticate: NTLM` or `Negotiate` headers anonymously.

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。

Compute the EXACT sub-transaction impact of a rollup rule in TRES Finance — every filter applied — and flag genuine problems (zero matches, overlap, config mistakes) before the rule is created. Two modes. Mode A: a pre-create quality gate invoked by the tres-rollup-rules skill to vet proposed candidate rules. Mode B: standalone review of a rule someone submitted or of existing PENDING rules. Trigger this skill when the user says "review this rollup rule", "what would this rollup rule actually match", "preview the rollup impact", "how many transactions would this rule catch", "is this rollup rule valid", "validate my rollup rule", "check my pending rollup rules", or "review the rollup requests". Do NOT use this to design or propose new rollups — that is tres-rollup-rules. This skill only measures impact and flags issues; it never executes mutations.

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 asks to "generate daily paper", "search arXiv for EEG papers", "find EEG decoding papers", "review brain-computer interface papers", or wants to create paper summaries for EEG/brain decoding/speech decoding research. This skill automates searching arXiv for recent papers on EEG decoding, EEG speech decoding, or brain foundation models, reviewing paper quality, and generating structured Chinese/English 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。

Verify that every quantitative claim in the paper is traceable to an analysis output file, and that no important output was omitted. Make sure to use this skill whenever the user wants to check that the paper and analysis are consistent before submission. Triggers include: "run the quality gate", "check the paper matches the analysis", "verify consistency", "does the paper match my results", "check my numbers", "are my tables right", "quality check before submission", "verify my claims", "make sure everything is consistent", "double-check the paper against my output files", or any pre-submission integrity check between paper text and computed results.

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 Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web 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。

Query the tika-eval H2 database directly for counts and joins the canned reports do not compute — connection gotchas, key tables, example queries. Use when the xlsx/summary.md reports are not enough.

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。

Refactor and review SwiftUI view files with strong defaults for small dedicated subviews, MV-over-MVVM data flow, stable view trees, explicit dependency injection, and correct Observation usage. Use when cleaning up a SwiftUI view, splitting long bodies, removing inline actions or side effects, reducing computed `some View` helpers, or standardizing `@Observable` and view model initialization patterns.

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。

Computational geometry with Shapely - create geometries, boolean operations, measurements, predicates

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。

Unit-aware computation with Pint - convert units, dimensional analysis, unit arithmetic

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。

Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc 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。

Use this skill whenever the user asks for an Excel spreadsheet, a .xlsx file, or a workbook with formulas/pivots/multiple sheets — creating one from scratch or editing an existing one. Covers the rule that computed values must be written as real Excel formulas (never hardcoded numbers), and a static formula-safety check. Formulas are NOT recalculated in this environment — see 'Not supported'.

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 working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

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。

Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.

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。

Analyze git repositories to build a security ownership topology (people-to-file), compute bus factor and sensitive-code ownership, and export CSV/JSON for graph databases and visualization. Trigger only when the user explicitly wants a security-oriented ownership or bus-factor analysis grounded in git history (for example: orphaned sensitive code, security maintainers, CODEOWNERS reality checks for risk, sensitive hotspots, or ownership clusters). Do not trigger for general maintainer lists or non-security ownership questions.

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。

Compute well-defined metrics from existing formulas, datasets, or test outputs. Use as an explicit/manual helper when the metric definition is already known, not for choosing the overall analysis owner or dashboard strategy.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。

Master discrete mathematics, logic, formal proofs, and computational thinking. Build the mathematical foundation for all computer science.

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。

Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。