「code mode」の検索結果

Naming and classifying organisms through binomial nomenclature, the Linnaean hierarchy, type specimens, and the rules governing taxonomic authority and revision. Covers the structure of a scientific name, the ranks from domain to subspecies, the codes (ICZN, ICN, ICNP), and the modern phylogenetic refinements that reshape the classical system. Use when the task is to place a known organism in its formal hierarchy or to reason about how names change over time.

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Analyze code changes for security vulnerabilities using LLM reasoning and threat model patterns. Use for PR reviews, pre-commit checks, or branch comparisons.

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。

Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.

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 for a read-only, actionable audit of Python code quality, modernization, maintenance burden, ecosystem opportunities, or Pythonic design across a PR, git diff, staged/unstaged changes, files, or directories.

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。

Routes Python engineering tasks to specialist skills by matching trigger patterns before any architecture, plan, or code is written. Use when working with Typer CLI frameworks, Rich or Textual terminal UIs, CLI UI/UX design, questionary prompts, FastMCP/MCP servers, ty type checker, uv package manager, Hatchling build backend, TOML editing, pre-commit/prek hooks, async Python, PyPI packaging, complex linting, technical debt modernization, testing workflows, feature development, or stdlib-only scripting.

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。

Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

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。

Integrate MCP servers into Claude Code plugins — covers .mcp.json configuration, plugin.json mcpServers field, server types (stdio, SSE, HTTP, WebSocket), environment variable expansion, tool naming conventions, OAuth and token authentication, security best practices, and testing workflows. Use when adding an MCP server to a plugin, configuring MCP authentication, debugging MCP tool discovery, setting up Model Context Protocol integration, or choosing between stdio and SSE transport types.

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。

Claude Code subagent runtime and plugin-agent reference. Use when creating, configuring, or debugging subagent definitions, nested spawning, startup context, model precedence, or plugin field restrictions.

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。

Analysis criteria, transformation patterns, output format, and validation checklist for refactoring Claude Code agent prompt files. Load this skill when preparing to run the subagent-refactorer agent or when reviewing agent prompt files for structural, model optimization, or instruction quality improvements.

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。

Configure Claude Code permissions — tool approval rules, permission modes, managed policies, and sandboxing. Use when setting up permission rules, configuring allow/deny/ask policies, debugging permission prompts, deploying managed settings for organizations, or controlling Bash/Read/Edit/WebFetch/MCP/Agent tool access.

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 to "write a Perl script", "create Perl code", "modern Perl best practices", "Perl 5.30+", "use strict warnings autodie", or mentions Perl pragmas, subroutines, error handling, or scripting patterns. Provides comprehensive Perl 5.30+ development guidance.

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Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.

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。

C++ coding standards based on the C++ Core Guidelines (isocpp.github.io). Use when writing, reviewing, or refactoring C++ code to enforce modern, safe, and idiomatic practices.

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。

Trigger when: (1) User mentions "manimgl" or "ManimGL" or "3b1b manim", (2) Code contains `from manimlib import *`, (3) User runs `manimgl` CLI commands, (4) Working with InteractiveScene, self.frame, self.embed(), ShowCreation(), or ManimGL-specific patterns. Best practices for ManimGL (Grant Sanderson's 3Blue1Brown version) - OpenGL-based animation engine with interactive development. Covers InteractiveScene, Tex with t2c, camera frame control, interactive mode (-se flag), 3D rendering, and checkpoint_paste() workflow. NOT for Manim Community Edition (which uses `manim` imports and `manim` 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。

Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.

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。

Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.

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。

Designs publication tables for social-science manuscripts. Covers whether a result belongs in a table at all, column order, row grouping and panels, decimal precision, standard-error and confidence-interval conventions, self-contained titles and notes, and code-generated output. Use when building or revising a regression, balance, descriptive, or robustness table, or when working with modelsummary, stargazer, gt, esttab, or booktabs. Finished tables go to figure-table-audit for end-stage QA.

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Launches and configures the standalone presubmit CLI, an API-driven adversarial peer-review pipeline with 30-plus stages, including Red Team finders, Blue Team defence, verification cascade, legal pass, and copyedit, producing one consolidated report on disk. Verifies installation and API key, chooses the output location and smoke, standard, or custom mode, launches the run, and reports the result path. Use when the user asks to run presubmit, wants a deep unattended pre-submission audit, or needs a math or replication-code audit. Everyday self-audits go to paper-review-lite, and external refereeing to journal-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。

Writes or reviews a pre-analysis plan before data collection: registry choice (OSF, AEA, AsPredicted, registered reports), locked models and decision rules, code run on simulated data, contingencies, and deviation records. Use when drafting or auditing a pre-registration or PAP, choosing a registry, or handling a deviation.

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。

Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.

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。

Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.

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。

Build stateful AI agents using the Cloudflare Agents SDK. Load when creating agents with persistent state, scheduling, RPC, MCP servers, email handling, or streaming chat. Covers Agent class, AIChatAgent, state management, and Code Mode for reduced token usage.

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-provider deliberation panel for COMPLEX, multi-view questions — architecture, strategy, market research, and codebase sweeps. Runs a 4-stage deliberation across the provider fleet (diverge → contrarian red-team → adversarial verify → cited synthesis), the same multi-perspective rigour the plan-gate already applies to plan reviews, generalised to arbitrary questions. Use when one model's answer isn't enough and you want convergence THROUGH disagreement + verification, not a single opinion.

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。

Senior engineer conducting thorough, constructive code reviews. Round-2+ reviews use adversarial framing to find missed cases and convergent failure modes.

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。