「hn」の検索結果

Writing effective code documentation - API docs, README files, inline comments, and technical guides. Use for documenting codebases, APIs, or writing developer guides.

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。

Code refactoring patterns and techniques for improving code quality without changing behavior. Use for cleaning up legacy code, reducing complexity, or improving maintainability.

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。

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated 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。

Digital forensics techniques for detecting device compromise, analyzing malware indicators, and investigating targeted surveillance. Based on methodologies from Citizen Lab's Pegasus Project and Predator investigations.

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。

Botnet architecture taxonomy, detection techniques, and defensive analysis. Covers C2 topologies, DGA analysis, fast-flux detection, P2P overlay mapping, blockchain C2, and ML-based traffic fingerprinting. Defensive research for understanding and countering distributed malicious infrastructure.

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。

Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.

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。

Optimize AgentDB with quantization, HNSW tuning, caching, batch ops, and pruning.

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。

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

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 "plan chunks", "break this down", "plan the implementation", or when a story has status planning and needs implementation details before building. Required before any story can be implemented. Transforms story sparks into detailed chunk-by-chunk implementation plans with full technical specifics, file lists, and risk analysis. Supports parallel planning of multiple stories via batch mode.

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 primary outcome is comparing documentation claims with implementation evidence, synchronizing docs from verified changes, or freshness review, including drift, missing coverage, stale claims, and post-change updates.

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 explicitly asks Rewrite Room to route documentation work, or when audit/sync/freshness, user-facing authoring, citation-driven writing, docs-to-skill conversion, and AI-instruction optimization overlap and exactly one workflow must be chosen.

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。

Autonomous feature research and gap analysis. Use when starting /add-new-feature or analyzing existing architecture documents. Explores codebase patterns, identifies ambiguities, and produces feature-context-{slug}.md for orchestrator RT-ICA phase. Does NOT make technical decisions.

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。

Per-project plugin configuration via .local.md files — covers the .claude/plugin-name.local.md pattern for storing user-configurable settings with YAML frontmatter and markdown body. Use when implementing plugin settings, reading YAML frontmatter from hooks, creating configuration-driven behavior, managing agent state files, or adding per-project plugin configuration. Covers file structure, parsing techniques, common patterns (temporarily active hooks, agent state management, configuration-driven behavior), security considerations, and best 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。

Fetch and report current API syntax, changelog entries, and breaking changes for a specific library or protocol version. One research angle within a parallel technical-research set — runs independently and returns a structured cited report. Invoke when a specific library name and version are the target.

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。

Research community usage patterns, real-world gotchas, and client compatibility for a specific known library, tool, or protocol feature. Use when a technical-researcher orchestrator needs community-sourced evidence about a named library or feature — bug reports, workarounds, compatibility gaps, and patterns from issue trackers and discussions. Distinct from the broad ecosystem-researcher agent — this skill targets a KNOWN entity and mines what real users have actually experienced, not what exists in a domain.

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 branches and generate structured MR/PR descriptions with domain-based change categorization — bug fixes, enhancements, technical debt, documentation, testing, build/CI, and non-functional changes. Use when preparing merge request descriptions, pull request bodies, writing changelogs from git diffs, documenting branch changes, or generating release notes. Works with GitHub and GitLab without requiring JIRA or issue tracker integration.

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。

Explains Chain of Verification (CoVe) prompt design — a 4-step pattern separating generation from independent factual verification. Use when designing prompts that require factual accuracy, reducing hallucinations, checking technical standards or APIs, or producing step-by-step procedures where subtle errors are costly.

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。

Transforms validated /linear-walkthrough artifacts into one presentation-ready deck outline per major codebase component. Use when the user asks to create a presentation, prepare a walkthrough deck, build an onboarding deck, produce an architecture review deck, or summarize a codebase for a technical audience. Requires walkthrough directory output -- reads unified walkthrough, per-section files, validation reports, coverage maps, entry points, and open questions -- then orchestrates four parallel agent phases to produce slide outlines with speaker notes, evidence references, and suggested visuals.

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 set of candidate Skills or app capabilities is technically valid but still needs to become a clear, useful ChatGPT/Codex Plugin that people can understand, discover, and invoke for real work.

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 and diagnoses list experiments (the item count technique), covering whether indirect measurement is warranted, control-list construction against floor and ceiling effects, design variants, estimators and assumption tests in the list package, and power planning for the precision penalty. Use when the user measures a sensitive attitude or behavior and asks about list experiments, item count, unmatched count, or veiled questioning, or has an implausible prevalence estimate. Deciding whether sensitivity bias exists at all goes to survey-design.

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 and diagnoses list experiments (the item count technique): whether indirect measurement is warranted, control-list construction, design variants, estimators and assumption tests in the list package, and power. Use when measuring a sensitive item or debugging an implausible prevalence estimate.

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 reviewing a skillsaw PR. Dispatches 7 specialist reviewers (Architecture, Python Expert, Security & Supply Chain, QA Engineer, Technical Writer, Ecosystem, Slopinator) as parallel sub-agents by default, then synthesizes a single verdict. Use --serial for cheaper inline execution.

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。

Review documentation for quality, clarity, SEO, and technical correctness. Optimized for Docus/Nuxt Content but works with any Markdown documentation. Use when asked to: "review docs", "check documentation", "audit docs", "validate documentation", "improve docs quality", "analyze documentation", "check my docs", "review my documentation pages", "validate MDC syntax", "check for SEO issues", "analyze doc structure". Provides actionable recommendations categorized by priority (Critical, Important, Nice-to-have).

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。