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概要と使いどころ
Analyze meeting transcripts to extract decisions, action items, owners, due dates, open questions, and risks. Use after recorded meetings, sales calls, customer interviews, or planning sessions, or to build a decision log.
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。
Technical leadership guidance for engineering teams, architecture decisions, and technology strategy. Use when assessing technical debt, scaling engineering teams, evaluating technologies, or making architecture 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。
Executive leadership guidance for strategic decisions, org development, and stakeholder management. Use when planning strategy, preparing board presentations, managing investors, or making executive 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。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing context. For applying psychology to specific pages, see cro; for pricing tactics, see pricing; for copy framing, see copywriting.
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。
Expert TSA cybersecurity compliance advisor for critical infrastructure owners and operators. Use this skill whenever a user asks about TSA Security Directives for pipelines, freight railroads, passenger rail, public transit, or bus operators; the TSA Cyber Risk Management Program (CRMP); Cybersecurity Implementation Plan (CIP); Cybersecurity Operational Implementation Plan (COIP); Cybersecurity Assessment Plan (CAP); incident reporting to CISA; designation of a Cybersecurity Coordinator; Critical Cyber Systems (CCS); OT/IT network segmentation; the TSA November 2024 NPRM; or any directive in the SD Pipeline-2021 series, SD 1580-21-01 (freight rail), or SD 1582-21-01 (public transit/passenger rail). Also trigger for questions like "are we covered by TSA directives?", "what does the TSA require for pipeline cybersecurity?", "how do I build a CIP?", "what must I report to CISA?", or any request involving transportation critical infrastructure cybersecurity 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。
Search and cite French case law and statutes with the LibreJustice MCP tools. Use whenever the user asks about French or European court decisions (Cour de cassation, Conseil d'État, cours d'appel, TJ/TA, CNDA, Conseil constitutionnel, CNIL, CEDH, CJUE), needs authorities for a brief, requête, référé or consultation, wants to check how courts rule on an issue, needs counts of decisions on a point, or asks what a code article says at a given date, even if they never mention LibreJustice.
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 detailed law-school case briefs of United States judicial opinions in a fixed nine-section structure: memory jogger, facts, procedural history, judicial votes, holding, opinion-by-opinion analysis, five future-application hypotheticals, critique, and verified key quotations. Also builds vote tables, cross-case consistency tables, perspective-based critiques, and LaTeX in article, Beamer and casebook-chapter formats. Use whenever someone asks to brief a case, analyse or explain an opinion, summarise a decision for students, generate hypotheticals from a holding, critique a judgment, tabulate how justices voted, or convert case analysis to LaTeX. Triggers on "brief this case", "case brief", "explain the holding", "what did the court hold", "hypotheticals based on", "critique this opinion", "vote table", "Beamer presentation", "chapter brief", and on any reference to a judicial opinion or Supreme Court decision in a teaching context. Requires web search to verify quotations, votes and holdings.
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 team's shared, AI-ready knowledge base — people, companies, meetings, SOPs, and decisions structured so an agent can answer on your team's behalf. Team-scope sibling to second-brain. Modes — capture, compile (wiki pages + INDEX.md), query (trust-weighted, saved to outputs/), review (verify / deprecate / supersede stale captures), lint, connect, search. Structured raw dirs (people/, companies/, meetings/, sops/, decisions/, customer-language/, sales-objections/). Every capture stamps author, timestamp, and trust status. Optional sync from call transcripts, Slack/email exports, CRM. Vault at ${COMPANY_BRAIN_VAULT:-$HOME/Documents/CompanyBrain}/. Triggers on "/company-brain," "/cb," "capture this into the team brain," "log this meeting," "save this SOP," "compile the company wiki," "query the team brain," "what does the team know about X," "review the company brain," "lint the company brain," "who's the internal expert 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。
Expert Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-processing.
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。
Expert blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.
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。
AI movement decision router for chase, patrol, crowd, and bake choices on top of NavigationAgent/Server. Use when deciding node agent vs RID server, bake vs obstacle, layer masks, or retarget policy — not for engine navmesh recipes. Keywords: AI navigation, chase retarget, patrol, crowd RVO, bake vs obstacle, NavigationAgent decision tree.
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。
Expert Godot shader patterns for batch-safe hitflash, alpha-scissor foliage/dissolve, screenspace postFX, depth reconstruction, triplanar, and instance uniforms — not first-shader tutorials. Trigger on draw-call batching breaks, discard vs depth-prepass, foliage shadows, post-process quads, or world-position FX. Keywords: instance uniform, ALPHA_SCISSOR, hint_screen_texture, hint_depth_texture, global uniform, sampler2DArray, canvas_item, spatial, post-processing.
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。
Expert blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.
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。
AI movement decision router for chase, patrol, crowd, and bake choices on top of NavigationAgent/Server. Use when deciding node agent vs RID server, bake vs obstacle, layer masks, or retarget policy — not for engine navmesh recipes. Keywords: AI navigation, chase retarget, patrol, crowd RVO, bake vs obstacle, NavigationAgent decision tree.
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。
Grilling session that challenges your plan against the existing domain model, sharpens terminology, and updates documentation (CONTEXT.md, ADRs) inline as decisions crystallise. Use when user wants to stress-test a plan against their project's language and documented 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。
Interpret app metrics and make data-driven decisions. Covers DAU/MAU, retention, LTV, ARPU, App Store Connect analytics, AARRR funnel analysis, cohort analysis, and diagnostic decision trees. Use when user wants to understand their metrics, diagnose problems, or build a data-driven growth 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。
Capture an architectural decision so the next session (or engineer) knows WHY. Use after any non-obvious technical choice.
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。
Before compaction Loopkit extracts decisions into claude-decisions.json (machine-readable). Read it alongside claude-progress.txt at session start — prose is for humans, JSON is for the loop.
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。
Search Sutochno.ru (СУТОЧНО.РУ), a Russian short-term apartment-rental platform where you book directly from owners and pay in rubles (Mir card / bank transfer). Best for Russian-speaking travellers booking stays in Georgia / the CIS, where Airbnb and Booking have withdrawn or do not accept Russian cards. Sutochno is NOT behind a bot wall — it ships on the toolkit standard patchright-docker base but runs the bundled chromium headless with no Akamai sensor to defeat. Trigger phrases include search Sutochno, суточно, Russian apartment rental, ruble payment, Mir card, Tbilisi apartment, Batumi apartment, Georgia CIS rental.
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 HTML flowcharts and process diagrams with decision trees, color-coded stages, arrows, and swimlanes. Use when users request flowcharts, process diagrams, workflow visualizations, or decision trees.
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 scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
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。
Dung khi co data tho tu Meta, TikTok, GA4, CRM hay Google Sheet va can bien thanh insight ra quyet dinh duoc — kem decision log ghi quyet dinh, can cu, tac dong ky vong va ngay review lai. Kich hoat khi user nhac 'phan tich du lieu', 'doc so lieu nay', 'export data ra insight', 'so nay noi len dieu gi', 'lam decision log', 'cohort analysis'. Khong dung cho — chan doan tai sao ads kem thi dung skill 03-danh-gia-hieu-suat; viet bao cao trinh bay thi dung skill 07-bao-cao-marketing.
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 raw data exists — Meta, Google, TikTok, GA4, Shopify, a CRM export, or a spreadsheet — and has to become insight and decisions: descriptive, diagnostic, predictive, and prescriptive layers, cuts by channel, campaign, creative, audience, and time, cohorts, and a decision log. Trigger on 'analyze this data', 'read these numbers for me', 'what does this export say', 'pull insight from GA4', 'cohort analysis', 'here is the spreadsheet'. Also use when the user pastes a table and asks what it means. Not for — diagnosing ad root cause, see `03-performance-eval-global`; writing the report a stakeholder reads, see `07-marketing-report-global`; auditing account setup, see `21-ads-audit-global`.
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 phase has unresolved decisions before planning and no CONTEXT.md exists. Asks adaptive questions on the phase's gray areas and writes {phase_num}-CONTEXT.md with locked decisions; --auto, --chain, --power change the questioning.
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。