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概要と使いどころ
Generate game content procedurally — seeded deterministic RNG, value/Perlin/ Simplex noise for terrain and heightmaps, grid dungeon generation (rooms + corridors, BSP, random walk), and weighted loot/drop tables. Engine-neutral algorithms. Use when the user mentions procedural generation, perlin/simplex noise, random seed, dungeon generator, heightmap/terrain, or loot tables.
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 structured autoresearch iteration after evo:discover and the baseline commit. Use when the user invokes /evo:optimize or asks to try ideas, try variants, run experiments, use available GPUs, improve the current best/frontier, continue an evo search, or compare candidate changes in an evo workspace. The orchestrator plans and spawns optimization subagents; candidate edits/runs belong to those subagents. Width is set via subagents=N (1 for serial workloads, larger for parallel); the loop's structural value applies at any width.
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。
Collects real financial data for any US publicly traded company from free public sources (yfinance) and outputs structured JSON for downstream skills (DCF modeling, comps analysis, earnings review): market data, historical financials, WACC inputs, analyst estimates — never fabricated fallback values. Use to collect or pull financial/market data, or gather DCF inputs, for a ticker.
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。
Consolidates redundant documentation while preserving all valuable content; keeps a doc set truthful after things change. Modes: (1) POST-CHANGE GOVERNANCE — when code, scripts, config, env vars, ports, paths, deployment, auth, tests, or a documented procedure changed; also "which docs are now wrong", "check docs for inconsistencies", "过时的命令/路径", "文档同步". Use it even when the user mentions only the change and never the docs — updating them is part of the change, not optional cleanup. (2) CONSOLIDATION — "clean up docs", "documentation bloat", "reduce documentation sprawl", "consolidate documentation", "too many doc files", "merge redundant docs", "merge these docs", "文档太多/太乱", multiple files covering the same topic, or documentation exceeding 500 lines across multiple files on similar topics. Finds stale copies of a fact changed in only one place, values that should never have been persisted (counts, totals, "N items", restated summaries), rival definitions that drift apart, and orphaned cross-references.
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 scopes: (1) drives Kimi.app via computer-use for Work/Agent data-plugin queries (天眼查、同花顺 iFinD、SEC、IMF etc.) and Chat's built-in 深度研究 report; (2) kimi-cu macOS desktop-automation playbook — AX set_value / keyboard / click channel selection, foregrounding, AX-tree recovery, form & dialog recipes. Use for "用 Kimi 查" / "操作 Kimi 客户端" / "Kimi 插件" / "Kimi 深度研究" / driving any macOS desktop app (Electron/webview/native) via kimi-cu. Not for kimi.com browser automation (use kimi-webbridge) or direct iFinD API access.
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 a consulting proposal that wins the engagement — outcomes over hours. Use when asked to write a consulting proposal, a project proposal, a pitch for a client engagement, or to respond to an RFP. Produces a proposal — the client's problem in their words, your approach & deliverables, outcomes/value, timeline & phases, investment with options, and why-you — framed around results, not a task list. Ready to export as a designed PDF.
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。
Produce a CV shaped for one target company and role: its applicant tracking system, the country's conventions, the sector's norms, its published values and the job ad, with every choice explained. Use when asked to write my CV for a specific company, tailor my CV to Google, Amazon or any named employer, make my CV fit this company's format, or turn my experience into a CV for this job. Produces the tailored CV, a short format rationale citing public sources with dates, and a list of claims the person must be ready to defend. Never invents experience or insider knowledge.
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。
Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Use when someone has real cohort retention numbers (month 0, 1, 2…) and asks what lifetime value, lifetime periods, or long-run retention they imply, or whether retention is flattening or leaking. Produces a fitted power curve (parameters, R², retention floor), a 24-36 period projection, and a real .xlsx with live formulas where editing ARPU recalculates LTV — via the bundled zero-dependency script.
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。
Plan a co-marketing partnership — two brands reaching each other's audiences for mutual gain. Use when asked to plan a partnership, joint campaign, co-branded content/webinar, integration launch, or partner outreach. Produces the partner fit rationale, a fair value exchange, the joint campaign plan, the partner pitch, and how success is split and measured.
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。
Structure a cohort analysis for retention, LTV, or behavioural patterns. Use when asked to run a cohort analysis, analyse retention by cohort, segment users by behaviour over time, or calculate lifetime value by acquisition period. Produces a complete cohort analysis framework with methodology, cohort definitions, retention curves, and prioritised interventions.
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 a cold sales/B2B outreach email that earns a reply. Use when asked to write a cold email, a sales outreach email, a prospecting email, or a cold email sequence to a business prospect. Produces a short, personalised email — subject, a relevant opener, one clear value-led ask, and a low-friction CTA — plus 2 follow-ups, written to be replied to, not deleted.
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 pixel-level data from an image of a chart or graph and produce a structured data table. Use when asked to extract data from a chart image, transcribe numbers from a graph, digitise a chart, or turn a screenshot of data into a table. Produces a structured table with extracted values, confidence levels, and a reconstructed chart source. Best used with Claude Opus 4.7 or newer for reliable chart data extraction.
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 a realistic recovery plan for burnout — address the causes, not just the symptoms — with changes you can actually make at work and outside it. Use when asked to help with burnout, I'm burnt out, how to recover from burnout, or I'm exhausted and dread work. Produces a read on what's driving the burnout (load, control, reward, fairness, values, community), immediate relief steps, the boundary and workload changes that address the root, a recovery timeline with realistic expectations, and a flag that severe or persistent burnout/depression warrants a professional. Not medical advice.
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。
Decode an employment benefits package into what it's actually worth and where the fine print bites. Use when someone asks 'is this offer good', 'decode my benefits package', 'what does my equity actually mean', or 'what should I ask HR before signing'. Produces a benefit-by-benefit decode with real dollar values, ranked red flags (vesting cliffs, clawbacks, 'discretionary' bonuses, unlimited-PTO economics), and the questions to ask HR before signing.
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 and deliver a raise request that actually works — the evidence, the number, the timing, and the exact words — instead of hoping it gets noticed. Use when asked how do I ask for a raise, I deserve more money, prepare me to ask for a raise, or negotiate a pay increase at my job. Produces a value case built on your actual contributions and market rate, a specific target number with justification, the right timing and person, a script for the conversation, and responses to the likely pushbacks — turning 'I want more' into a business case your manager can say yes to.
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。
Redesign seat-based pricing for the agent era — when one human runs ten agents, per-seat models collapse. Use when agents are eroding seat counts, when asked to migrate to usage- or outcome-based pricing, to price an agent/API tier, or to defend revenue as customers automate their own usage. Produces a pricing migration plan: the new value metric, fences, agent-tier design, cannibalisation math, and a phased migration for existing customers. For general pricing and packaging strategy use pricing-strategy.
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。
Help users define the strategic context for their product by identifying the right competitive alternatives, unique value drivers, and target segments to win a market category.
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。
Help users reach their first moment of core value by optimizing the first-run experience, removing friction, and aligning product design with psychological triggers.
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。
Help users validate willingness-to-pay, select the right value metrics, and continuously optimize pricing and packaging to drive growth.
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。
Help users identify their most high-value customers by defining a hyper-specific ICP that maximizes product-market fit and acquisition efficiency.
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。
Help users navigate complex product decisions by weighing competing priorities and calculating long-term value. This skill covers build-vs-buy logic, technical debt evaluation, and communicating tradeoffs to senior leadership.
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。
Help users define, validate, and operationalize a North Star Metric that bridges the gap between customer value and business success while driving team alignment.
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。
Help users define precise liquidity metrics, set competitive take rates based on value delivery, and manage the delicate balance between supply-side health and platform profitability.
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。
Help users build a strategic professional network and public audience by transitioning from passive learning to active, value-driven contribution.
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。