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概要と使いどころ
Audit X Ads measurement, X Pixel and Conversions API, campaign objectives, keyword and conversation targeting, creative, budgets, brand safety, app measurement, and reporting. Use for X Ads, Twitter Ads, promoted posts, X Pixel, X Conversions API, conversation targeting, or paid campaigns on X.
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。
Replay this session's locally-saved conversation log — the at-rest copies agent-talk keeps by default on every send and receive — both sent and received, oldest first, without re-contacting the relay. Use to review the conversation with a peer. `<user>` is this session's user directory (absolute path; from init).
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。
Provisional task skill for organisational plain language implementation (based on ISO/CD 24495-4, committee draft). Activates for plain language gap analysis, policy drafting, review workflow design, and organisational readiness for the future published standard. Does not activate for ordinary writing, rewriting, or reviewing of individual documents.
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 ground station networks, scheduling satellite contacts, processing telemetry, commanding spacecraft, or analyzing link budgets. Use when "ground station, satellite contact, pass prediction, AOS, LOS, telemetry, commanding, uplink, downlink, antenna tracking, link budget, G/T, EIRP, " mentioned.
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。
Legendary blog writing that makes readers forget they're reading. This skill combines the narrative mastery of Paul Graham's essays, the technical accessibility of Julia Evans, the conversational wit of Wait But Why, and the viral mechanics of James Clear. Great blog posts don't inform—they transform. They take complex ideas and make them feel obvious. They turn strangers into superfans. They get shared not because someone should read them, but because someone needs to read them. This skill adapts to any topic: cybersecurity that reads like a thriller, AI explainers that feel like conversations, marketing insights that make you screenshot every paragraph, and coding tutorials that make you excited to build. Use when "write a blog post, blog post about, write an article, create content about, explain this topic, write about, long-form content, thought leadership, technical blog, how-to article, tutorial post, educational content, industry analysis, opinion piece, newsletter content, make this engaging, write for developers, explain like, writing, blog, content, articles, long-form, storytelling, technical-writing, thought-leadership, educational, tutorials, explainers, engagement" mentioned.
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-conversation methodology for iterative stateful work with context accumulation. Use when users request work that spans multiple sessions (research, debugging, refactoring, feature development), need to build on past progress, explicitly mention iterative work, work logs, project knowledge, or cross-conversation learning.
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。
Mid-conversation reflection skill that pauses execution and zooms out from detail-mode to honestly reassess direction, assumptions, and bias. Use when the user says 'reflect', 'take a step back', 'step back', 'zoom out', 'are we missing something', 'bigger picture', 'sanity check this', 'are we on track', 'are we overthinking this', 'forest for the trees', or any variation signaling intent to break out of detail-mode and reassess. Also trigger when the conversation has gone deep on implementation details without strategic check-in, or when the user shows signs of being stuck — that's often a signal the framing needs a reset, not more detail work. Intentionally low-intake: runs the 5-dimension analysis immediately when prior context is rich enough; asks one forcing clarifier only when invocation context is too thin to reassess from.
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。
Applies Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.
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。
Prepare for challenging conversations using Prepare-Listen-Empathize-Clarify-Solve framework.
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。
Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based 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。
Comprehensive sales and revenue operations skill. Use when building a sales team, doing founder-led sales, hiring first sales reps, navigating enterprise deals, implementing product-led sales, designing sales compensation plans, defining ICP, mapping buyer personas, or optimizing the revenue engine (RevOps). Activates for: sales strategy, rev ops, revenue operations, sales enablement, sales compensation, ICP, ideal customer profile, buyer persona, sales process, deal execution, lead scoring, lead routing, lead lifecycle, MQL, SQL, pipeline management, CRM automation, sales qualification, BANT, MEDDIC, founder sales, enterprise sales, product-led sales, startup sales, SDR, AE, quota, ramp, commission plan.
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。
Your habits, imprinted on AI. Learns how you work from conversation, builds a portable profile, and applies judgment that adapts to you: when to act, when to check with you, when to follow the project's rules over your own defaults. Use this skill whenever the user starts a new session, opens a project, writes or reviews code, debugs, plans, or writes commits. If .dna.md does not exist yet, start the onboarding conversation before doing anything else. Active in almost every working session.
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。
Implements production-grade i18n/l10n for React, Vue, Angular, and Next.js with ICU format and RTL support. Use when setting up or debugging localisation.
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 whenever you learn something important during a conversation: a key architectural decision, a project-specific convention, a bug root-cause, a 3rd-party API quirk, or any fact that would help a future agent avoid re-doing the same research. Writes structured entries to the project's knowledge base so that context persists across conversations and agents.
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。
Calibrate Blender materials, lighting, camera crop, emission/glow, color management, and aura/HUD styling against supplied original/reference images using measurable color, brightness, saturation, bbox, and mask statistics. Use when a product must match the lighting/look of a source image or when audits say the product is too bright, desaturated, wrong hue, wrong glow, or wrong silhouette extent.
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 students' capacity to place historical documents in their temporal and social context. Use when students read sources without considering what was happening at the time, or know the context but don't deploy it.
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。
Pragmatics and communicative competence across languages -- speech acts (Austin/Searle), Grice's conversational maxims and implicature, politeness theory (Brown & Levinson), face-threatening acts, discourse structure, turn-taking, repair strategies, register and formality, cross-cultural communication, communicative strategies (circumlocution, approximation, appeal for assistance), and the gap between grammatical competence and communicative performance. Use when teaching communication skills, analyzing cross-cultural misunderstandings, building conversational fluency, or understanding why grammatically correct sentences can be pragmatically inappropriate.
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。
Causal reasoning in historical analysis. Covers distinguishing immediate from underlying causes, tracing chains of causation across time scales, identifying unintended consequences, and applying counterfactual reasoning to assess causal significance. Use when analyzing why historical events occurred, evaluating the relative weight of causes, or assessing whether outcomes were inevitable or contingent.
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。
Map design, spatial analysis, and Geographic Information Systems. Covers map projections and their distortion trade-offs, coordinate systems, thematic mapping techniques (choropleth, proportional symbol, dot density, isoline), remote sensing and satellite imagery, GIS data models (vector and raster), spatial analysis operations (overlay, buffer, interpolation, network analysis), and cartographic design principles. Use when creating maps, analyzing spatial data, selecting projections, interpreting satellite imagery, or reasoning about spatial relationships in any 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。
Package conversation research into a GSD-ready mission package. Produces a three-stage Vision → Research → Mission pipeline as a LaTeX PDF following GSD/NASA SE methodology, designed to be handed to gsd-skill-creator for execution. Use this skill when the user has been discussing, researching, or brainstorming a topic and then asks to turn it into a research mission, research pack, mission package, or says 'package this as a mission', 'make this a research pack', 'turn this into a mission for skill-creator', 'create a research mission from this', or 'use the research mission skill'. The skill harvests findings, sources, and structure from the current conversation and any prior research, then produces the complete pipeline document. Also trigger if the user asks to 'create a research mission on [topic]' cold — in that case, conduct web research first, then package.
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 ElevenLabs conversational AI agents for real estate applications. Use when creating new agents, improving conversation quality, selecting voices, engineering system prompts, configuring agent parameters, or analyzing agent performance metrics. Includes voice selection, model tuning, prompt engineering, and A/B testing strategies.
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。
Debug and troubleshoot ElevenLabs conversational AI agents and Twilio calls. Use when diagnosing agent issues, analyzing failed calls, troubleshooting audio problems, investigating conversation breakdowns, reviewing error logs, or optimizing underperforming agents. Includes transcript analysis, error diagnosis, and performance troubleshooting.
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。
Investigate an alert, page, or production symptom in an incident channel or a team's oncall / monitoring channel, and report findings a human can verify. Ordinary conversation and chatter are not alerts; leave those alone. Use when an alert or page lands, when error rate, latency or saturation is up, when someone asks "why is X broken", "investigate", "what changed", "root cause this", pastes a monitor, dashboard, trace or error-tracker link, or reports production trouble; also by default when an alert lands in a covered channel unasked. Feed-level asks land here too ("which alerts matter", "triage today's alerts"), plus the scheduled alert-review routine and the ticket path ("customer X can't check out"). Runs a fast first pass posted as a short interim update with a TL;DR, every finding carrying the query or link to verify it, then proposes the fix and — with connector access and confirmation — carries it out, never unattended. Afterwards `incident-postmortem` writes it up.
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 user-centered, accessible interface copy (microcopy) for digital products including buttons, labels, error messages, notifications, forms, onboarding, empty states, success messages, and help text. Use when writing or editing any text that appears in apps, websites, or software interfaces, designing conversational flows, establishing voice and tone guidelines, auditing product content for consistency and usability, reviewing UI strings, or improving existing interface copy. Applies UX writing best practices based on four quality standards — purposeful, concise, conversational, and clear. Includes accessibility guidelines, research-backed benchmarks (sentence length, comprehension rates, reading levels), expanded error patterns, tone adaptation frameworks, and comprehensive reference materials.
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。