2,011 件 ・ 関連度順
概要と使いどころ
Analyze folders of Markdown/text research notes, Obsidian vaults, paper cards, invention notes, or memory exports as a practical knowledge map. Use when asked to find center topics, noisy or low-signal notes, possible contamination, overgrown topic groups, orphan notes, cleanup actions, or a useful public knowledge-hygiene workflow.
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 implementation is complete, all tests pass, and you need to decide how to integrate the work — guides completion of development work by presenting structured options for merge, PR, or cleanup
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。
Clean editorial-style interfaces. Warm monochrome palette, typographic contrast, flat bento grids, muted pastels. No gradients, no heavy shadows.
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。
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
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。
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.
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。
Create structured GitHub issues from text, screenshots, or lists, with acceptance criteria and preserved reporter context. Use for filing bugs/features, batch creation, or template cleanup. Don't use for resolving, triaging, or deep issue analysis.
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 a PR end-to-end with CI checks, fix cycles, and optional auto-merge. Use for PR review, cleanup, or readiness checks. Don't use for creating PRs, raw issue analysis, or non-PR code 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。
Master software architect specializing in modern architecture patterns, clean architecture, microservices, event-driven systems, and DDD. Reviews system designs and code changes for architectural integrity, scalability, and maintainability. Use PROACTIVELY for architectural 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。
Implement proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design. Use when architecting complex backend systems or refactoring existing applications for better 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。
Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser 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。
Set up, verify, and clean up Stripe test data end to end. Use when creating test customers, products, or subscriptions and you want a repeatable setup-then-teardown workflow, or when you need to remove leftover test customers and products with delete_stripe_test_customers, archive_stripe_test_products, and delete_stripe_test_products.
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 the card's Review Hero loop by hand — trigger a review, address its comments, rerun until clean, then merge. Use in an external tool (Claude Code, Cursor) where Workhorse's automated loop isn't running.
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。
Production-quality Bash scripts for Solana vanity generation, keypair verification, batch operations, dependency auditing, and test orchestration — with security-hardened patterns including file permissions, input validation, and cleanup traps.
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。
Convert PDF documents to clean structured Markdown for LLM context. Supports two modes: fast (PyMuPDF) and accurate (IBM Docling TableFormer AI). Features aggressive persistent caching, image extraction with metadata, table detection, and batch processing. Use when asked to convert PDFs, extract PDF content, parse documents, or prepare PDF data for AI/LLM consumption.
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。
Development methodology rules (Clean Architecture, TDD flow, 3-Strike rule, decision priority) that govern planning, implementation, and review. Load before designing, staging, implementing, or reviewing code changes.
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。
Disciplined diagnosis loop for hard bugs, regressions, flaky failures, and performance problems with unclear cause. Use for root-cause debugging after a concrete symptom exists; do not use for routine implementation or speculative cleanup.
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 designing, evaluating, documenting, modernizing, reviewing, refactoring, or implementing software architecture and code-level structure, including requirements, context, architecture style selection, microservices patterns, WebAssembly/WASI/Component Model, UMA portable capabilities, runtime contracts, capability graphs, governed MCP/agent orchestration, clean code, code smells, Rust crate/library API architecture, Cargo feature/MSRV/SemVer policy, unsafe/FFI/no_std/library testing, modularity, views, NFR tactics, IAM/auth, security patterns, cost estimation, architecture-as-code fitness functions, ADR/RFC work, and engineering execution plans.
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。
Post-fix verification — re-scan the repo, gate on `--exploits weaponized --severity high`, recheck the specific CVE against the new installed version, write the verdict to `.vulnetix/memory.yaml`. Use when confirming a fix landed, validating a version bump did not introduce regressions, or producing a clean-scan attestation for compliance.
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。
Control MARS/Innate robots over rosbridge WebSocket. Covers connecting to the robot, executing skills, moving the arm, driving the base, tilting the head, and reading sensors — all via JSON-over-WebSocket to rosbridge_server on port 9090. Use this skill whenever working with MARS robots, Innate OS, rosbridge, ROS2 robot control, maurice_arm, brain_client, or any project that talks to a robot over WebSocket. Also trigger when seeing imports from brain_client, brain_messages, maurice_msgs, or references to topics like /cmd_vel, /mars/arm, /brain/chat_in, /execute_skill.
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 the document of a research run — plan the question and the outline with rs_plan, write prose and declare its claims together with rs_write, choose the claim strength the evidence actually holds, follow the section structure the run's kind maps to, read the draft back, and build a watermarked draft before the gated clean build. Use when a run's document has to be written, a section drafted, a claim bound to a source, or a build attempted.
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。
Hand a research run over as a file — read `rs_open action=capabilities` for the formats this build writes, choose between `md`, `docx`, a self-contained `html`, the `ris`/`bib`/`enw` library exports and `tex`, pick `draft=watermark` while working or `draft=clean` to hand it over, and read `build/build.json` for what was built from which bytes. Use when a document is finished enough to leave the run, when a citation style or an export has to be chosen, or when a build refuses.
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。
Work the verification ladder of a research run with rs_verify — read the ladder report before claiming anything is checked, run the two file rungs (r1-record, r2-locate) that need no reader, record the r3-support verdicts and strength verdicts that only a reader can make, tell a miss from an inconclusive, and know what a clean build will still refuse. Use when a document is written and about to be called checked, when a claim's quote does not match its source, or when a gate or build answers with an integrity refusal.
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 starts a non-trivial coding/building task in Claude Code (e.g. "帮我做个 X", "help me build Y", "add feature Z", "搭一个 Z"). Layers an AI-native pedagogical coat over normal coding work — surfaces implicit assumptions in user intent, annotates non-trivial design decisions inline with brief concept-scaffolding for terms the user may not know, and replaces "should I run it?" handoffs with rigorous AI self-verification plus a clean list of what only the user can verify. Skip for pure Q&A, single-line edits, read-only investigation, or refactoring along an existing pattern.
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。
Arlo Belshee의 6단계 네이밍 프로세스와 Clean Code 네이밍 규칙을 적용해, 여러 파일에 흩어진 개별 이름 스멜(타입 반복·-er/-Utils·불용어·축약어 등)을 탐지하고 배치 교체하여 코드 가독성 향상. 단, 하나의 긴 메서드를 통째로 관통(grouping→extract→rename)하는 것은 /intent-revealing-names가 적합. /naming-process로 호출.
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。