「safety」の検索結果

Use when creating or editing a slash command in src/agent-src/commands/ — frontmatter, numbered steps, safety gates — even when the user just says 'add a /command for X'.

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Extraia dados estruturados de respostas de LLM com validação Pydantic, tente novamente automaticamente extrações que falharem, analise JSON complexo com segurança de tipo e transmita resultados parciais com Instructor - biblioteca de saída estruturada testada em batalha

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Garanta estrutura válida de JSON/XML/código durante a geração, use modelos Pydantic para outputs type-safe, suporte modelos locais (Transformers, vLLM) e maximize velocidade de inferência com Outlines - biblioteca de geração estruturada da dottxt.ai

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Use when the user wants to know what will break if they change something, or needs safety analysis before editing code. Examples: "Is it safe to change X?", "What depends on this?", "What will break?"

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**REQUIRED** - Always activate FIRST on any git/VCS operations (commit, status, branch, push, etc.), especially when HEAD is detached. If `.jj/` exists -> this is a Jujutsu (jj) repo - raw git commands can corrupt data. Essential git safety instructions inside. DO NOT IGNORE.

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EMR/EHR development patterns for healthcare applications. Clinical safety, encounter workflows, prescription generation, clinical decision support integration, and accessibility-first UI for medical data entry. Use when building EMR or EHR features such as encounter workflows, prescription generation, or clinical data entry UI.

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Design and review client UI changes — visual token discipline, reuse-before-adding, feedback surfaces (toast vs in-place notice vs empty state), overlay and menu safety, platform window adaptation, loading states, and error copy placement. Use when adding or changing product-user-visible GUI behavior, or when reviewing such a PR.

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Mathematical foundations, logical structures, and formal verification guidance. Trigger when: - Designing protocol contracts, formal specifications, verifying state spaces, or using proof assistants. - Tasks involve: TLA+, Alloy, Lean, Coq, model checkers, invariants, temporal logic. - Prompt contains keywords: formal verification, alloy, tla+, lean, coq, invariant, state machine, model checker, liveness, safety.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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Critical Git safety rules to prevent destructive operations. Use whenever performing git operations, reverting changes, or managing commits. Prevents accidental loss of work.

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Guide for when to use and when not to use return validators in Convex functions. Use this skill whenever the user is writing Convex queries, mutations, or actions and needs guidance on return value validation. Also trigger when the user asks about Convex type safety, runtime validation, AI-generated Convex code, Convex AI rules, Convex security best practices, or when they're debugging return type issues in Convex functions. Trigger this skill when users mention "validators", "returns", "return type", or "exact types" in the context of Convex development. Also trigger when writing or reviewing Convex AI rules or prompts that instruct LLMs how to write Convex code.

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Critical Git safety rules to prevent destructive operations. Use whenever performing git operations, reverting changes, or managing commits. Prevents accidental loss of work.

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。

Configure Azure API Management (APIM) as AI Gateway to secure, observe, control AI models, MCP servers, agents. Helps with rate limiting, semantic caching, content safety, load balancing. USE FOR: AI Gateway, APIM, setup gateway, configure gateway, add gateway, model gateway, MCP server, r...

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Configure Azure API Management as an AI Gateway for AI models, MCP tools, and agents. WHEN: semantic caching, token limit, content safety, load balancing, AI model governance, MCP rate limiting, jailbreak detection, add Azure OpenAI backend, add AI Foundry model, test AI gateway, LLM policies, co...

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You are an AI product engineer who has shipped LLM features to millions of users. You've debugged hallucinations at 3am, optimized prompts to reduce costs by 80%, and built safety systems that caught thousands of harmful outputs. You know that demos are easy and production is hard.

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Design spec with 98 rules for building CLI tools that AI agents can safely use. Covers structured JSON output, error handling, input contracts, safety guardrails, exit codes, and agent self-description.

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Check upgrade safety for a Tamanu upgrade that spans many versions: verify that every intermediate hotfix is included in the target version, and surface the new configuration/settings, data migrations, and FHIR rematerialisation impact the upgrade brings. Use when the user wants to check an upgrade from one Tamanu version to another (e.g. "check hotfixes from v2.31 to v2.47"), confirm no intermediate hotfix was dropped, or understand what config, migrations, and FHIR rework a multi-version jump requires. Handles Tamanu's train-release model (release/X.YY branches vs vX.YY.Z patch tags) and the cherry-pick model where the same logical fix has different SHAs on different branches. Purely git-based — no live database or Canopy access needed. Not for cutting a release (see release-cutoff-checks) or deciding which tests to run (see scope-tamanu-release-tests).

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Review skills in any project using a dual-axis method: (1) deterministic code-based checks (structure, scripts, tests, execution safety) and (2) LLM deep review findings. Use when you need reproducible quality scoring for `skills/*/SKILL.md`, want to gate merges with a score threshold (for example 90+), or need concrete improvement items for low-scoring skills. Works across projects via --project-root.

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Professional financial analysis skill for investment decisions, budgeting, financial statement analysis, and business plan evaluation. Use this skill when you need to analyze financial ratios (profitability, safety, efficiency), calculate DCF/NPV/IRR valuations, perform budget variance analysis, or conduct sensitivity/scenario analysis. Triggers include "analyze financial statements", "calculate DCF", "investment evaluation", "budget variance", "財務分析", "投資評価", "予算差異分析".

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Author an editorial HTML+SVG diagram with the Diagram Design kit - choose the visual type, read its reference, start from an example, and satisfy the safety and accessibility contract that diagram_write({kind:"html"}) enforces.

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Review+fix protocol with optional pre-core git diff review and safety guardrails (unsoundness, invariants, footguns, incidental complexity). Use when prompts say "/tw-fix this PR", "fix current branch", "fix this diff", "repair CI red", or "apply a minimal patch", and when crash/corruption/invariant-break issues need correction with a validation signal. Stop only after self-review exhausts actionable changes, each full cycle reaches post-self-review rerun plus terminal diff-review closure, and two consecutive zero-edit full cycles are clean.

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Use when modifying, removing, or refactoring code that lacks test coverage. Emphasizes the danger of untested changes and the RGR workflow to add characterization tests before modifications.

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Full type inference philosophy (never cast, never annotate inferred values), Register module declaration, from narrowing on hooks and Link, strict:false for shared components, getRouteApi for code-split typed access, addChildren with object syntax for TS perf, LinkProps and ValidateLinkOptions type utilities, as const satisfies pattern.

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Knowledge base from the NIST Framework for Cyber-Physical Systems (SP 1500-201/202/203, v1.0, 2017). Use for engineering and analyzing cyber-physical systems (CPS) and IoT: the CPS Framework's aspect × facet grid (nine aspects of concern examined through conceptualization, realization, and assurance facets), cross-property trustworthiness risk management (safety/security/privacy/reliability/resilience together), data interoperability (syntactical/semantic/contextual, canonical models, identifiers, provenance), the timing aspect (time-interval classes, time domains, time-aware networking) and secure/resilient time (GNSS jamming/spoofing, PTP/NTP attacks, holdover, the clock-error model and Allan/MTIE metrics), plus the use-case requirements method. Bridges IT and OT under a clock. Scope limits: a 2017 living/draft document, thin on post-2017 standards and on detailed control catalogs; it names but does not reproduce ISO/IEC/IEEE 42010 and 15288 process text or permission-licensed third-party figures.

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