「aml」の検索結果

Configures Codemagic CI/CD pipelines for Flutter using `codemagic.yaml`. Use when setting up automated Flutter builds on Codemagic, configuring iOS code signing without Fastlane Match, deploying to App Store Connect or Google Play from Codemagic, caching pub dependencies in Codemagic, setting up encrypted environment variable groups, running tests with Codemagic's test dashboard, or choosing between Codemagic and GitHub Actions + Fastlane for a Flutter CI/CD pipeline.

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Map and attack the authentication, authorization, session, tenant, federation, and machine-identity boundaries of a system, proving whether access controls actually hold rather than whether they appear to. Use whenever work touches login, sessions, tokens, roles, permissions, multi-tenancy, password reset, invitations, OAuth/OIDC/SAML, API keys, service accounts, webhooks, admin impersonation, or any question of "can this actor do or see this thing." Prefer this over a generic security pass for identity-heavy surfaces. Part of the audit suite; inherits the shared constitution while its own job is proving or disproving access boundaries.

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Use ast-grep (sg) for AST-aware code search and rewrite across 25 languages. Trigger for structural code matching or deterministic codemods: find every function/call/class/import shaped like X, rewrite console.log to logger.info, strip `as any`, migrate require() to import, find empty catch blocks or missing await, and scan/apply YAML rules. Prefer this over rg/grep when the target is syntax shape rather than text; use rg for string contents, comments, filenames, or regex-style byte searches.

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Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.

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Deploy containerized applications to Azure Container Apps using Azure Developer CLI (azd). Use when setting up azd projects, writing azure.yaml configuration, creating Bicep infrastructure for Cont...

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Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.

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Use for Nauthilus authentication and identity-provider work touching OIDC, OAuth2, SAML, MFA, TOTP, WebAuthn, recovery codes, LDAP-backed identity, browser login and consent flows, token/session/flow state, `private_key_jwt`, introspection, discovery metadata, endpoint-specific client authentication, and security-sensitive auth tests.

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Use when writing, running, or debugging Playwright e2e tests in this Hydrolix Grafana datasource plugin. Covers the docker-compose `playwright` service invocation, locator patterns for Grafana's react-select / Monaco editor / panel chrome, state-dependent interactions in QueryEditor, and timeout/retry expectations. Apply whenever a task touches `tests/*.spec.ts`, `tests/helpers.ts`, the `playwright` service in `docker-compose.yaml`, or the test results under `test-results/`.

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Use for environment validation: .env, config.yaml, paths, Python, Git — check + auto-fix + cron every 60min.

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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory, coverage-bounded integrity checks, two-stage peer review, and auditable quality-assurance artifacts. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로.

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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

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Use when deciding WHERE to do work on Syntropic137 itself - "run this through Syntropic", "dispatch a workflow", "should this go in a workspace or local", "kick off a run on the Mini", "dogfood this", "build Syntropic inside Syntropic", "why did the agent's push fail", "can an agent run our QA", "the execution says completed but nothing shipped". Covers what a workspace agent can and cannot do, why `.github/workflows` pushes must come from the local machine, how to write a task prompt that does not repeat a known failure, and how to verify a run actually delivered. Do NOT use for PR/release mechanics (see `devops`), for using the `syn` CLI as an end user (see the syntropic137 plugin skills), or for authoring workflow YAML.

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Create and manage Claude Code skills following Anthropic best practices. Use when creating new skills, modifying skill-rules.json, understanding trigger patterns, working with hooks, debugging skill activation, or implementing progressive disclosure. Covers skill structure, YAML frontmatter, trigger types (keywords, intent patterns, file paths, content patterns), enforcement levels (block, suggest, warn), hook mechanisms (UserPromptSubmit, PreToolUse), session tracking, and the 500-line rule.

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Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.

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AI-powered E2E web testing — eyes and hands for AI coding tools. Declarative YAML scenarios, Playwright execution, visual matching (OpenCV + OCR), platform auto-detection (Flutter/React/Vue), learning DB. Install: npx skills add ksgisang/awt-skill --skill awt -g

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。

Guidelines for modern Avalonia UI layout using Zafiro.Avalonia, emphasizing shared styles, generic components, and avoiding XAML redundancy.

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。

Write custom Semgrep SAST rules in YAML to tespit etme
application-specific vulnerabilities, enforce coding standards, and integrate into CI/CD pipelines.

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Uses Falco YAML rules for runtime threat Tespit in containers and Kubernetes, monitoring syscalls for shell spawns, file tampering, network anomalies, and privilege escalation. Manages Falco rules via the Falco gRPC API and parses Falco alert output. Use building yaparken container runtime security or investigating k8s cluster compromises.

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Use when adding or changing REST endpoints or the OpenAPI contract — deriving endpoints from the Lean commands and views, writing documents/codebase/openapi.yaml first, then regenerating the backend controller interfaces and the frontend client.

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MUST USE when starting a new video project, when the user provides footage and asks to edit/create a video, or when 01_intent/creative_brief.yaml does not yet exist. This skill interviews the user to understand their intent before any editing begins. Never skip this step.

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Use when timeline.json exists and the user asks to review the rough cut, critique the edit, or generate review_report.yaml and review_patch.json.

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Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.

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Publication-quality physics plots — vector fields, streamlines, contour maps, 3D surfaces, phase space, spectrograms, and animations. Optimized for journal submission with LaTeX labels, proper colormaps, and multi-panel layouts.

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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.

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。