1.1천건 · 관련도순
Review whether every page provides an accessible privacy policy link, especially in the footer. Check that the policy explains data collection, use, sharing, retention periods, contact details, and user rights, and identify missing information.
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Draft a privacy policy based on the data your product collects and the regions it serves. Receive a summary, full policy draft, and customization notes that identify clauses requiring review by a privacy lawyer.
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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.
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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.
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Track gaps between regulatory requirements and company policies, including owners, deadlines, and remediation status. Surface overdue and aging items, record resolutions or accepted risks, and prepare Slack notifications that require explicit confirmation before sending.
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Compare a regulatory change with an indexed company policy library. Identify affected policies, missing or conflicting provisions, responsible owners, and specific changes needed, with source labels and flags for uncertainty about whether the rule is in force.
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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".
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"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'.".
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Use this skill when an engineering team needs a secure development policy, or has one that does not match how code really ships, and asks "write our secure SDLC policy", "secure development policy from our CI", "document our code review and scanning for the audit", "what does our pipeline already enforce", "change management for engineers" or "secure coding policy for SOC 2 or CRA". Reads the repositories and pipeline definitions, inventories what is actually enforced (reviews, checks, scanners, deploy gates, dependency and secret controls), and writes a policy whose every "must" points to the place it is enforced, with gaps listed as decisions.
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Use this skill when a team suspects its policies describe a company that does not exist, before an audit or customer review, or asks "do our policies match what we actually do", "check the policy against reality", "paper versus practice", "will the auditor find gaps in our policies", "our policy says quarterly but we never do it" or "align policies with how we work". Turns each policy into testable statements, samples the systems and records that show the practice, classifies every gap and proposes whether to fix the policy or the practice, with owners and priorities.
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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.
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Audit or draft an editorial policy for a blog, news site, or sensitive-topic publication. Cover author qualifications, research, fact-checking, corrections, and funding disclosures, and check that readers can find the policy from relevant pages.
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Guides Kubernetes security configuration with network restrictions, Pod execution rules, and role-based access control. Use its configuration examples to separate workloads, limit permissions, and enforce deployment requirements across a cluster.
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Audit Google Cloud access permissions to find overly broad roles, unnecessary service account keys, public access, and impersonation risks. Use gcloud CLI, Policy Analyzer, and IAM Recommender to document findings and plan more limited permissions.
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Triage employee questions in an HR inbox against policies documented in Notion. Prepare Gmail drafts with policy references for routine questions, and route ambiguous, legal, personal, or compensation matters to HR through Slack.
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Create slides for safety reviews, risk discussions, incident retrospectives, or security exercises. Warning stripes, three-level cards, status alerts, policy code blocks, checklists, and stacked bar charts provide a visual structure for the presentation.
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Apply the System.Text.Json APIs introduced in .NET 11 to produce PascalCase JSON names and retrieve typed serialization metadata, the information used to convert objects. Create runnable examples and check metadata availability without exception-based probing.
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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".
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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.
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Viking Search tuning for specified policy directions. Use this when the user provides specific queries, a type of query, or a business policy direction, and asks to boost, suppress, or fix a class of search results through request-parameter passthrough. You must only perform read-only baseline evaluation and request-level candidate testing; do not modify search scenes, app config, dictionaries, recommend scenes, or primary recall parameters.
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A zero-hallucination privacy-policy generator that takes anyone — non-lawyer founder to lawyer — from a guided intake to a publishable, jurisdiction-aware privacy policy. Jurisdiction-first: it detects which laws apply from where your users are, then drafts only the required clauses — GDPR/EU + UK, US (CCPA/CPRA, ~20 state laws, COPPA, sector overlays), and global/MENA (LGPD, Quebec Law 25, India DPDP, China PIPL, UAE/DIFC, Saudi PDPL), plus app-store, cookies, and AI/EU AI Act disclosures. Its rule: state only what you confirm; never invent a statute, citation, fine, or date — every claim is source-cited and QA-gated. Not legal advice.
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Trigger for: explaining current FAR, DFARS, or agency-supplement text; determining documented acquisition-policy status for an agency and FAR part; comparing codified text, RFO model text, and agency deviations; tracing acquisition rulemaking; finding procurement comment periods; analyzing public comments; refreshing prior policy analysis; or producing an Acquisition Policy Impact Brief. Use published federal sources and supplied policy documents without making legal or procurement-specific applicability determinations. Do not use for clause selection, market research execution, opportunities, pricing, grants, or cooperative agreements.
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Route new policy briefing, policy update, regulatory update deck, regulatory change deck, compliance change summary, legal update deck, or legal/product policy presentation requests here before generic AIPPT. Use this whenever the user wants a new policy interpretation deck with timelines, impacts, risks, and recommended actions. Do not use this scene pack for email memos, FAQ-only outputs, compliance copy polishing, or non-deck legal writing.
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Draft compliance policies required by a framework (access control, incident response, data retention). Use when asked to "write an access control policy", "draft our incident response policy", or "we need a data retention policy".
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