「production」の検索結果

Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.

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 Docker, CI/CD pipelines, deployment strategies, infrastructure as code, and observability setup. Use when writing Dockerfiles, configuring GitHub Actions, planning deployments, setting up monitoring, or when asked about containers, pipelines, Terraform, or production infrastructure.

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。

Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.

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。

Print and mail physical letters and postcards through Lob, verify US addresses, and cancel pieces before production. Trigger phrases: lob, send postcard, send letter, direct mail, print and mail.

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。

Debugs failing GitHub Actions CI runs for PostHog PRs, commits, and branches, and answers broad CI-health questions ("is CI red?", "is master green today?", "what's broken right now?"). Use when the user asks why CI is red, asks for the current CI or master status, or mentions a failing check, GitHub Actions run, Depot runner, workflow, job, shard, merge queue kick, flaky test, lint failure, typecheck failure, snapshot diff, migration check, generated types drift, or skills build failure. Start with the `hogli ci:insights` digest (cross-run CI history from engineering analytics), then guides read-only inspection, failure classification, smallest local reproduction with hogli, and safe reporting without rerunning CI or posting to GitHub. Running unattended as the "Master-red diagnosis" workflow: see references/master-red-incident.md.

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 asked to deploy, release, publish, or ship OpenMeld to production — workers, Web, CLI, or local components. Routes to the guarded release lanes; never deploy raw.

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。

Create fast, high-impact 15-30 second brand and product marketing videos from an official company website, verified brand sources, or user-uploaded product imagery. Use for launch films, website motion videos, AI/SaaS demos, product promos, and Vibe Motion/Remotion productions that require exact logos, authentic imagery, English-first copy, product-feature storytelling, strong visual impact, smooth transitions, and deterministic frame-based animation.

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。

Audit whether tests earn their maintenance cost and which suite owns each contract. Use when pruning redundant tests, investigating implementation coupling or test-only production hooks, reviewing the value of proposed coverage, or auditing an entire subsystem. Use write-tests to implement the resulting test changes.

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。

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.

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。

Search LCSC Electronics for electronic components — find parts by LCSC number (Cxxxxx) or MPN, check stock/pricing, download datasheets, analyze specifications. Sister company to JLCPCB, same parts library. Sync and maintain a local datasheets directory for a KiCad project, or use batch MPN-list seeding (`--mpn-list`) for bulk workflows without a project. No API key needed — uses the free jlcsearch community API. Use this skill when the user mentions LCSC, JLCPCB parts library, JLCPCB assembly parts, production sourcing, Cxxxxx part numbers, needs to find LCSC equivalents for parts, is preparing a BOM for JLCPCB assembly, or wants to download datasheets and LCSC is available. For package cross-reference tables and BOM workflow, see the `bom` skill.

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。

Takes existing content markdown files and builds production-final resource center pages on client websites using their existing tech stack and design system. Output is live-ready — no review pass, no placeholder content, no prototype styling. Implements hub pages, section listings, article pages, and cross-linking for Blog, Guides, Learn, and Comparisons sections.

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。

Take an agent-written app from repo to live production on the user's OWN accounts, with providers they choose (hosting, database, auth, payments, email, domain/DNS). The human connects accounts and approves changes; supported wiring operations run through a local CLI and produce verification evidence with explicit limits. Use when the user wants to ship, deploy, go live, launch, publish, or put their app online, or asks to wire up env vars, webhooks, auth settings (signup, email confirmation, password policy), a real signup → confirmation email → login journey, password recovery, account isolation between two users, auth redirects, email DNS or a custom 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。

29 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for AI agents with minimal token output.

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。

Review generated or changed test code against universal testing rules before it ships. Best used reactively after an agent writes, edits, generates, or refactors tests, before presenting, committing, or merging them. Use for pytest (test_*.py, *_test.py), PHPUnit/Pest (*Test.php), Jest/Vitest (*.test.ts, *.spec.js), Go (*_test.go), files under tests/, __tests__/, or spec/, and review requests like 'write tests for X', 'add tests', 'test this', 'review these tests', or PR diffs containing tests. Can also guide test writing when explicitly invoked before the work. This skill is the quality gate that prevents AI-generated test bloat. DO NOT USE for production or implementation code review (use clean-code-guard), CI or test-runner configuration, running or debugging tests, or general architecture discussion.

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。

Review generated or changed documentation before it ships — READMEs, API references, docstrings, PHPDoc/JSDoc, changelogs, tutorials, and doc sites. Best used reactively after an agent writes or edits docs, after code changes documented behavior, or before publishing docs. Use when the user says 'review the docs', 'is this documentation accurate', 'update the docs', 'write a README', 'document this API', 'add a docstring', or 'add a changelog entry'. Core job: verify every referenced function, flag, endpoint, config key, and code sample against the source; catch docs-vs-code drift; strip filler and unverifiable claims. DO NOT USE for production code review (use clean-code-guard), test review (use test-guard), marketing copy or blog posts, prose style editing of non-technical writing, or documentation site theming.

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 strengthening ACM CCS reproducibility evidence, including the artifact-availability posture, threat-model-to-evidence mapping, attack reproduction steps, defense overhead measurement, measurement-dataset provenance, environment and version pinning, and honest justification when artifacts cannot be shared.

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 Simulink models for Embedded Coder (ERT), Simulink Coder (GRT rapid-prototyping), or AUTOSAR code generation. Use when the user asks to generate embedded C or C++ code, run a full build of a model, produce a code generation report, configure a model for production/ECU deployment or rapid-prototyping code, target ARM or x86 hardware, apply MISRA C/C++ compliance (ERT/AUTOSAR only — Simulink Coder does not ship MISRA profiles), or set up GRT, ERT, AUTOSAR, or shared-library targets. Handles target selection, hardware mapping, model hierarchy propagation, and constraint introspection via the configure_for_codegen function. Do NOT use for GRT shared-library variants (grt_malloc.tlc), DDS, or ROS, or for iterative optimization workflows that measure baseline metrics, apply targeted changes, and re-measure to confirm improvement.

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。

Sanitize and anonymize a Simulink model for external sharing. Use when stripping proprietary content, anonymizing block/signal names, removing metadata, or creating a shareable reproduction model for bug reports. Do NOT use for simply copying or sharing a model internally.

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。

Patterns for building entity actors with Akka.Hosting - GenericChildPerEntityParent, message extractors, cluster sharding abstraction, akka-reminders, and ITimeProvider. Supports both local testing and clustered production 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。

Configure Akka.NET with .NET Aspire for local development and production deployments. Covers actor system setup, clustering, persistence, Akka.Management integration, and Aspire orchestration patterns.

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。

Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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 the user wants to triage a CORPUS of production crashes/hangs from an aggregator (Sentry, App Store Connect) — grouped, counted issues — rather than a single crash file.

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 the user mentions Core Data review, schema migration, production crashes, or data safety checking.

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。