「pact」の検索結果

Conducts retention impact assessments for new processing activities to determine appropriate data retention periods. Covers regulatory requirements scanning, proportionality review, purpose-based retention determination, and retention period documentation aligned with GDPR Article 5(1)(e) and Article 25 data protection by design. Activate for retention assessment, new processing retention, retention period determination queries.

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Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.

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This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.

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。

Compact this Claude Code session.

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。

Guidance for compacting context manually at phase boundaries instead of waiting for auto-compaction mid-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 the user wants full metadata for a paper — from a DOI, PMID, arXiv id, OpenAlex id, or title. Returns title, author list, first author, corresponding author(s), publication date, journal name + ISO-4 abbreviation, impact factor, volume/issue/pages, DOI/PMID, citation count, and abstract in one record. Triggers on "paper metadata", "who is the corresponding author", "first author of", "what journal / impact factor for this DOI/PMID", "cite this paper", "文献元数据", "通讯作者", "第一作者", "影响因子". PROACTIVELY USE when the user pastes a DOI/PMID/arXiv id or paper title and asks about its authors, venue, or impact.

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。

Conduct Fundamental Rights Impact Assessments (FRIA) for high-risk AI systems as required by EU AI Act Article 27. Use when (1) deployers of high-risk AI systems (Annex III) need to assess fundamental rights impacts before first use, (2) public bodies or private entities providing public services are deploying AI systems, (3) credit scoring or insurance risk assessment AI systems are being deployed (Annex III 5b, 5c), (4) evaluating impact on natural persons' fundamental rights (dignity, liberty, privacy, data protection, non-discrimination, children's rights, etc.), or (5) creating EU AI Act Article 27 compliance documentation with market surveillance notification.

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。

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

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。

Strategic compaction skill: suggests context compression at logical breakpoints. Use to suggest compaction at logical intervals during development sessions.

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。

Calculates dilutive impact of equity issuance on existing shareholders with EPS, ownership, and NAV per share analysis. Use when modeling dilution, communicating shareholder impact, or comparing capital raise alternatives.

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 should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 10 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSearch/WebFetch tools and produces impact-ranked analysis reports. All analysis thinking and output are conducted in English.

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。

Fix scattered/torn regions in hex-grid simulated annealing layouts that use "competitive flood-fill" (one tile per region per outer iteration). Use when: (1) regions of >20 tiles end up dispersed instead of forming compact blobs, (2) maxDist/sqrt(N) dispersion ratio is 10×+ worse than the ideal compact blob, (3) tiles assigned to one region show up in multiple disconnected fragments across the layout, (4) the visualization has a "trypophobia ring" pattern at the perimeter where some regions spill into a perimeter spiral, (5) an overflow spiral fallback exists in the layout code and frequently kicks in for large regions, (6) bigger regions look worse than smaller ones (small regions stay compact, big ones shred). Root cause: interleaved competitive flood-fill (round-robin one tile at a time across all regions) lets large regions get boxed in by neighbors before they finish growing — the unfilled remainder is dumped into the overflow spiral at the perimeter, far from the seed. Fix: replace with sequential region-by-region BFS — process regions in size-descending order, fully claim each region's N contiguous tiles before moving to the next.

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Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.

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。

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

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。

Runtime router for context decisions during a task — decide read-more / stop / ask / assume / delegate, pick a minimal read-set, and time compaction. Front-page mental model is Write / Select / Compress / Isolate; a lazy pattern index routes to 25 context-engineering patterns, one reference per decision. Use when context is near budget, a long session is degrading, you must choose what to read for a task, or you are deciding /compact vs /clear vs a sub-agent. NOT for structural .claude/ overhead audits (see mk:context-audit); NOT for owning persistence (see mk:memory); NOT for USD cost (see mk:budget).

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。

Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.

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。

OpenLineage, DataHub, Marquez for data lineage tracking and impact analysis. Activate on: data lineage, OpenLineage, DataHub, Marquez, impact analysis, data catalog, column lineage, data discovery. NOT for: data quality validation (use data-quality-guardian), dbt documentation (use dbt-analytics-engineer).

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The LAW a council SEAT is bound by — deliberation protocol, pact obligations, governance, and decision-ledger recording — the discipline half of a seat. Trigger when: - Holding a seat on a bound council (architect, lead-maintainer, process-auditor, or a summoned guest). - Depositing an independent assessment, routing a decision, or recording assent or a finding. - Auditing whether the council honored the pact, the delegation table, or the barring lifecycle. - Prompt contains: /council, council seat, deliberation protocol, pact, delegation table, decision ledger, barring, merge-consent.

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Prepares a DRAFT data incident impact brief from the incident notes and data inventory the user provides: the timeline as stated, systems involved matched to the inventory, data categories held and evidenced as involved, people and record counts potentially affected with the source and method behind every figure, third parties, notification questions for legal and the data protection officer, and every UNKNOWN with the evidence that would settle it. No legal determination: it never states whether the incident is a reportable breach, whether notice is due, to whom or by when. Use when the user asks to "prepare the impact brief for this incident", "what data was involved", "how many people are affected", "map the incident to our data inventory" or "pull together what legal needs on the incident". Do not use for the blameless postmortem or root-cause timeline, use incident-postmortem-drafter instead. Drafts for human review; never approves, authorises or signs off.

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Drafts a blameless incident postmortem (summary, impact, timeline, detection and response, contributing factors as candidates for review, action items) from an incident channel export, ticket notes and any alert or change records, with UNKNOWN for every fact the inputs do not contain. Never assigns blame, names a root cause or re-rates severity. Use when the user asks to "write the postmortem for this incident", "build the incident timeline from the chat export", "draft the post-incident review" or "turn this ticket and channel log into an incident report". Do not use for a knowledge article from the resolved ticket, use knowledge-article-drafter instead; for project retrospectives, use lessons-learned-synthesis; for data-breach impact and notification questions, use data-incident-impact-brief. Drafts for human review; never approves, authorises or signs off.

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。

Reads a project or processing description and returns a DRAFT data protection impact assessment pack: processing description, necessity and proportionality questions, a risk table with blank rating cells, mitigations to consider and open questions. Never rates risk, determines the lawful basis or judges adequacy; the DPO assesses and signs. Works from descriptions, never personal data. Use when the user asks to "start a DPIA for the new monitoring tool", "draft a data protection impact assessment", "scaffold a DPIA from this processing description" or "what would the DPO need for a DPIA on this". Do not use for redacting or de-identifying a document, use document-deidentification-pass instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Drafts for human review; never approves, authorises or signs off.

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Print a ready-made /compact line in a canonical handoff format, so the operator pastes it as the next message and shrinks working memory without losing the thread. Use at the end of a long session. Triggers: "/cc", "compact", "finish via compact".

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diff-time で「影響範囲を repo 全体で調査した証拠」「失敗系を検証した証拠」「外部依存・レート制限・キャッシュ整合を確認した証拠」の欠落を検出する evidence-sufficiency 観点。defect そのものは既存 skill へ委譲し、本 skill は「そのリスク種別を調査した証拠が差分・PR 本文・テストに残っているか」の meta 評価のみを行う。証拠が同一 diff に同梱（テスト・canary・grep 記録）されていれば充足とみなす

Show ponytail's measured impact as a compact scoreboard: less code, less cost, more speed, from the benchmark medians. One-shot display, not a persistent mode, and not a per-repo number. Trigger: /ponytail-gain, "ponytail gain", "what does ponytail save", "show ponytail impact", "ponytail scoreboard".

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。