「output」の検索結果

Use when validating or documenting a multi-stage KrillinAI CLI output plan; the current pipeline command supports dry-run planning only, not end-to-end execution.

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Builds security-focused full-stack web applications by implementing integrated frontend and backend components with layered security at every level. Covers the complete stack from database to UI, enforcing auth, input validation, output encoding, and parameterized queries across all layers. Use when implementing features across frontend and backend, building REST APIs with corresponding UI, connecting frontend components to backend endpoints, creating end-to-end data flows from database to UI, or implementing CRUD operations with UI forms. Distinct from frontend-only, backend-only, or API-only skills in that it simultaneously addresses all three perspectives—Frontend, Backend, and Security—within a single implementation workflow. Invoke for full-stack feature work, web app development, authenticated API routes with views, microservices, real-time features, monorepo architecture, or technology selection decisions.

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Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.

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Ask users questions via the UI. Use when you need clarification, user preferences, or confirmation before proceeding. The user CANNOT see CLI output - this tool is the ONLY way to communicate with them.

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Turn changelog-scan output into polished, categorized release notes draft. Propose only.

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Scan recent merges/PRs/commits for release note content. Structured output for drafter.

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Triage recent changes, CI failures, issues, and conversations. Produces a concise, actionable findings report suitable for a loop to consume. Writes structured output to a state file or issue tracker.

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Triage recent changes, CI failures, issues, and conversations. Produces a concise, actionable findings report suitable for a loop to consume. Writes structured output to a state file or Linear board.

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。

Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.

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Periodically self-assess output quality to catch degradation before the judge 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。

Read when a terminal-tools call returned something surprising — empty stdout despite no error, exit_code is null, output_handle came back expired, "too many jobs" / "session busy" / "too many PTYs", warning was set unexpectedly, semantic_status disagrees with exit_code. Diagnostic recipes only — load on demand. Don't preload; the foundational skill covers the happy path.

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Required when terminal_* tools are available. Explains foreground execution, outer collect_result handles, promoted job IDs and their retrieval/cancellation, deadlines, output retention, platform shell selection, and structured editing when enabled.

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Diff a new or changed regulation against current privacy policy and practice — outputs a gap list and a remediation plan with owners and dates. Use when a new reg drops, the user asks "does [regulation] affect us", "gap analysis for [state privacy law]", "compliance check against [reg]", or pastes regulatory text.

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Professor's review queue — student output waits here for professor approval before going to clients or courts. Only active if "formal review queue" supervision style was chosen at setup; otherwise dormant. Use when the professor wants to see what's waiting for review, approve, edit-then-approve, or return an item.

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 or review an element chart — a patent claim chart (infringement, invalidity, or review) or a civil element chart for any cause of action or defense — with every cell pin-cited and gap detection as the priority output. Use when the user asks for a claim chart, element chart, proof chart, infringement or invalidity contention, element-by-element mapping, or asks "what are we missing to prove [claim]".

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Draft a demand letter from a completed intake, gated on a privilege / FRE 408 / waiver / admission checklist, with a .docx output, post-send checklist, and an offer to create a matter. Use when the user says "draft the demand", "write the [type] letter", or has a finished demand intake ready to turn into a sendable draft.

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Guided customization of your legal clinic profile — change one thing without re-running the whole cold-start interview. Adjust clinic profile, jurisdiction, supervision style, practice-area templates, semester configuration, or output safeguards. Use when the user says "change my [thing]", "new semester", "add a practice area", "update my config", or "customize".

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Detects when Luminance, Kira, or a similar bulk-review tool is in use, hands off the high-volume clause extraction to it, and QAs its output per the trust level in `~/.claude/plugins/config/claude-for-legal/corporate-legal/CLAUDE.md`. Use when user says "send to Luminance", "bulk review", "AI extraction", or when diligence-issue-extraction hits a high-volume category.

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Review a vendor agreement, NDA, or SaaS subscription against your playbook. Identifies the agreement structure from titles, routes to the right review skill (vendor-agreement-review, nda-review, saas-msa-review), and integrates the output into a single memo. Use when the user says "review this contract", "check this MSA", "is this NDA okay", "look at this SaaS agreement", or attaches an inbound agreement for review.

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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.

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请求入口的意图识别与技能路由。按渐进式判断意图：先通用任务（直接做）、再高风险临床（拒绝）、再区分纯信源核验与医学学习、最后登记/订阅。纯信源核验先走 source-verify；原始路径受限时再进入 medical-source-failover。医学学习才调对应 skill + output-format 校验。

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指南学习流程约束。判断请求类型→读取状态→按流程创建轨道/预览/投递/排查迁移；操作命令与细则见 references/learning-operations.md。选题用权威信源，输出前经 output-format 校验。不生成诊疗/处方/急诊SOP/个体患者建议。

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Wire a new model-pricing field (a datasheet cost key like `cost_per_request`, `output_cost_per_video_per_second_720p`, etc.) end-to-end through Bifrost's pricing engine - Options struct, DB table + migration, datasheet sync upsert columns, cost calculation, custom pricing overrides, public API, OpenAPI docs, MDX docs, and the UI override form. Ends with a repo-wide probe to confirm nothing was missed. Invoked with /add-pricing-field <field_name> or /add-pricing-field (asks for the field).

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Fetch a URL and return a concise markdown summary of its content. Read-only: no files are written; the summary is returned as output only. Use when asked to "fetch and summarize", "summarize this URL", "what does this page say", or "get the content of <url>". Proactively suggest when the user pastes a URL and asks what it contains. (munder-difflin)

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。