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概要と使いどころ
Identifies upsell and cross-sell opportunities within existing customer accounts. Analyzes product usage, feature gaps, team growth, industry benchmarks, and competitive pressure to surface revenue expansion plays scored by potential, effort, and likelihood. Generates an expansion-playbook.md with account-by-account opportunities, recommended pitch, timing, and approach.
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Assesses how ready a business is for AI adoption across six dimensions. Evaluates data maturity, tech stack, team skills, process documentation, budget, and culture. Generates a comprehensive ai-readiness-report.md with scores, gap analysis, and recommended starting points. Aligned with OneWave AI's audit methodology.
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。
Generates a comprehensive client health overview across all accounts. Reads CRM data, support tickets, usage metrics, billing, and engagement logs. Calculates health scores, trend direction, and RAG status per client. Outputs a sorted risk report with recommended actions.
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 to test whether an AI agent, prompt, or skill actually works before or after it ships — build a small eval set, score runs, and catch regressions when the prompt or model changes. Trigger when the user asks whether a prompt is good, wants to compare models or versions, sees inconsistent agent output, or is about to put an agent in front of customers.
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。
Scores backlog items with RICE/WSJF/Kano and files GitHub issues for top candidates. Use when triaging a roadmap or prioritizing features for a sprint.
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。
Scores agent actions by expected gain, cost, uncertainty, and redundancy. Use when deciding whether to dispatch an agent or invoke a tool.
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。
Scores feature worthiness and enforces branch-size limits against overengineering. Use when evaluating whether a feature belongs in the current scope or branch.
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。
Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions. Use when reversibility score is low and adversarial review is warranted.
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 hunches into accepted results: worthiness score, evidence bar, research-to-rules. Use when vetting ideas. Not for QA; use night-market-validation-and-qa.
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 data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.
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 DPM-Solver and DPM-Solver++ single-file samplers for diffusion ODE sampling, PyTorch/JAX integration, ScoreSDE/DDPM examples, and Stable Diffusion acceleration.
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 ClearerVoice-Studio tasks for ClearVoice inference, SpeechScore metrics, and speech-model training or data-preparation 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。
Honey benchmark scoreboard vs baseline and rival 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。
Show Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline, or for the headline numbers.
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。
Show Honey's benchmark scoreboard: committed quality and token results per task tier vs Caveman, Ponytail, and no-skill. One-shot.
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。
把已验证的传播机制转成适合目标账号的内容选题，并完成生成、迁移、评分和筛选，让选题自然连接人设、产品或门店。用户要从低粉爆款、垂直样本、邻近受众或跨赛道内容中寻找新选题时使用。 English: Turn validated content mechanisms into topics that fit the target account, then generate, adapt, score, and filter them while connecting naturally to the persona, product, or store. Use to find ideas from low-follower breakouts, niche examples, adjacent audiences, or other categories.
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。
Triage Monte Carlo alerts interactively or build an automated workflow. Fetch, score, and troubleshoot alerts using MCP tools now, or design a reusable workflow that runs on a schedule.
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。
The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing.
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。
Scores shortlisted accounting packages against 57 evidence-backed fields, emitted as CSV, SQL, JSON Schema or Notion on request. Use for choosing accounting software.
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 a goal through acc's scored-memory loop via acc_act(runtime="solve"); deliberate any returned brain_frame and submit via continue.
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。
360 feedback register: reviewer, subject, review cycle, visibility, due date and score, as CSV, SQL, JSON Schema or Notion on request. Use for 360 reviews or peer feedback cycles.
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, build or improve procedural risograph animations and videos in this repo, including narrative films, abstract shorts, motion effects and visual inspection. Delivers self-contained Canvas 2D HTML and a verified MP4. For standalone still art use riso-still; for sound use riso-score. Not for editing tools/.
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。
Audit and recommend improvements for a repository's agent-readable context, including AGENTS.md, CLAUDE.md, WARP.md, CONTRIBUTING.md, .agents/skills, and specs/ PRODUCT.md and TECH.md contracts. Use when asked to review, score, assess, or standardize repo instructions, agent onboarding, spec workflows, or cross-repo agent-context conventions. Use agentsmd-scaffold instead when the user wants to generate or apply root/scoped AGENTS.md files.
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。
Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
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。