4,579 件 ・ 関連度順
概要と使いどころ
Build targeted prospect lists by analyzing LinkedIn profiles, extracting job titles, companies, locations, and recent activity. Identifies decision-makers, tracks job changes for warm outreach, and enriches contact data. Use when users need to find prospects, build lead lists, or track decision-maker movements.
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Build and run a labelled eval set for a System One model (Jev, Von, or any typed-decision config), then sweep criteria wordings and thresholds against it. Use when a Jev/Von classification is wrong or unreliable, when choosing between the hosted API and a local open model, when tuning noul thresholds, or before shipping any typed-decision feature. Produces an accuracy-by-wording matrix and a calibrated threshold.
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。
Assesses architecture decisions, ADR compliance, and coupling. Use when evaluating design changes or validating structural decisions before merging.
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 minimal code via a decision ladder with full safety, edge, and negative-case coverage. Use when adding code, choosing a dependency, or auditing a diff.
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。
Guides when to ask clarifying questions versus proceed autonomously. Use to reduce unnecessary clarifying questions when intent is clear.
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。
Maintain an online covariance over named series whose set changes over time (e.g. assets entering and leaving). Use when observations arrive as dicts keyed by name rather than fixed-length vectors. Wraps precise's keyed / FixedUniverse / DynamicUniverse adapters.
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Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
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。
Coach leaders and managers to prepare, run, and review high-bandwidth meetings that advance real work and decisions. Use for meeting retrospectives, employee work reviews, decision meetings, agenda and question design, direct feedback, meeting-quality diagnosis, or repeated low-value discussions. Preserve direct standards without taking over the worker's work or degrading the truth signal.
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。
Implements compliance with Turkey's Personal Data Protection Law (Kisisel Verilerin Korunmasi Kanunu, KVKK, Law No. 6698). Covers data controller obligations, data subject rights, VERBIS registration, cross-border transfer restrictions, Board decisions, and administrative fines. Keywords: KVKK, Turkey, VERBIS, data controller registry, Board decision, cross-border.
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Conducts pre-DPIA threshold screening to determine whether a full Data Protection Impact Assessment is required under GDPR Article 35. Applies the EDPB WP248rev.01 nine-criteria test, national supervisory authority blacklists, and organisational risk appetite to produce a documented screening decision. Keywords: threshold screening, DPIA trigger, pre-DPIA, WP248, Article 35(1), blacklist, screening decision.
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。
Decision framework for choosing between consent and legitimate interest as the lawful basis for processing. Covers power imbalance indicators, conditionality prohibition under Article 7(4), granularity requirements, the three-part LIA test (purpose, necessity, balancing), and practical decision trees for common scenarios.
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Guides assessment and use of the EU-US Data Privacy Framework adequacy decision for transatlantic data transfers. Covers DPF self-certification with the Department of Commerce, DPF principles compliance, Data Protection Review Court, and annual EC review. Keywords: DPF, EU-US, adequacy, Privacy Shield, transatlantic transfers.
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。
Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.
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。
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
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。
CUMCM 国赛、MCM/ICM 美赛与电工杯数学建模竞赛的端到端协作工作流。Use when a user explicitly works on one of these modeling contests or asks to run/review a modeling-competition paper from problem selection through modeling, solving, robustness, writing, compliance, and final submission review. Provides 10 stages, persistent decision state, competition-specific rules/templates, deterministic scoring helpers, numbered decisions, and Codex/Claude Code handoff. Do not trigger for generic model selection, ordinary data analysis, or non-competition paper review.
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。
Converges an underspecified request into a typed IntentSpec before planning or acting. Use when the user asks to implement, add, change, refactor, fix, migrate, configure, handle, triage, sort out, look into or decide something and the request leaves decisions open (which objects, which approach, what happens on failure, which trade-off), contradicts itself, admits two readings, or rests on an approach its sources may rule out — in a codebase, a ticket queue, a research brief or a runbook — or when the user says "clarify the intent", "what do you need from me", or invokes intent-router. Looks up what its sources hold (code, history, decision records, a ticket log, an order record, the policy in force) before asking; asks only preference or irreversible questions, one at a time, with a recommended default; halts instead of guessing; checks the delivered work against the spec. Silent on a fully stated task its sources do not contradict. Not for explaining existing state ("what does X do", "why is Y slow").
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。
Builds a feature specification from scratch through a relentless, evidence-based interview that walks the design tree decision-by-decision, resolving dependencies as it goes. Use when the user wants to plan, design, scope, specify, or flesh out a new feature, capability, or system behavior before implementation. Produces a feature specification focused on system behaviors, not implementation detail. Does not plan a restructure of code that already exists — use plan-a-change. Does not refine or stress-test an existing plan — use iterative-plan-review. Does not document already-built features — use project-documentation. Does not design the contract for an interface — use design-an-api. Does not research open-ended options before there is a feature to specify — use research. Does not map the bounded contexts of existing code — use ddd-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。
Builds a new Claude Code agent (subagent) from scratch through a relentless, evidence-based interview that walks the agent's design tree decision-by-decision — entity fit, domain focus and vocabulary, role identity, anti-patterns, description, model tier, tools, and self-containment — then reviews the finished agent against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new agent or subagent. Does not build a skill or slash command — use skill-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.
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。
Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.
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。
Creates and updates coding standards, conventions, rules, and guidelines for the current project. Use when creating new standards from scratch, converting existing documents into coding standards, or updating existing standards. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not write feature or system documentation — use project-documentation for that. Does not research open-ended options — use research. Does not produce runbooks for operational scenarios — use runbook for that.
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 or update a runbook for an operational scenario — an incident an alert fires for, a recurring scheduled task, or a known failure mode on a live service — using a consistent template. Use when writing, drafting, authoring, or updating a runbook for an alert, incident, on-call procedure, scheduled maintenance, or operational SOP. Applies a YAGNI preflight requiring the scenario to be real before producing the runbook. Does not produce feature or system documentation — use project-documentation. Does not record architectural decisions — use architectural-decision-record. Does not create coding standards — use coding-standard.
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。
Creates or updates project documentation for a feature, system, or component and publishes it to a user-specified Confluence location. Use when the user wants feature or system documentation written to Confluence, posted to a Confluence space or page, or synced to a Confluence location. Requires a configured Atlassian MCP server. Does not document to local files only — use project-documentation for that. Does not publish an arbitrary existing markdown file — use markdown-to-confluence for that. Does not plan or specify a new feature to Confluence — use plan-a-feature-to-confluence for that. Does not create architectural decision records — use architectural-decision-record. Does not create coding standards — use coding-standard. Does not produce runbooks — use runbook.
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。
Creates and maintains project documentation for features, systems, and components. Use when documenting how a feature, system, or component works — including writing, updating, or organizing docs. Does not scan or detect the project's technology stack — use project-discovery for repository analysis and config detection. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not create or update coding standards — use coding-standard instead. Does not generate PR descriptions — use update-pr-description for that. Does not produce runbooks for operational scenarios — use runbook for that. Does not rewrite existing prose for readability — use edit-for-readability for that. Does not produce an ephemeral, understand-now overview of code or a PR — use code-overview for that.
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。