244 件 ・ 関連度順
概要と使いどころ
Use when explaining or planning around AAMAS peer review, covering OpenReview review release, the double-blind rebuttal on preliminary reviews, area-chair discussion, the mixed game-theory, MARL, and systems reviewer pool, the public posting of reviews and decisions, and how acceptance criteria weigh the interaction contribution.
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Extract and score leads from GitHub repositories by analyzing stars, forks, issues, PRs, comments, and contributions. Produces unified multi-repo CSV with deduplicated user profiles. No paid API credits required.
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Build an evidence-based competitive pricing strategy for ecommerce products. Use when a seller asks how to position a price against competitors, set regular and promotional prices, protect contribution margin, design bundles or price tiers, respond to competitor moves, or coordinate prices across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not use for automated repricing implementation or claims of a mathematically proven optimal price without sufficient data.
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E-commerce growth strategy advisor. Diagnoses current business health using unit economics (CAC, LTV, AOV, contribution margin), identifies the highest-impact growth opportunities across 5 levers (traffic, conversion, AOV, retention, expansion), and builds a prioritized 90-day growth roadmap. Uses the Ansoff Matrix adapted for e-commerce to evaluate market penetration, channel expansion, product expansion, and new market entry. Includes multichannel readiness assessment (Amazon, Walmart, TikTok Shop, Etsy, DTC/Shopify/Shopify) and product line expansion analysis. No API key required. Use when: (1) planning next phase of e-commerce growth, (2) deciding whether to expand to new channels or products, (3) diagnosing why growth has stalled, (4) prioritizing what to fix or build next.
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Evaluate ecommerce price candidates using unit economics, historical observations, elasticity analysis, scenario modeling, and controlled experiments. Use when a seller asks what price to test, how price changes could affect contribution or revenue, how to estimate elasticity, how to optimize a bundle or tier, or how to design a price experiment across Amazon, Shopify, TikTok Shop, Walmart, eBay, or other channels. Do not claim a proven optimal price without sufficient clean data, and do not change live prices without explicit authorization.
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Author or revise a cloud-api-operations recipe (config/source/skills/cloud-api-operations/references/recipes/). Encodes why the long tail of control-plane capabilities is deliberately NOT wrapped as MCP/CLI tools, the API-first admission test for whether a capability deserves a recipe, the negative and positive lists, the fixed recipe structure, the writing rules distilled from writing-for-agents, the user-facing copy red lines, and the product regeneration/verification pipeline. Also routes contributors (and their agents) to the contribution flow and the pre-PR self-test protocol in references/contributing.md. Use when adding a recipe, judging whether a scenario belongs in the recipe set, updating the recipes index, re-checking existing recipes against the admission test, or onboarding someone who wants to submit one.
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。
Reduce defensive writing in drafts and revisions by removing unnecessary caveats, disclaimers, hedges, apology-like framing, negative self-limiting statements, and over-explanations while preserving necessary scope, accuracy, safety, legal, ethical, and methodological limits. Use when drafting or revising academic writing, papers, essays, abstracts, introductions, contribution statements, discussions, conclusions, technical explanations, grant writing, product writing, policy writing, professional reports, or any text the user wants to be clearer, stronger, more direct, more concise, more confident, less verbose, less caveated, less apologetic, or less defensive.
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Help the user turn their Retentioneering ideas, friction reports, bug findings, or feature needs into high-quality upstream contributions: from capturing and validating the idea, through minimal reproductions and issue drafts, to preparing, testing, and submitting a pull request that follows this repository's conventions. Use when the user says they found a bug, wants a feature, wrote a workaround worth upstreaming, or asks how to contribute, open an issue, or make a PR to retentioneering-tools.
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Multi-agent board meeting protocol running a structured 6-phase deliberation with isolated C-suite contributions to prevent groupthink. Use when making major strategic decisions or resolving cross-functional disagreements.
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 for drafting issue closure and decline responses as an open-source package maintainer. Use when helping compose a reply that says "no" to a feature request, closes an issue as won't-fix, redirects a user to a different package, explains why a design choice is intentional, or otherwise declines or closes a community contribution. Also use when the maintainer needs to explain a deprecation, point out a user misunderstanding, or communicate an effort/scope tradeoff to a contributor.
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Trigger for: OT project description, OTA scope, Research OT under 10 U.S.C. 4021, Prototype OT under 10 U.S.C. 4022, follow-on production scope under 10 U.S.C. 4022(f), milestone-based project scope, prototype objective, phase or go/no-go structure, BAA white-paper conversion, SOO conversion, transition planning, or scope reduction before OT cost analysis. Produce a milestone-based .docx plus a separate chat-only handoff to OT Cost Analysis. Use TRL only when the project supports it. Never place cost estimates, funding profiles, contribution calculations, prices, or skill-routing content in the document. Do NOT use for a FAR SOW/PWS, OT cost analysis, IGCE, agreement terms and conditions, or an Agreements Officer's authority, participant-status, successful-completion, or follow-on determination.
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Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.
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smina molecular docking CLI. AutoDock Vina fork with customizable scoring functions, native SDF/MOL2/PDB ligand input, autoboxing, local energy minimization, and per-atom score breakdowns. Pipeline: receptor PDBQT prep -> ligand prep (RDKit/OpenBabel) -> dock via autobox or explicit grid -> rescore/minimize with custom scoring -> rank poses by affinity. Choose smina over Vina when you need custom scoring terms (--custom_scoring), local optimization of an existing pose (--local_only), per-atom contributions (--atom_term_data), or SDF/MOL2 ligands without manual PDBQT conversion. For unknown binding sites use diffdock; for the Python-bindings/Vinardo workflow use autodock-vina-docking.
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Polish, translate, diagnose, and restructure technical manuscript text into IEEE-style academic English using routed failure-mode guidance. Use for IEEE abstracts, introductions, related work, methods, experiments, conclusions, figure captions, rebuttals, Chinese-to-English technical writing, AI-like or template-like academic prose, and engineering prose that needs clearer problem logic, contribution framing, method rationale, evidence discipline, natural author voice, or reviewer-ready wording.
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。
Draft, restructure, and diagnose IEEE-style technical manuscript sections using a routed static/dynamic workflow. Use for IEEE conference, journal, Transactions, Letters, or magazine papers when writing titles, abstracts, introductions, related work, methods, experiments, conclusions, contribution statements, section outlines, or Chinese-to-English engineering paper drafts in AI, computer science, communications, control, signal processing, electrical/electronic engineering, power, robotics, circuits, and applied systems.
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。
Read, decompose, and analyze IEEE-style papers using routed technical-reading workflows. Use when extracting problem setting, object-method-condition logic, contributions, method pipeline, equations, datasets, baselines, metrics, ablations, limitations, replication details, citation role, related-work positioning, implementation notes, or reviewer-style assessment from a paper, PDF, abstract, or manuscript excerpt.
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。
Route Apache Airflow repo tasks across Dag authoring, operations, providers, deployment, and contribution workflows.
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。
Run protected AAS maintainer sweeps, PR merge batches, canonical sync, Core preview checks, and scripted releases. Use for repository maintenance, main alignment, CLI/MCP/Workbench changes, or release work; not ordinary contribution tasks.
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Use when writing, testing, or preparing ANY contribution or pull request to the JSONbored/awesome-claude (HeyClaude) repo — adding a community content entry (agent/MCP server/skill/hook/command/rule/guide/collection/statusline), or making a platform/code change (website, registry package, MCP package, scripts). The repo has two separate review paths: a private, one-shot "HeyClaude submission gate" that decides single-file content PRs, and the shared gittensory-orb bot that posts an advisory readiness/linked-issue comment on platform/code PRs but does not itself merge or close content PRs. Unlike JSONbored/gittensory and JSONbored/metagraphed, a linked issue is OPTIONAL here — do not import their mandatory-linked-issue rule into this repo. Invoke for any "contribute to / open a PR against / submit an entry to / fix a bug in awesome-claude (HeyClaude)" 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。
Use when a contributor wants to move beyond simple bug fixes into architectural improvements, technical debt discovery, design proposals, or module ownership opportunities.
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。
Turn vague or partially formed AI, computer-science, data-driven, and engineering-method research directions into evidence-grounded, falsifiable, paper-ready Research Idea Briefs through heuristic questioning, staged confirmation, literature positioning, novelty attack, and method-figure planning. Use when the user needs to clarify a research idea, formalize inputs/outputs/objectives/constraints, frame background and motivation, select 3-5 anchor papers, identify a defensible gap, derive 2-3 contributions, stress-test novelty, or design an input-to-output method framework and core-paper figure. Also trigger on requests such as 明确科研 idea、启发式梳理 idea、互动追问科研问题、梳理论文创新点、定义研究任务、找对标论文、把研究方向变成论文方案、画方法框架图. Do not use as the primary workflow for running experiments, exhaustive systematic reviews, drafting full manuscript sections, or polishing finished prose.
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。
Break down full ICML reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted issues.
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。
Break down full ICLR reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted issues.
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。
Unit economics analysis, CAC LTV estimation, churn inference, gross margin per unit, customer economics, subscription economics, per-unit profitability, contribution margin, payback period, cohort economics
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。