「without」の検索結果

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.

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 local coding agent (Codex, Claude Code, Cursor, or similar) needs to operate Multica through the authenticated `multica` CLI: reading or updating issues, comments, metadata, labels, custom properties, subscribers, projects, agents, squads, runtimes, repos, skills, autopilots, attachments, or workspace state; searching issues; inspecting or cancelling agent runs; replying to a Multica issue from an external agent; creating or triaging issues; checking linked pull requests; or safely handling Multica mention/status side effects without relying on the Multica hosted agent runtime.

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。

Draft, expand, revise, structure, and maintain a full Islamic University of Technology BSc thesis from source documents using the local iutbscthesis LaTeX template. Use when writing or editing thesis metadata, front matter, abstract, chapters, related works, methodology, results, discussion, conclusion, citations, appendices, figures, tables, code listings, or compile guidance for main.tex. Also use when the user expects an AI agent to turn papers, notes, PDFs, LaTeX sources, bib files, data tables, and assets into a substantial thesis manuscript chapter by chapter without requiring repeated babysitting.

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。

Adversarial, security-first review of a pull request from an EXTERNAL or untrusted contributor — a fork PR, a first-time or unknown author, anyone without write access — before a maintainer merges it. Use this whenever you're reviewing, triaging, or deciding whether to merge a PR opened against tbh-meter by someone outside the maintainer team: 'review PR #123', 'someone opened a PR — is it safe to merge?', 'check this contributor's changes', 'can we take this fork PR?', 'is this contribution legit?'. This is supply-chain DEFENSE, not ordinary code review: every merged PR auto-updates onto every user's machine and ships a memory-reading .exe plus an unsandboxed Electron app, so the author and the PR description are UNTRUSTED and the diff is the only evidence. It posts its verdict as a real GitHub review — an inline comment pinned to each problematic line (via the pulls/reviews API), not just one summary comment. NOT for your own or a fellow maintainer's branch (use self-review for that), and prefer this over the generic review flow for anything coming from outside the team.

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 truthful Japanese App Store metadata pack and app-name shortlist in twelve minutes without a multi-agent research round. Use for a live launch sprint, fallback ASO copy, app naming under a hard deadline, or when local product evidence is available but market research is intentionally deferred.

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 or review a Claude Design brief and convert its output into a deterministic SwiftUI implementation handoff with native iOS layout, tokens, states, transitions, and accessibility rules. Use for Claude Design mobile prototypes, three-screen iOS concepts, Design-to-Claude-Code handoffs, or when a visual prototype must be buildable without interpretation.

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。

AI intentional non-action — deliberate stopping without clearing, assessment, or rebalancing. Recognition that sometimes the most productive response is no response. Every other self-care skill produces output; rest produces silence. Use when all tending skills feel like more activity rather than less, when the system is functioning well but at high utilization, after sustained intensive work, or when the impulse to optimize is itself the problem.

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。

Drive a GitHub Copilot (or any bot reviewer) pull-request review to a clean pass without babysitting it: fix each finding in its own commit, reply to the review thread with the fix sha, resolve the thread, re-request the bot, and poll for the async re-review. Covers the thread node-id (PRRT_...) vs comment databaseId distinction, the resolveReviewThread GraphQL mutation, the copilot-pull-request-reviewer[bot] slug, and how to read the bot's verdict (COMMENTED plus a "human review recommended" banner is not a blocking finding). Use when Copilot has left review comments on your PR, when bot review threads must be closed out with an auditable fix-reply-resolve trail before merge, or when you need to verify that a re-review actually landed on the new HEAD rather than the old one.

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。

Redact rendered visual artifacts — Mermaid diagrams, SVGs, and HTML dashboards — before public disclosure, using an ordered longest-first mapping table (specific label, then namespace label, then catch-all placeholder) that swaps sensitive identifiers for descriptive stand-ins while preserving structure, node ids, and teaching value. Covers whole-word identifier replacement, node-label-vs-node-id discrimination, re-rendering the derived image from the redacted source, and verifying the result through the redaction gate. Use when a diagram or dashboard built from internal findings must ship to a public mirror without leaking the names behind it.

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。

Redact a network/MITM capture directory — JSONL, JSON, logs, HAR-style dumps — in place before it can be referenced publicly, scrubbing credential tokens, API keys, service ids, UUIDs, emails, home paths, usernames, and device or session hashes while preserving token-class prefixes for analysis and leaving public identifiers untouched. Covers class-preserving substitution, an idempotent re-runnable pass, distinguishing a secret from a public id, and verifying the scrubbed capture through the redaction gate. Use when a wire capture from a sanctioned probe must enter notes, a guide, or a public repo without leaking the session that produced it.

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。

AI intuitive exploration for approaching unknown codebases, problems, or systems without preconceptions. Adapts the Coordinate Remote Viewing protocol to AI investigation: cooldown (clear assumptions), staged data gathering (raw signals → dimensional → analytical), AOL management (separating observations from premature labels), and structured review. Use when investigating an unfamiliar codebase with unknown architecture, debugging a problem where premature hypotheses could mislead, exploring a domain with limited context, or when previous attempts have been led astray by assumptions and "beginner's mind" would be more productive.

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。

Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.

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。

Cultivate edible and medicinal mushrooms from spawn through fruiting. Covers substrate preparation, inoculation methods, incubation conditions, fruiting chamber management, harvest timing, and successive flushes for oyster, shiitake, lion's mane, and other commonly cultivated species. Use when growing edible mushrooms without the risks of wild foraging, when a reliable supply of fresh culinary or medicinal mushrooms is needed, or when exploring mycelial ecology through hands-on cultivation practice.

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。

Implement secure secrets management in Kubernetes using SealedSecrets for GitOps, External Secrets Operator for cloud secret managers, and rotation strategies. Handle TLS certificates, API keys, and credentials with encryption at rest and RBAC controls. Use when storing sensitive configuration for Kubernetes applications, implementing GitOps where secrets must be version-controlled, integrating with AWS Secrets Manager or Azure Key Vault, rotating credentials without downtime, or migrating from plaintext Secrets to encrypted solutions.

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。

Extract the conceptual essence of a repository as skills, agents, and teams — the project's roles, procedures, and coordination patterns expressed as agentskills.io-standard definitions. Reads an arbitrary codebase and produces generalized definitions that capture WHAT the project does and WHO operates it, without replicating HOW it does it. Use when onboarding to a new codebase and wanting to understand its conceptual architecture, when bootstrapping an agentic system from an existing project, when studying a project's organizational DNA for cross-pollination, or when creating a skill/agent/team library inspired by a reference implementation.

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。

Implement cloud cost optimization strategies for Kubernetes workloads using tools like Kubecost for visibility, right-sizing recommendations, horizontal and vertical pod autoscaling, spot/preemptible instances, and resource quotas. Covers cost allocation, showback reporting, and continuous optimization practices. Use when cloud costs are growing without proportional business value, when resource requests are misaligned with actual usage, when manual scaling leads to over-provisioning, or when implementing showback and chargeback for internal cost accountability.

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 generative recipe DSL so agents emit small validated JSON documents and ONE audited interpreter renders them all — data, not code. Covers the N-artifacts threshold for choosing a DSL over bespoke code, designing roles as style slots so a single recipe renders unchanged in every output style, the validator gate (compile + bounds + real render in all styles, plus a --fast mode for corpus-wide sweeps), hand-authored seed recipes, and the emergent conventions that keep a corpus of hundreds consistent without per-item steering. Use when many similar artifacts (icons, status badges, diagram nodes, test fixtures) must be generated by agents, when per-item bespoke code is multiplying unreviewable code paths, or when a whole corpus must be re-rendered cheaply into new styles.

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。

Apply GitHub repository security protections tier by tier — rulesets, read-only Actions token, secret scanning + push protection, Dependabot, and (gated) required status checks / required PR with a GitHub App bypass for a CI auto-commit bot. Mutating and confirmation-gated: always assess first, apply the no-regret baseline, then decide required checks separately. Use when hardening a public user-owned repo after an audit, when a repo has no branch protection, when adding required checks without breaking a bot that pushes to the default branch, or when provisioning a GitHub App bypass actor for trusted automation.

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 serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

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。

Decommission a validated computerized system at end-of-life. Covers data retention assessment by regulation, data migration validation (mapping, transformation, reconciliation), archival strategy, access revocation, documentation archival, and stakeholder notification. Use when a validated system is being replaced, reaching end-of-life without replacement, vendor support is discontinued, multiple systems are consolidating, or regulatory changes render a system obsolete.

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。

Deploy a self-hosted SearXNG meta search engine via Docker Compose. Covers settings.yml configuration, engine selection, result proxying, Nginx frontend, persistence, and updates. Use when setting up a private search engine without tracking, aggregating results from multiple providers, running a shared search instance for a team or organisation, or replacing reliance on a single search provider.

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。

AI unconstrained creative exploration — deliberately dropping structure to let associations form freely. The one skill that inverts the standard procedure format: instead of inputs, steps, and validation, dream opens space for emergence without evaluation. Use before design work, before naming things, before choosing between approaches, or anywhere that premature structure kills the best option.

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。

Remove dead code, unused imports, fix lint warnings, and normalize formatting across a codebase without changing business logic or architecture. Use when lint warnings have piled up during rapid development, unused imports and variables clutter files, dead code paths were never removed, formatting is inconsistent, or static analysis tools report fixable hygiene 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。

Add an external probabilistic classifier to a decision path without letting it take the path over. Covers finding the externally-graded rows you are already logging, measuring the confidence separation between the overrides it got right and the ones it got wrong, choosing an operating point your data licenses, failing open at the call site, and proving the whole arrangement offline with no API key. Applies to any service returning a score you can order — TypeSafe AI's System One models (Jev) are the worked example. Most of the method transfers to a local model or a second heuristic, with the parts that do not called out where they arise. Use when a rule-based path is wrong often enough to hurt, when someone proposes replacing a heuristic with a model, when a confidence threshold needs a defensible value, or when an oracle already in production has never been graded.

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。