「judgment」の検索結果

Verifies UK case-law citations, pinpoint references and quotations against the official public register — The National Archives' Find Case Law — and statutory references against legislation.gov.uk, before a document that cites them is relied on, served or filed. Every check returns a graded verdict: VERIFIED, MISMATCH (the citation resolves to a different case — the classic AI miscitation), NOT ON REGISTER, OUTSIDE COVERAGE (the register cannot answer — absence proves nothing), or UNCHECKABLE (a law-report citation). The grading exists because there are two ways to get this wrong: citing a case that does not exist, and accusing a real case of not existing. Use when asked to check citations, verify a case exists, confirm a quotation is verbatim, check a pinpoint paragraph, audit a draft's authorities, screen a document for hallucinated cases, or check a statutory provision is in force. Not a substitute for reading the judgment: existence is not authority.

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Verification-first methodology for the post-WWII tribunals: the IMT at Nuremberg, the twelve Nuremberg Military Tribunals, and the IMTFE at Tokyo. Citations are verified against the official records (Blue/Green Series, Pritchard-Zaide) and the digital archives (Avalon, Harvard NTP, UVA IMTFE). Guards the four classic traps (IMT vs NMT; Nuremberg vs Tokyo; Charter article numbers; majority judgment vs separate opinions, e.g. the Pal dissent). Research aid, not legal advice. Part of the open-source "Skills for International Justice" library — methodology: github.com/jeannesulzer/international-criminal-tribunals-skills

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a skill that determines whether a given subject matter qualifies for copyright protection under EU law by applying the Cofemel two-step test (concept of work + originality), grounded in fifteen CJEU judgments (including the most recent Mio/Konektra and Calinescu) and the four EU directive provisions on originality. It argues from one of two positions: pro-rightsholder (establishing that the work is original) or pro-alleged-infringer (contesting work status or originality via the four exclusion grounds — technical function, rule-dictated outcome, sweat of the brow, idea/functionality). It includes sector-specific modules for photography, software/GUI, sporting events, databases, applied art, functional texts, derivative works/critical editions, and multimedia works.

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EU Digital Regulation Legal Analysis Forge — generates a tailored expert prompt for structured legal analysis of an EU digital-regulation document, optionally executes it in-session, and always produces a plain-English explainer alongside the formal output. Handles Regulations, Directives, Commission Guidelines, EDPB Opinions, CJEU and AG judgments, national DPA decisions, codes of conduct, and draft consultations across the EU digital stack.

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Turn a lawyer's bounded expertise, workflow, review standard, or legal judgment pattern into a safe, inspectable legal AI skill. Use when a user provides legal memos, comments, checklists, negotiation notes, review habits, compliance playbooks, or professional feedback and wants a reusable Lawve-style skill. Do not impersonate the lawyer or claim to reproduce a person; distill only authorized, source-bounded capability.

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Searches the domestic courts of 122 jurisdictions — 153 databases, 49 automated — for extradition and arrest-warrant surrender decisions, each in its own language and vocabulary. Answers the question no commercial database does: how are OTHER executing states treating this requesting state, or this ground? Searching by requesting state localises its name per jurisdiction (AE becomes 'Emiratele Arabe Unite' in Romania, 'Emirati Arabi Uniti' in Italy), so the sweep finds what those courts actually wrote. Every hit passes a relevance gate; every search leaves a dated report, so the research is reconstructable months later. Use when asked what foreign courts have said about surrender to a given state, whether any European court has refused extradition on prison conditions, assurances or Article 3, for comparative extradition research, or to build a foreign-authority section of an extradition argument. Not for domestic case law of the user's own jurisdiction, and never a substitute for reading the judgment.

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AI co-counsel for divorce and family-law attorneys — a jurisdiction-portable scaffold spanning the full matter lifecycle. Eight operating modes mirror how a matter actually moves: intake and onboarding, financial disclosure, children and support, property division and QDRO, discovery and document review, drafting, negotiation and mediation prep, and court prep — plus post-judgment modification and enforcement. The methodology is jurisdiction-agnostic: it forces real, verifiable research for every local form, formula, or rule rather than inventing one, handling common-law, community-property, civil-law, and MENA personal-status regimes as variables. Built around one non-negotiable: privilege. It drafts, analyzes, organizes, and pressure-tests. It is not the lawyer.

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Produces detailed law-school case briefs of United States judicial opinions in a fixed nine-section structure: memory jogger, facts, procedural history, judicial votes, holding, opinion-by-opinion analysis, five future-application hypotheticals, critique, and verified key quotations. Also builds vote tables, cross-case consistency tables, perspective-based critiques, and LaTeX in article, Beamer and casebook-chapter formats. Use whenever someone asks to brief a case, analyse or explain an opinion, summarise a decision for students, generate hypotheticals from a holding, critique a judgment, tabulate how justices voted, or convert case analysis to LaTeX. Triggers on "brief this case", "case brief", "explain the holding", "what did the court hold", "hypotheticals based on", "critique this opinion", "vote table", "Beamer presentation", "chapter brief", and on any reference to a judicial opinion or Supreme Court decision in a teaching context. Requires web search to verify quotations, votes and holdings.

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When you want to set up an agent loop, cron-scheduled task, or recurring workflow that runs autonomously. Judgment layer on top of Claude Code's ScheduleWakeup, CronCreate, and /loop — or, on other hosts, cron/GitHub Actions plus a headless agent CLI. Picks dynamic pacing, fixed cron, or a one-shot loop; designs idempotent loop bodies; sets bail-out conditions so loops don't run forever. Examples — weekly review pulse, daily brief, hourly metric monitor, periodic vault compile, upstream-check for an adapted skill. Triggers on "/loopify," "set up a loop," "schedule this task," "run this daily," "run this weekly," "cron this," "make this recurring," "automate this on a schedule," "keep this running until X." Part of the -ify trifecta (skillify / toolify / loopify). NOT for authoring a new skill — that's skillify. NOT for adding a tool/integration — that's toolify.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Write, compose, integrate, and improve programs that call Jev, TypeSafe's System One judgment model.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.

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Makes agent reasoning visible, surfaces tradeoffs, and fades help so humans build judgment. Use when reviewing or learning from agent-written code.

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Adaptive, targeted web fuzzing - deterministic wordlist selection (wl-pick.sh) plus judgment. Picks the right SecLists list per surface (content/vhost/api/params/artifacts) smallest-first, calibrates filters against soft-404s, recurses, escalates T0 harness -> T1 seclists -> T2 cewl -> T3 app-specific on signal, pivots to hidden-param fuzzing, and detects/handles WAF/Cloudflare/throttle (backoff, origin-bypass, or hard STOP on the DoS tell). Engagement-type aware (ctf loud, pt calibrated, bb stealth). Use for "fuzz", "content discovery", "directory brute", "vhost fuzz", "find hidden params", "which wordlist", "gobuster/ffuf/feroxbuster/cewl/arjun".

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Reconstruct and report long-running or multi-turn research, architecture questions, reviews, decisions, completion results, and status as a clear, self-contained brief. Use when a reader must re-enter earlier context, understand the real question and its relationships, distinguish verified facts from judgment and open evidence, or make a decision without reconstructing the work log. Quote the relevant original user wording before substantive analysis, keep one coherent main line, use spatial diagrams when relationships matter, and finish with an explicit decision boundary and practical next step.

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Transform an AI agent into a disciplined software development partner with strong judgment, transparent decisions, proportionate verification, and craftsmanship. Use for software implementation, feature work, bug fixing, refactoring, optimization, migrations, and sustained collaboration inside a codebase. Do not use merely because a task involves an AI agent; use a dedicated agent-design skill when the primary problem is agent architecture.

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能力蒸馏工作流——从用户批准的强模型访谈或真实任务轨迹中提取非显然的判断规则，形成可审计的 judgment packet，再交给 skill-audit 和 skill-creator 决定是否落成可加载 skill。当用户说“蒸馏这个模型的判断力”“把这次任务的关键决策固化下来”“模型窗口要关了，保留它在某类场景的判断”时使用。普通流程文档、直接写 SKILL.md、泛化最佳实践或未授权的会话日志扫描不使用本 skill。

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Judgment rules for locating the correct boundary of a requested change: staying inert outside it while completing every required site inside it. Use when scope is ambiguous, a diff touches neighboring surfaces, required dependent edits are unclear, or a proposed compatibility layer, migration, fallback, flag, abstraction, or parallel implementation may exceed the request.

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Judgment rules for user-facing text and docs: CLI and diagnostic output, error and help text, README and docs structure, code comments, titles, and generated reports, decks, or exports. Use when writing or changing any user-visible string, when adding or restructuring docs or deciding which page owns a fact, when a page is about to record a version, a deployment state, or a value the code already owns, when a comment, title, or artifact could carry the reasoning or an abandoned option behind the change, when a behavior change needs its copy sites swept, or when reviewing a diff that touches copy or docs.

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Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked to run smoke tests, validate an end-to-end user journey, dogfood a product, test a release candidate, or execute a non-deterministic workflow that may include authentication or human checkpoints. Do not use as a substitute for deterministic unit, integration, or scenario tests.

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Thesis report synthesis — turn a thesis's markdown artifacts into its letter-size thesis-report.html, then OPTIMIZE it against real page renders. Four-step loop — pack (deterministic Python bundle of all sources + report/metrics.json), author (the LLM writes report/content.html — narrative, KPI tiles, badges, timeline, tables and citations are LLM judgment), assemble (deterministic validation, template injection, Q50 pins, Q47 overflow gate), render + optimize (Chrome headless per-page PNGs — the LLM reads them and iterates until every page is visually clean). Spec 046 Q46–Q50.

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Append-only gap closure and the cadence engine for research theses. Evaluates artifact CURRENT state (never git history, never task checkboxes) against current pins and wrong_if falsifiers, then appends a Convergence section to tasks.md re-proposing the gaps. A clean run leaves tasks.md byte-identical. Deterministic gaps (stale, invalidated) are uncapped; judgment-class gaps are capped per spec 046 Q40.

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Clinical-trial readout analysis: pull the trial, evaluate the readout with AdCom-style scrutiny (endpoints, statistics, subgroups, missing data, safety, tolerability/persistence), place it in a cross-trial comparison lattice vs SoC and class peers, and size the stock reaction with historical grounding. The judgment core for binary biotech events.

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