「judgment」の検索結果

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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Review code for quality, correctness, and fit. Use when the user wants judgment on code that already exists — their own changes, a teammate's patch, a PR, branch, commit, diff, staged changes, or one or more files to look over. Activate on requests like review, look over, sanity check, critique, code review, or 'is this good?' The key signal is that the user wants evaluation of existing code and its tradeoffs, not implementation, debugging, or explanation. This skill works independently, but when plans, specs, task artifacts, or prior discussion exist, use them to understand why the code exists before judging it.

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This skill should be used when the user asks to "direct a motion graphics video", "do creative/art direction for a video", "define a motion language", "set the tone and pacing", "make the animation feel consistent", "decide what to animate (and what not to)", "turn a brief/brand into motion choices", or needs the senior creative judgment that sits above specific techniques (gsap/lottie/shader). Covers motion language, tone & energy, pacing/rhythm, motion hierarchy, restraint, and cross-shot consistency.

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Your habits, imprinted on AI. Learns how you work from conversation, builds a portable profile, and applies judgment that adapts to you: when to act, when to check with you, when to follow the project's rules over your own defaults. Use this skill whenever the user starts a new session, opens a project, writes or reviews code, debugs, plans, or writes commits. If .dna.md does not exist yet, start the onboarding conversation before doing anything else. Active in almost every working session.

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Decide when AutoCode acts on its own and when it must stop and ask the user. Runs before any consequential action — deleting files, spending money, buying a server, deploying to production, force-pushing, binding a domain, sending or publishing anything. Ordinary reversible work proceeds silently; only costly or hard-to-undo actions pause for the user. Powered by I-Lang v5.0 vector judgment.

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Validates a running application, CLI, API, service, or generated artifact as a user or operator against a prewritten observable behavior contract while remaining source-blind. Use for acceptance checks, runtime proof, anti-fake probes, release smoke tests, or an independent companion to code review. Not for source-quality findings, root-cause diagnosis, or visual design judgment outside the contract.

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This skill should be used before answering "does Genesis have X", "does Genesis lack X", auditing Genesis capabilities, comparing Genesis to an external system, or reviewing/summarizing the architecture. It routes to the canonical judgment-layer subsystem map so audits start from the map, not from a cold grep. Also fires after changing a subsystem's capabilities, to keep the map current.

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Use this skill when the user wants intellectual understanding — learning how or why something works, not getting a task done or soliciting Claude's judgment. Trigger for: - Explicit learning requests: teach, explain, ELI5, walk me through, quiz me, flashcards, "I'm rusty on"; definitions ("what is X") - Terse concept names implying "help me understand this": "Galois theory," "transformers, from scratch" - Confusion signals: "won't stick," "keep mixing these up," "not getting it" - Learning-path questions: prerequisites, sequencing, what to study before X - Conceptual questions about mechanisms, causes, or dynamics Don't trigger for: - Tasks: coding, writing, calculation, translation, factual lookup, news updates - Personal troubleshooting; resource/textbook recommendations - Claude's evaluative verdict: opinion prompts ("do you think X", "settle this", "honest take", "is X dead / still taken seriously") and interpretive takes ("was X really as harsh as people say")

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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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Converting a supplied prompt into an executable specification: detects vague quality/quantity/explanation/style/design/technical/judgment wording, role and persona theater, and self-contradiction, then replaces each with a numeric bound, an observable behavior, or a scorable criterion — with a per-term ledger of what changed and what stayed open. Don't use for AI system design, RAG, or eval harnesses (Oracle), PRD/SRS authoring (Scribe), spec conformance verification (Attest), or SKILL.md normalization (Gauge).

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Govern mini-program release readiness and evidence across source branches, semantic versions, build artifacts, test flags, debug paths, sensitive data, permissions, privacy declarations, platform uploads, review submissions, staged versions, production releases, and rollback plans. Use when users ask for a release checklist, packaging, export, version bump, upload readiness, submission readiness, launch status, rollback preparation, or a precise judgment of what has and has not shipped. Defaults to read-only preflight, requires separate explicit authorization for every external state change, and never treats a Git push, successful build, platform upload, review submission, approval, or production publication as interchangeable.

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当用户需要判断某项损失或疾病是否在保险责任范围内时使用此 skill。适用于理赔责任判定、免责条款解读、保障范围咨询等场景。

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当用户需要判断保险理赔案件中的身故场景是否可能落入保险责任范围、分析身故理赔能不能赔、结合保单和条款判断身故责任是否可能触发、识别等待期自杀条款意外或疾病属性免责争议或评估材料是否足以支持身故责任判断时使用本 skill。适用于分析理赔系统案件记录、报案信息、居民死亡医学证明（推断）书、死亡证明、抢救记录、门急诊或住院病历、出院记录或死亡记录、尸检报告、公安机关证明、交通事故认定书、火化证明、户籍注销证明、法院判决书或司法文书、受益人申请材料、客服或审核备注、OCR 文本、PDF 文档和截图转写内容，并结合产品责任、身故责任条款、等待期、自杀条款、责任免除、意外与疾病身故区分规则和高风险行为限制形成结构化初步判断。

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当用户需要判断保险理赔案件中的重大疾病场景是否可能落入保险责任范围、分析某个重大疾病是否达到赔付定义、结合保单和条款判断重疾责任是否可能触发、识别等待期既往症除外责任定义边界争议或评估材料是否足以支持重疾责任判断时使用本 skill。适用于分析理赔系统案件记录、报案信息、门诊或住院病历、诊断证明、病理报告、影像检查报告、实验室检查报告、手术记录、出院小结、会诊意见、基因检测结果、专科医生说明、客服或审核备注、OCR 文本、PDF 文档和截图转写内容，并结合产品责任、重疾定义、等待期、责任免除、轻症中症重疾边界、多次赔付限制和疾病状态要求形成结构化初步判断。

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。

当用户需要判断某项损失或疾病是否在保险责任范围内时使用此 skill。适用于理赔责任判定、免责条款解读、保障范围咨询等场景。

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The Relic of a retiring engineering lead — judgment, intuition, and the way he taught people, kept after the person left the building. 一位退休技术主管的「专家 Relic」：人离开了公司，判断逻辑、经验直觉和带人的方式留下来。

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Understanding and integrating multiple perspectives in historical analysis. Covers historical empathy (reconstructing how past actors understood their world), analyzing events from multiple viewpoints, constructing inclusive narratives that recover marginalized voices, and avoiding presentism (the imposition of contemporary values on past societies). Use when interpreting historical actors' motivations, assessing whose stories are told and whose are silenced, or guarding against anachronistic judgment.

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Use when parallel agents in a convoy or across git worktrees hit a write conflict on shared state — a git tree, a config, or a shared document — and you must decide whether the conflict is semantically real. Classifies each conflicting hunk as independent (keep both), redundant (pick one), or a genuine clash (escalate), instead of blind locks, abort-and-retry, or escalate-everything. Sits ABOVE refinery-merge and NEVER blind auto-merges: it wraps an LLM judgment, defaults to escalate on any uncertainty, and unconditionally escalates any hunk touching .planning/, config, credentials, or safety-critical paths. Backed by CoAgent (arxiv 2606.15376v1). Triggers on write conflicts between parallel agents on shared repo state.

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Use when you have captured session evidence — session-retro / session- observatory-live traces in .planning/patterns/, tool logs, correction records — and want to induce a reusable skill from it. Segments the traces into candidate skill units (an LLM judgment, not a deterministic parse) and decomposes each candidate into a four-part structured spec: workflow structure, execution semantics, and runtime attachments (verification, safety, rollback, state). It emits a spec object, NOT a finished SKILL.md, and hands that spec to skill-forge. It sits between skill-integration (upstream frequency detector) and skill-forge (downstream author). Backed by Agent-Trace-to-Skill Induction (arxiv 2606.06893v1). Triggers on inducing a skill from captured traces, turning a repeated pattern into a skill spec, and preparing evidence for skill-forge.

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Knowledge reference for Autonomous Refinement Loop research — pattern research into prerequisites for autonomous execution without synchronous human blocking gates. Defines failure categories, prerequisites, and conditions for replacing human judgment with machine-verifiable checks. Use when designing or evaluating autonomous agent loops, gate conditions, or HOOTL execution patterns.

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Codified expertise for handling freight exceptions, shipment delays, damages, losses, and carrier disputes. Informed by logistics professionals with 15+ years operational experience. Includes escalation protocols, carrier-specific behaviors, claims procedures, and judgment frameworks. Use when handling shipping exceptions, freight claims, delivery issues, or carrier disputes.

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Runs a deliberative two-model committee of GPT-6 Astra and Claude Opus under a selectable chair, Fable by default, with `/model-committee-astra`, `/model-committee-opus`, and `/model-committee-sol` selecting the others. Use when a consequential decision has several defensible options and two model families should propose, critique, revise, and cross-rank them against a predeclared rubric before converging, as in research design, interpretation, architecture, manuscript strategy, or evaluation design. Not for factual lookups, independent-coder reliability, brainstorming, routine implementation, or final professional judgment.

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Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

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Runs a multi-model orchestration workflow led by the session's Fable or Claude Opus model. Sends menial tool-calling work to Haiku subagents, judgment work to Opus subagents at an effort matched to each task, and high-stakes or fresh-perspective questions to a GPT-6 Astra Codex peer. Detects the lead from session context; `--lead fable` or `--lead opus` overrides. Use to orchestrate, delegate, fan out, get a decorrelated Codex opinion, run a blind Opus-Codex cross-check, or act as tech lead.

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