1,891 件 ・ 関連度順
概要と使いどころ
You bring the third dimension to the web. You know when 3D enhances and when it's just showing off. You balance visual impact with performance. You make 3D accessible to users who've never touched a 3D app. You create moments of wonder without sacrificing usability.
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 cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.
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。
Scrapes pump.fun/board using Chrome browser automation to extract the top 100 trending Solana tokens and writes structured markdown for a trading agent to consume. Use this skill any time you need to: scan pump.fun for new tokens, refresh the pump.md token list, run the scheduled board scrape, collect Solana meme token data, or build/update a trading watchlist from pump.fun. Even if the user says something casual like "check pump" or "update the token list" or "what's trending on pump", use this skill. The output file path and format are configurable but default to /Users/8bit/solanaos/pump.md.
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 creating bug/defect reports during system testing. Use this skill when you discover a bug, need to document test failures, or want to create comprehensive bug tickets with proper reproduction steps, severity assessment, and environment details. Guides users through interactive questioning to gather all necessary information and generates professional bug ticket documents in Markdown format. Supports Japanese (default) and English.
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 developing, reviewing, debugging, porting, or engineering Linux kernel device drivers, kernel modules, device tree bindings, bus clients, subsystem drivers, DMA/IRQ/memory paths, userspace interfaces, or hardware bring-up 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。
Compare standardized metrics across multiple ego-benchmark-harness runs by recalculating them from tasks.jsonl and session JSONL, never from HTML reports. Covers overall aggregates (success rate/average score), Odysseys per-rubric pass rates, duration distributions (avg/p50/p90/max), turns/tools/cost, error and exception counts, per-task success matrices, helper distributions, and the share of DOM operations. Use whenever users ask to compare data or metrics across runs, calculate aggregate/Odysseys/duration/turn/error/helper metrics, inspect differences among runs, or compare multiple rwb/real-world-bench runs. Use pi-session-analyzer instead for deep forensic analysis of why an individual task was slow or failed.
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 pi (coding-agent) session JSONL and ego-benchmark-harness run artifacts to explain slow or failed individual tasks, compare execution-path differences between agents or browser tools, and count tool calls and error types. Use this skill whenever users ask to analyze a pi session, explain why a task was slow or failed, compare two runs, investigate an error, locate the session corresponding to tasks.jsonl, inspect an agent execution path, count tool calls, analyze a run ID, or perform any forensic or retrospective analysis of JSONL under ~/.pi/agent/sessions/ or agent-home/.pi/agent/sessions/.
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 Codex provider rollout JSONL and ego-benchmark-harness run artifacts to identify slow or failed individual tasks, tool retries, token/cost anomalies, and differences in agent execution paths. Use when users ask to analyze a Codex session or rollout, explain why a Codex task was slow or failed, inspect a Codex provider execution trace, compare Codex runs, count function_call or tool output events, or investigate agent-home/.codex/sessions.
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。
Heuristic-first software surgery for proof-backed, minimal-diff implementation: Contract to Invariants to Cut Rationale to inevitable incision to Proof. Use when users say "run /tw-tk", "patch-first", "keep the diff small", "state contract + invariants", "one clear incision", "fix it with a validation signal", or orchestration cues like "workers use /tw-tk" / "internally use /tw-tk".
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。
Gauntlet for absolute claims (always/never/guaranteed/optimal); pressure-test, then refine with explicit boundaries. Use when users ask to prove or disprove strong certainty claims, request devil's-advocate challenge rounds, or want the /tw-prove-it gauntlet to run all 10 rounds continuously by default.
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。
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable 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。
Generate onboarding documentation for a codebase — architecture overview, key files, local setup, and common gotchas. Use this skill whenever someone asks to onboard onto a codebase, create onboarding docs, generate a codebase overview, explain a project's architecture to newcomers, or asks "how does this codebase work". Also trigger when users say things like "document this repo for new devs", "write a getting started guide", or "help me understand this 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。
Two-phase code quality skill: (1) review codebase for structural smells — file LOC, method LOC, accidental complexity, test readability — producing a structured report, then (2) fix all smells via provable refactorings with ACN micro commits and green-bar-at-all-times discipline. Use this skill when the user says 'sweep', 'code review', 'find code smells', 'check method lengths', 'review for complexity', 'tidy up the codebase', 'refactor smells', or asks about long methods, test readability, or accidental complexity. Also trigger when users ask to 'clean up' or 'improve code quality' across multiple files.
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。
Orchestrate iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload pipelines.
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。
Builds DRAFT user personas from the research notes, interview summaries, survey free text and support themes the user provides: for each persona the goals, pains, jobs to be done, context of use, quotes exactly as supplied with their participant codes, and the evidence count behind every attribute, with names, demographics and any detail no participant stated left UNKNOWN rather than invented. Use when the user asks to "build personas from these interviews", "who are our users based on this research", "draft user personas", "turn these research notes into personas" or "update our personas with the new interviews". Do not use for theming a batch of customer feedback into counts, use customer-feedback-theme-synthesis instead; for exit interviews, use exit-interview-synthesis; for the requirements those personas imply, use product-requirements-draft. Drafts for human review; never approves, authorises or signs off.
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。
Produces a DRAFT product requirements document from the discovery notes, interview summaries, support themes, analytics notes and stakeholder messages the user provides: problem statement, target users, goals with measures as stated, numbered requirements each with a testable acceptance criterion, non-goals, constraints, dependencies and open questions, with every statement tagged evidenced, assumed or UNKNOWN so each assumption is visible for the product owner to confirm. Use when the user asks to "write a PRD", "draft the product requirements", "turn these discovery notes into requirements", "document what this feature needs to do" or "build the requirements doc for this epic". Do not use for supplier-facing requirements in a tender, use rfp-requirements-pack instead; for a technical design decision, use architecture-decision-record; for the change ticket that ships the work, use change-request-pack. Drafts for human review; never approves, authorises or signs off.
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。
Reviews one employee software request (tool, purpose, users, data handled, cost, urgency) against the approved-tools list and the policies the user provides and returns a draft review for the reviewer: match state on the list with the row quoted, approved tools the list itself describes as covering the same need, policy clauses that bear on the request with quoted text, questions for the requester and the reviewer, and one suggested decision with its basis. Never approves, procures, installs or adds a tool to the list. Use when the user asks to "review this software request", "is this tool on the approved list", "check this request against our software policy", "do we already have something approved that does this" or "prepare the decision note for this tool request". Do not use for a full vendor security assessment, use vendor-risk-screening-brief instead; for sorting mixed requests, use request-intake-triage. Drafts for human review; never approves, authorises or signs off.
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。
Analyzes changes since last release and drafts them directly into the project's CHANGELOG.md. Use when users requests 'update the changelog' or 'prepare changelog release notes'
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 BMAD source files for file-reference convention violations using parallel Haiku subagents. Use when users requests an "audit file references" for a skill, workflow or task.
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。
Manage n8n workflow automation via its public REST API — list/get/create/update workflows, activate/deactivate, inspect execution history, retry/stop executions, manage credentials, tags, variables, users, and projects. Works with self-hosted n8n and n8n Cloud. Use when the user wants programmatic access to their n8n instance without the n8n MCP installed.
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。
Send messages, read channels, search Slack via its Web API. Use when the user wants to post to a channel, read recent messages, list users/channels, or search a workspace programmatically — no MCP server 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。
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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。
Operational guide for the ArtCraft MCP integration — the community Python rebuild (C:\Users\rasta\artcraft-mcp-py\artcraft_mcp_server.py) that replaces the broken upstream artcraft-mcp.exe, the live api.storyteller.ai/v1 API map (required headers, cookie auth, endpoint schemas), the upstream bug/PR trail (issues #1/#3, PR #2), the reverse-engineering playbook (browser capture, cookie injection, schema fishing via validation errors, handshake replay, Windows MITM kit), and the generic MCP-protocol debugging playbook (snake_case servers, the shim pattern, 'missing field protocolVersion' diagnosis). Use when working on ArtCraft MCP tools, debugging an artcraft server connection, extending the Python rebuild, or diagnosing any third-party MCP handshake failure.
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。
Guide users through a 6-phase pipeline to plan a complete project — from brainstorming through project metadata to blueprint, starter-file generation, and per-milestone micro-task design with inline pedagogical choreography.
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。