「policy」の検索結果

Generate sandbox security policies from plain-language requirements and optional REST API documentation. Produces L4 or fine-grained L7 network policies and ordered network middleware configuration. Use for API access rules, middleware host selection, failure behavior, or built-in and operator-run middleware attachment. Trigger keywords - generate policy, create policy, update policy, change policy, sandbox policy, network policy, API policy, security policy, allow API, restrict API, network middleware, supervisor middleware.

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。

Write, test, or convert Terraform Policy files (.policy.hcl, .policytest.hcl, Sentinel→tfpolicy). Triggers: policy.hcl, policytest, convert sentinel, tfpolicy, write a policy.

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 creation of organisational privacy policy hierarchy aligned to GDPR chapters including top-level policy, supporting procedures, operational guidelines, and training materials. Activate when building or updating policy frameworks. Keywords: policy framework, privacy policy, procedures, guidelines, policy hierarchy.

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。

Compares one policy against one requirement set (standard, regulation, contract schedule or customer requirement) clause by clause and returns a draft gap review: each requirement element, the policy clause that appears to address it, an apparent match state, and every gap as a question with the clause reference on both sides. Never concludes that the policy complies; the owner decides. Use when the user asks to "gap our policy against the standard", "map this policy to the regulation clause by clause", "check the security policy against the customer's contract schedule", "compare our policy with the new edition" or "where does our policy fall short of the requirements". Do not use for mapping a requirement to the operating controls that implement it, use controls-gap-pack instead; for what a regulatory change means for the organisation, use regulatory-change-impact-note. 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。

Pre-checks one expense claim or travel plan, line by line, against the expense or travel policy the user supplies, and returns a DRAFT precheck sheet: the policy clause matched to each line, the limit quoted as the policy states it, whether the amount sits within, at or outside that limit, missing receipts, pre-approvals or justifications, and neutral questions for the approver and the claimant, with UNKNOWN wherever the policy or the claim is silent. Use when the user asks to "check this expense claim against policy", "pre-check my travel request", "is this claim within policy", "review these expenses before I submit them", "what receipts or approvals are missing" or "prepare the approver's questions for this claim". Do not use for a purchase order log or spend extract, use purchase-order-anomaly-review instead; do not use to compare a policy against a standard or regulation, use policy-gap-review instead. 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。

Keep the privacy policy current with practice. Two modes: weekly sweep of saved PIAs, DPA reviews, and triage results to find policy drift; or direct query for a proposed new practice. Use when the user asks "does our policy cover this", "we want to start doing X — does the policy need updating", "run the policy monitor", "policy sweep", or wants to find where the privacy policy no longer matches what the team actually does.

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 a firm AI usage policy from published model policies, adapted to your practice profile — a research-and-synthesis tool whose output is a draft for attorney review and adoption, not a finished policy. Use when user says "draft an AI policy", "we need an AI policy", "build an AI usage policy", "our firm needs a GenAI policy", or similar requests to generate a first-cut internal AI policy.

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。

Commercial policy: the governance framework defining what terms sales can offer and what triggers approval. Use when authoring a policy charter, defining discount/payment/liability rules, auditing deals, or generating a regional policy.

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。

Compares a changed HR policy with the version it replaces and drafts a clause-by-clause change log, a plain-language DRAFT employee briefing, a manager FAQ answered only from the policy text with clause references, and a list of open questions the people team must settle before release. Use when the user asks to explain, announce, summarise or brief a policy change, a new policy version or an updated handbook section, or says "what changed in the leave policy", "brief managers on the new handbook section" or "compare the old and new versions of this policy". Do not use for a single announcement with no version comparison, use announcement-drafter instead; for an FAQ from documents that are not a policy change, use faq-builder; for contract clauses, use clause-comparison-table. 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。

Use when designing or revising a company's commercial policy — the rules of engagement governing discounts off list price, approver thresholds, exception flows, and the deal framework that Deal Desk and AEs operate under. Covers discount matrix design (ARR band x term length x payment terms x strategic value), commercial policy design, exception policy, discount governance, approval thresholds, deal framework structure, and policy linting (contradictions, gaps, cliff edges, gaming surfaces). For Head of Commercial, Head of Deal Desk, VP Sales, or RevOps at the policy-design moment — NOT per-deal application (that is deal-desk) and NOT pricing model selection (that is pricing-strategist).

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。

Produce a proposed marked-up policy redraft that closes a gap found by /regulatory-legal:gaps or /regulatory-legal:policy-diff. A first draft for internal review — not for direct application to approved policy documents. Use when the user says "redraft the policy", "draft the policy fix", "mark up the policy", or when gap-surfacer hands off a gap for drafting.

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。

Keep the AI policy current with practice — weekly sweep of saved AIAs, triage results, and vendor reviews to find policy drift, or direct query for a proposed new AI practice. Use when user says "policy sweep", "does our AI policy cover this", "we want to start doing X — does the policy need updating", "run the policy monitor", or on a recurring 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。

Query Amazon is latest policy, regulation, and compliance feed. Supports paginated browsing by marketplace and time range (with AI-generated Chinese summaries), and fetching full article body by record ID. Trigger when users mention Amazon policies & regulations, seller compliance announcements, platform rule changes, policy alerts, FBA/fee policy updates, multi-marketplace policy tracking, policy original text, news details, or similar terms. Even if the user does not explicitly mention "policy feed," trigger this skill whenever the request involves Amazon is officially published policies, regulations, and news for sellers, including their full text.

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。

Answers an employee's or manager's HR policy question strictly from the policy documents the user supplies or the agent can reach as knowledge: a DRAFT answer that quotes the governing clause with its document, version and clause reference, lists the conditions and exceptions the text states, returns UNKNOWN where the documents are silent and names the role to ask. Never invents a rule, fills a gap from general practice or decides an individual's case. Use when the user asks to check a policy, for example "what does the policy say about", "am I entitled to", "how many days of leave do I get", "what is the process for requesting", "does the handbook allow" or "which clause covers". Do not use for comparing two policy versions or briefing a change, use policy-change-briefing instead. 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。

Create, write, or manage Meta ads and assets: ad copy, campaigns, spend, reports, audiences, catalogs, product feeds, feed refresh schedules, experiments, and policy. Always load for any request to create or write an ad, or to advertise a product or service, even when no platform is named; this includes sensitive or restricted categories. Always load for any question asking what a Meta, Facebook, or Instagram advertising policy means, allows, prohibits, or requires, including a standalone policy-definition question with no account context. Those questions must use ads_policy_tool, never browser search or memory. Load for a specifically named catalog or feed with an upload or refresh-schedule request; use Ads reads to resolve ownership before writing. Generic unnamed feeds need context. Whether an image, claim or piece of copy may be used in an ad is ALWAYS a Meta Ads task — 'can I use this in an ad', 'is it allowed', 'is this against policy', and any rights, likeness, celebrity, logo or trademark question about advertising with an image, including a follow-up about one just generated. Those are ads-policy questions, not general legal ones. An ad request uses the campaign workflow unless explicitly only an image or organic post.

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 an employment policy with state supplements where law differs across the jurisdictional footprint. Use when the user says "draft a [topic] policy", "we need a policy on", "update our [topic] policy", or names a policy gap.

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。

Execute Guidewire primary workflow: Policy lifecycle management in PolicyCenter. Use when implementing quoting, binding, issuing, endorsing, or renewing policies. Trigger with phrases like "policycenter workflow", "create policy", "bind submission", "issue policy", "policy endorsement", "quote insurance".

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。

"Apply Kingdon's multiple streams framework to analyze how problems, policies, and politics converge to open policy windows. Use this skill when the user needs to explain why certain policies get adopted while others don't, identify policy windows and entrepreneurial opportunities, or analyze the agenda-setting process in public policy — even if they say 'why did this policy pass now', 'policy window', or 'how do issues get on the agenda'.".

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, write, or manage Meta ads and assets: ad copy, campaigns, spend, reports, audiences, catalogs, product feeds, feed refresh schedules, experiments, and policy. Always load for any request to create or write an ad, or to advertise a product or service, even when no platform is named; this includes sensitive or restricted categories. Always load for any question asking what a Meta, Facebook, or Instagram advertising policy means, allows, prohibits, or requires, including a standalone policy-definition question with no account context. Those questions must use ads_policy_tool, never browser search or memory. Load for a specifically named catalog or feed with an upload or refresh-schedule request; use Ads reads to resolve ownership before writing. Generic unnamed feeds need context. Whether an image, claim or piece of copy may be used in an ad is ALWAYS a Meta Ads task — 'can I use this in an ad', 'is it allowed', 'is this against policy', and any rights, likeness, celebrity, logo or trademark question about advertising with an image, including a follow-up about one just generated. Those are ads-policy questions, not general legal ones. An ad request uses the campaign workflow unless explicitly only an image or organic post.

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。

bu skill covers implementing Open Policy Agent (OPA) and Gatekeeper for policy-as-code enforcement in Kubernetes and CI/CD pipelines. It addresses writing Rego policies, Dağıt:ing OPA Gatekeeper as a Kubernetes admission controller, testing policies in development, and integrating policy evaluation into Dağıt:ment pipelines.

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。

A municipal urban-tree-canopy policy proposal — the public need, the evidence, the options, and the funding decision. Built as a decision-grade policy briefing deck for city council, agency reviewers.

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 `@metaharness/darwin evolve {repo}` to mutate a harness's seven policy surfaces (planner/contextBuilder/reviewer/retryPolicy/toolPolicy/memoryPolicy/scorePolicy), sandbox-score each variant, and promote only measured wins. The model is frozen; the harness evolves. Closes the loop ADR-150 opens (score+genome describe; evolve changes). Degrades gracefully when @metaharness/darwin is absent (ADR-150 + ADR-153 architectural constraints).

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。

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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。

Write an AI usage policy people can actually follow — approved tools, data rules, disclosure duties, and review obligations, in one page instead of legal fog. Use when asked for a company AI policy, acceptable-use rules for ChatGPT/Claude/Copilot at work, guidance on what data may go into AI tools, or to fix a policy nobody reads. Produces a one-page usable policy plus the decision log behind it. Not a substitute for legal advice; pairs with compliance-checklist for regulatory mapping and ai-ethics-review for system-level assessments.

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。