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概要と使いどころ
Use this agent when you need to synthesize and distill the core functions of a gene from multiple information sources (textual summaries, reviewed annotations, bioinformatics analysis) and populate the `core_functions` section of a gene review YAML file. This agent should be called after completing the gene research, annotation review, and bioinformatics analysis phases of a gene review project.
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。
Run Flutter/Dart formatting and static analysis for this repository. Use after completing Dart, Flutter, generated-source-input, or project configuration changes; when the user asks to lint, format, analyze, or check code style; or when preparing code for a pull request.
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。
Establish project thesis before first code. Infer product type from domain/folder/README. Identify users, business model, pSEO strategy, AI-native dev approach. Maintain PROJECT_BRIEF.md as source of truth.
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。
Server-Sent Events for unidirectional server-to-client streaming, EventSource API, fetch streaming, reconnection patterns, message parsing
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。
Mapbox GL JS interactive vector maps. Use when building a map with sources and layers, expressions, clustering, 3D terrain, geocoding or directions.
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。
SolidJS fine-grained reactivity — signals, effects, memos, stores, resources, control-flow components. Load when writing Solid components or reactive state.
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。
Angular 17–19 standalone components, signals, built-in control flow, inject() DI, the resource API. Load when writing or migrating Angular components.
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。
SST (Ion) infrastructure-as-code — TypeScript-first serverless on AWS with Pulumi, resource linking, and live Lambda dev
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。
GraphQL API server with Apollo Server — schema, resolvers, context, error handling, data sources, plugins
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。
Pino, Axiom, Sentry installation - one-time project setup for logging and error tracking with source maps upload
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。
Kubernetes manifests, Helm charts, Kustomize overlays, and resource 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。
Open-source headless CMS — content type schemas, REST API, Document Service API, custom controllers, lifecycle hooks, authentication, TypeScript
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。
A set of resources to help me write all kinds of internal communications, using the formats that my company likes to use. Claude should use this skill whenever asked to write some sort of internal communications (status reports, leadership updates, 3P updates, company newsletters, FAQs, incident reports, project updates, etc.).
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 what people have discussed in the last 30 days across high-signal public platforms. Use when the user asks for `/last30days`, recent trend research, competitive intelligence, market pulse briefs, or engagement-ranked synthesis from Reddit, Hacker News, GitHub, X, YouTube, Polymarket, and similar sources.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
Re-read pinned on-chain facts and watch documentary source fingerprints behind failure scenarios monthly, reporting drift without approving or changing scenario records automatically.
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。
Audit and repair Pharos documentation against the codebase. Use for documentation update passes, verified-doc drift, source-path/doc-sync failures, methodology/doc routing updates, or requests to verify docs without trusting existing prose.
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 writing or editing Storybook MDX docs (*.mdx) — the two-tab Overview/Implementation structure, story-backed `<Source>` examples, and doc table guidelines.
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 creating or editing Storybook stories (*.stories.tsx, React or React Native) — story layout, docs source type, controls, and export naming conventions.
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 hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.
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。
Systematic fuzzing across two dimensions: (1) content discovery — hidden directories, files, backup/source leaks, HTTP verb enumeration, 401/403 bypass via path manipulation; (2) input validation — auth/token stripping, type confusion, boundary value analysis, HTTP parameter pollution, header fuzzing, cookie fuzzing, mass assignment discovery, and entropy/predictability analysis of any generated value. Works on any domain. Chains from /web-exploit or /pentester; chains into /business-logic when boundary violations, predictable IDs, or mass assignment are confirmed.
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。
Exploit AI applications using Indirect Prompt Injection. This skill focuses on hiding malicious instructions within data sources (web pages, documents, emails) that the LLM processes, causing the AI to execute unintended actions or leak data without direct user interaction.
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。
Test for indirect prompt injection vulnerabilities where malicious instructions are injected through external data sources (websites, emails, documents, database records) that the LLM processes. Use this skill when assessing LLM-integrated applications that process user-generated content, retrieve web pages, parse emails, or read documents. Covers injection via web content, email bodies, user profiles, and database records that are fed to LLM context.
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。
Execute Indirect Prompt Injection attacks against Large Language Models (LLMs) by subtly embedding malicious instructions within external data sources (e.g., websites, documents, databases) that the LLM autonomously ingests. This forces the model to execute attacker-controlled commands under the guise of processing legitimate user requests.
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。