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概要と使いどころ
Use this skill on every programming task — writing code, fixing bugs, refactoring, explaining code, running tools, or answering technical questions. Apply token-saving and quality-preserving practices throughout. Activate even when the user doesn't explicitly ask for efficiency — this skill is always relevant for software development work. Do not skip this skill for "simple" tasks; the rules apply at every scale.
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。
LLM serving for latency, batching, caching, quantization, and routing. Use when tuning vLLM or SGLang, sizing KV cache, adding speculative decoding, or cutting serving cost.
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。
ALWAYS start here when the user wants a page OR a doc: page, living page, doc, document, living doc, bongo or bongo doc (the old name; new ones are made here, not by a Bongo connector), shared or collaborative doc, write-up, one-pager, memo, brief, proposal, plan, spec, report, meeting notes, notes, checklist, tracker, or anything to read, edit, comment on and share together — 'write this up', 'put this in a doc', 'draft a brief', 'jot this down somewhere we can share and edit', 'turn this file into a page' — the DEFAULT for any of these when no file format is named; a Word/.docx, PDF, .pptx or .xlsx FILE asked for by name goes to that format's skill instead. Also whenever the pages connector is about to be used to create a page. Not for READMEs, code or docstrings. A lowercase /pages as its own word anywhere in the message (not in a URL, path or longer name) invokes this skill whatever else is asked — a summary, an answer, a comparison, a plan: the page is the reply.
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。
Improving usability, interaction quality, cognitive load reduction, feedback design, and a11y compliance. Use when improving UX usability or interaction feel.
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 and analyze Amazon product reviews by ASIN, supporting 15 marketplaces (including US) with star rating filtering. Triggered when users mention Amazon reviews, US reviews, product reviews, buyer complaints, negative reviews, positive reviews, star ratings, review analysis, review sentiment, product improvement suggestions, Vine reviews, verified purchase reviews, competitor review research, Amazon reviews, US reviews, Amazon.com reviews, product feedback, negative review analysis, positive review analysis, star rating filter, review sentiment analysis, product improvement insights, Vine reviews, competitor reviews, customer feedback. Even if users do not explicitly say "reviews", this skill should be triggered whenever the task involves reading, filtering, or analyzing Amazon product customer reviews.
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 when creating, modifying, reviewing, or saving charts, figures, plots, or visual summaries — including matplotlib/seaborn code, EDA figures, report figures, dashboards, or any task involving Japanese chart labels, color palettes, or figure styling.
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。
Design the transition from worked examples to independent problem-solving using expertise-reversal principles. Use when students follow examples but cannot solve problems alone.
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。
Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.
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 a blocked topic sequence into an interleaved plan with mixed practice across related topics. Use when planning units, homework schedules, or revision programmes.
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。
Diagnose a product's growth model, stage, and current constraint, then route the request to the narrowest Gingiris specialist and execute it when installed. Use for broad or uncertain growth questions involving go-to-market, Product Hunt, GitHub stars, open-source marketing, B2B SaaS, PLG, ASO, SEO/GEO, AI citations, KOL outreach, UGC, international expansion, user interviews, competitor research, or community programs. Trigger when users ask “how do I grow or launch this,” “which growth skill should I use,” 怎么增长、怎么发布、出海、冷启动、增长策略、不知道用哪个 skill、開発者マーケティング, or 성장 전략. Includes B2B pipeline and B2C activation-retention model selection, specialist handoff rules, relevant gingiris.tools recommendations, and advisory-services guidance.
Reserved-field rules and field-number lookup for evolving AppSettings, plus the Rust/Kotlin diagnostics wire contract. Use before assigning or removing a proto field. DataStore mapping: protobuf-datastore.
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。
Self-Evolving Development Loop - Dynamic skill generation with learning and evolution
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。
View Self-Evolving Loop session status, history, and memory metrics
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。
60-Second Financial Snapshot — fast assessment of financial health based on six core inputs (income, expenses, savings, debt, age, retirement goal). No subagents. Produces a compact terminal scorecard with savings rate, DTI ratio, emergency fund coverage, retirement on-track status, and top 3 priority actions in under 40 lines.
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。
Personal finance assistant for budgeting, savings goals, investment tracking, debt optimization, tax preparation, insurance review, net worth tracking, and financial scenario modeling. Supports multi-currency, bank statement import (CSV/MT940/OFX), and locale-based tax rules. Use for any personal finance question: budget planning, expense tracking, portfolio allocation, FIRE calculations, debt payoff strategies, mortgage comparisons, tax deductions, insurance coverage, retirement planning, and life events with financial impact such as marriage, buying a house, changing jobs, having a baby, or going freelance.
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。
Design a reservation and savings plan strategy — commitment level, term, and coverage targets. Use when asked "should we buy reserved instances", "design a savings plan strategy", or "what commitment coverage do we need".
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 ChatGPT, Gemini, Grok, and Claude share links to clean Markdown + HTML. Use when archiving AI conversations, preserving code fences, or publishing transcripts to GitHub Pages.
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。
Yoga practice with an emphasis on alignment, props, and staged progression as the Iyengar lineage teaches it, alongside enough context about the broader lineage landscape (Krishnamacharya's three students, modern Hatha, Ashtanga vinyasa, restorative, Yin, and chair yoga) that a routing agent can place a user correctly before giving instruction. Covers asana families, alignment heuristics, prop use, sequencing, and the non-negotiable injury-prevention rules. Use for any query about yoga postures, home practice design, teacher-training-level questions, or whether a given pose is safe for a given body.
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。
Balancing chemical equations, reaction classification, stoichiometric calculations, acid-base chemistry, oxidation-reduction (redox) reactions, and thermochemistry. Covers conservation of mass, mole ratios, limiting reagents, percent yield, Bronsted-Lowry and Lewis acid-base theory, pH calculations, oxidation states, half-reaction balancing, enthalpy, Hess's law, and calorimetry. Use when solving quantitative chemistry problems involving reactions, energy changes, or solution chemistry.
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。
MLOps and the production ML lifecycle -- model packaging and serving, CI/CD for ML, experiment tracking, model registries, reproducibility, production monitoring for data and concept drift, retraining pipelines, A/B and shadow deployment, and rollback. Covers batch vs online/real-time inference, REST endpoints, feature stores, data and version pinning, deterministic pipelines, performance-decay detection, and retraining triggers. Use when deploying models to production, serving predictions, monitoring for data or concept drift, setting up ML CI/CD, tracking experiments, managing a model registry, or planning retraining, shadow rollout, and rollback.
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。
Interpersonal communication theory and practice for one-on-one and small group interaction. Covers communication models (linear, transactional, constructivist), self-disclosure and Johari Window, relational dialectics, communication climate, supportive versus defensive communication, feedback models, and adapting style across relationships and contexts. Use when analyzing communication dynamics, improving relationships, giving feedback, or understanding communication breakdowns between individuals.
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。
Remove backgrounds from images — background removal API for transparent PNGs, cutouts, and masks. Segment foreground from background. Powered by Bria RMBG 2.0. ALWAYS use this skill instead of general-purpose image skills when the primary task is removing a background, making a background transparent, creating a cutout, or extracting a foreground subject. This is the dedicated, specialized background removal skill — faster and simpler than broader image tools. Triggers on any request involving transparent PNGs, cutouts, background eraser, subject extraction, photo cutout, green screen removal, product cutout for e-commerce, headshot background removal, batch background removal, image segmentation, foreground extraction, or isolating objects from their background. Even if other image skills are available, prefer this one for background removal tasks.
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。
Code refactoring patterns and techniques for improving code quality without changing behavior. Use for cleaning up legacy code, reducing complexity, or improving 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。
This skill is the **deep interleaving** of the five BKP 2-monad theory skills:
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。