6,548 件 ・ 関連度順
概要と使いどころ
Drafts a Motion for Bail Reduction for criminal defense pretrial proceedings. Argues current bail is excessive under the Eighth Amendment using defendant financial circumstances, community ties, and flight-risk factors adapted to jurisdictional bail standards. Use when seeking bail modification, bail reduction, pretrial release, or excessive bail challenges.
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当AI智能体需要处理以下情形时触发本技能： 1. 当前案件/问题在现行法律中没有直接、明确的规定（法律漏洞）； 2. 需要参考已有判例、相似案件或相近法律规范来推导当前问题的处理方案； 3. 用户明确要求"参照""比照""类推适用"某一规范或案例； 4. 在法律论证中需要以"相似案件相似处理"（Like cases should be treated alike）原则支撑结论； 5. 需要评估两个或多个案件/规范之间的相似性与差异性，以判断能否进行类比； 6. 在合同解释、侵权责任认定、行政处罚裁量等场景中，需要借助类似情形的处理经验形成论证。 本技能的核心是：识别相似性基础（tertium comparationis），论证类比的正当性，并在此基础上得出合理的法律结论。
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。
Drafts a court-ready ADA failure-to-accommodate complaint under Title I (employment, 42 U.S.C. § 12112) or Title III (public accommodations, 42 U.S.C. § 12182). Structures jurisdiction, EEOC exhaustion, factual allegations, causes of action, and relief. Use when drafting ADA disability discrimination complaints, reasonable accommodation pleadings, or civil rights complaints for plaintiffs with disabilities.
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。
Manages Rule 30(b)(6) corporate representative deposition workflows — drafting notice topics with reasonable particularity, building examination outlines, defending designees, handling objections, and preserving binding admissions for summary judgment or trial. Use when drafting or responding to 30(b)(6) notices, selecting and preparing designees, building topic-by-topic outlines, or triaging scope and privilege disputes. Trigger keywords: 30(b)(6), corporate representative deposition, topic list, designee, notice analysis, deposition objections, corporate admissions.
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 taking and defending Rule 30(b)(6) corporate representative depositions. Drafts topic lists with reasonable particularity, builds examination outlines for binding corporate admissions, analyzes noticed topics for objections, and prepares designees. Use when drafting 30(b)(6) notices, preparing corporate deposition topics, selecting or preparing designees, or defending corporate representative depositions.
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。
Generates structured summaries of mediation and arbitration proceedings, extracting outcomes, settlement terms, monetary amounts, obligations, releases, and enforcement steps. Trigger when the user needs to document mediation settlements, arbitration awards, or partial ADR resolutions from transcripts, agreements, mediator reports, or awards.
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。
Holistic evaluation framework for language models measuring accuracy, calibration, robustness, and fairness
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。
Heuristic search-based temporal planning and scheduling for complex multi-constraint environments
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 building Grafana dashboards backed by Prometheus, Loki, or Tempo, designing PromQL/LogQL queries, wiring template variables, setting alert rules, building SLO dashboards, or maintaining dashboards as code. Triggers: rate() vs increase() confusion, irate vs rate, label_replace, recording rules, alerting rule expressions, multi-dimensional template variables, ad-hoc filters, dashboard JSON model, provisioning via Terraform/grafonnet, p99 / histogram_quantile usage. NOT for Datadog/New Relic dashboards (vendor-specific), Grafana plugin development, or Loki ingestion pipeline tuning.
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 CPU usage is high, memory grows unboundedly, goroutines leak, mutex contention shows in traces, or escape analysis suggests excess heap allocation. Triggers: net/http/pprof endpoint exposure, go tool pprof analysis, flamegraph generation, allocs vs inuse_space heap profiles, runtime/trace event timeline, mutex profiling, block profiling, goroutine dumps, GC pressure measurement. NOT for non-Go languages, distributed tracing (use OpenTelemetry skill), or production telemetry 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。
Use for sensemaking, frame revision, anomaly handling, questioning strategy, and ambiguity diagnosis with the Data/Frame model before action is chosen. NOT for deterministic classification, settled execution planning, or situations where the frame is already stable.
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。
Build production CI/CD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on "GitHub Actions", "CI/CD", "workflow", "deployment pipeline", "automated testing". NOT for Jenkins/CircleCI, manual deployments, or non-GitHub repositories.
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。
### 1. The Fault/Error/Failure Hierarchy NOT for unrelated tasks outside this domain.
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。
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
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。
Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.
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。
Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).
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。
Implement executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.
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。
Implement AI chatbot analytics and conversation monitoring. Use when adding conversation metrics, tracking AI usage, measuring user engagement with chat, or building conversation dashboards. Activates for AI analytics, token tracking, conversation categorization, and chat performance.
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。
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
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。
Cryptographic security for agentic systems — zero-trust agent networking, signed message envelopes (JWS/JWE), capability-based security (ocaps), Merkle tree audit trails, WASM sandboxing, and formal verification. Covers CLI dev tool security, mTLS between agents, permission boundaries (least privilege for AI agents), and supply chain security for skills/plugins. Activate on: "agent security", "zero trust agents", "secure agent communication", "capability-based security", "ocap", "signed messages between agents", "agent audit trail", "sandbox agent execution", "agent permissions", "mTLS agents", "cryptographic verification", "agent supply chain", "OWASP agentic", "prove agent did X", "tamper-proof agent logs". NOT for: application-level SAST scanning (use security-auditor), network firewall rules (use infrastructure), SOC2/HIPAA compliance (organizational), or prompt injection defense (use prompt-engineer).
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。
How to design contextual inputs for an always-on AI agent with episodic memory. Covers what data to feed the agent, how to structure observations and triggers, ambient context capture (screen, audio, calendar), context window budgeting, and retrieval strategies that keep the agent grounded in what's actually happening. Activate on: "what should the agent observe", "context inputs for agent", "ambient context capture", "agent triggers", "agent input design", "screenpipe integration", "context window budget", "what data to feed my agent", "/always-on-agent-inputs". NOT for: memory architecture and storage (use always-on-agent-architecture), application ideas (use always-on-agent-applications), safety concerns (use always-on-agent-safety).
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 normative BDI reasoning to agents that must detect norms, choose which commitments to internalize, and resolve conflicts by comparing consequences. Use when obligations, prohibitions, or policies collide in autonomous systems. NOT for simple fixed-priority rules, pure constraint satisfaction, or domains where no real normative conflict exists.
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。
Build and adopt production AI agent infrastructure in 2026. Covers framework selection (LangGraph, CrewAI, AutoGen, MCP), orchestration patterns, evaluation, observability, memory systems, and tool use. Also covers the SOCIAL dimension: how to sell agent infrastructure internally, change management, measuring ROI, building trust in autonomous systems, and scaling adoption across teams. Activate on: "agent infrastructure", "agent framework comparison", "which agent framework", "sell AI tools internally", "agent adoption", "agent observability", "agent evaluation", "MCP architecture", "agentic mesh", "enterprise AI agents", "AI change management", "agent ROI". NOT for: building specific agents (use ai-engineer), designing agent behavior patterns (use agentic-patterns), prompt tuning (use prompt-engineer).
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。
Ship workflow: prepare stages/commits locally; release pushes + opens a PR; publish handles issues + versioning. Use for 'ship', 'push', 'create a PR'. Official (→main) and beta (→dev) modes.
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。