「conform」の検索結果

Guide pour la rédaction de notifications de licenciement conformes au droit du travail français. Utiliser ce skill quand l'utilisateur demande de rédiger, préparer, créer ou éditer une lettre de notification de licenciement, notamment pour faute grave, faute lourde ou motif personnel. Le skill guide la collecte d'informations précises et la rédaction d'une notification juridiquement solide avec tous les éléments obligatoires du droit français.

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Expert EU Cyber Resilience Act (CRA) advisor for Regulation (EU) 2024/2847 — mandatory cybersecurity and vulnerability handling requirements for all products with digital elements (PDEs) sold in the EU. Use this skill for gap analysis, product classification (Default / Class I / Class II), conformity assessment route selection, CE marking, SBOM requirements, vulnerability and incident reporting to ENISA/CSIRTs, support period obligations, and manufacturer/importer/distributor duties. Trigger for EU CRA, Cyber Resilience Act, PDE compliance, Annex I requirements, SBOM EU, CE marking cybersecurity, or connected product security EU.

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EU AI Act (Regulation (EU) 2024/1689) compliance advisor — risk classification across all four tiers, all 9 prohibited practices (Art. 5, including the nudification/CSAM prohibition from Dec 2, 2026), all 8 Annex III high-risk use case areas, provider and deployer obligations (Arts. 9–17, 26), GPAI model obligations including the July 2025 Code of Practice (Arts. 51–55), conformity assessment and CE marking (Arts. 43–48), EU AI database registration, Art. 50 transparency (chatbots, synthetic media, AI-generated content), governance (AI Office, AI Board), penalties (Art. 99), confirmed phase-in timeline (Digital Omnibus adopted June 29, 2026: Annex III deferred to Dec 2, 2027; Annex I to Aug 2, 2028), and cross-framework mapping to ISO 42001, NIST AI RMF, and GDPR. Use for any EU AI regulation, AI system classification, or AI compliance question. Current as of July 2026. GPAI enforcement powers activate August 2, 2026.

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Analyse systématique d'un Data Processing Agreement (DPA) au regard de l'article 28 RGPD, des lignes directrices EDPB 07/2020 et 02/2024, des CCT 2021 (décision d'exécution 2021/914), des recommandations EDPB 01/2020 (mesures supplémentaires post-Schrems II), et du Règlement (UE) 2024/1689 (Règlement IA). Produit un rapport structuré clause par clause (18 clauses : 13 obligatoires + 5 complémentaires) avec diagnostic 🟢/🟡/🔴, remédiations prêtes à insérer, analyse détaillée des transferts internationaux, vérification Règlement IA, et questions à poser au fournisseur. Triggers : "analyse de DPA", "audit DPA", "vérifier un DPA", "DPA fournisseur", "data processing agreement", "art. 28 RGPD", "sous-traitant RGPD", "négociation DPA", "review DPA", "conformité contrat sous-traitance".

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Orientação completa sobre conformidade com regulamentações do Banco Central do Brasil: Resolução CMN nº 4.893/2021 (Política de Segurança Cibernética), Resolução BCB nº 85/2021 (GRSIC), Open Finance Brasil (Resoluções BCB nº 32/2020 e atualizações), e demais normas prudenciais do BACEN. Cobre elaboração e revisão de Política de Segurança Cibernética, Plano de Ação e Resposta a Incidentes (PARI), Gestão de Riscos de Serviços de Informação e Comunicação (GRSIC), consentimento e compartilhamento de dados no Open Finance, requisitos de API, gestão de terceiros (outsourcing) e sanções do BACEN. Triggers: Bacen, Banco Central, CMN 4.893, Resolução 4.893, segurança cibernética bancária, PARI, GRSIC, Open Finance, Open Banking, compartilhamento de dados financeiros, consentimento Open Finance, API financeira, outsourcing bancário, risco cibernético, incidente cibernético banco, LGPD financeira, fintech compliance, instituição financeira, DICT, Pix segurança.

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Swift 6.2 concurrency updates including default MainActor inference, @concurrent for background work, isolated conformances, and approachable concurrency migration. Use when adopting Swift 6.2 concurrency features or fixing data-race errors.

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。

Called when users need to create new OpenCode skills. Automatically creates directory structure and writes SKILL.md files conforming to specifications.

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。

Basic compiler conformance fixture.

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。

PR size-based review depth, performance review checklist, architecture conformance checks, and framework-specific review 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。

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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。

Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.

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。

Cloud quantum chemistry platform with Python SDK. Run geometry optimization, conformer generation, torsional scans, and energy minimization (DFT/semiempirical), and retrieve properties (dipole, partial charges, frontier orbitals) — no local QC software or HPC needed.

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Rigorously and honestly assess a NEW or proposed covariance / correlation / precision estimator, or a new covariance scoring rule, using precise. Use when someone proposes, asks to evaluate, or wants to compare a covariance methodology. Covers implementing it to the contract, conformance, benchmarking against the registry, out-of-sample validation, and statistically defensible inference.

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Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.

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Select, build, and validate Blender deformation and motion-control systems including Simple Deform, Curve, Lattice, Mesh Deform, Shrinkwrap, Surface Deform, Armature, shape keys, hooks, constraints, and drivers. Use when an object must bend, twist, taper, follow a path, conform to a surface, deform smoothly, articulate, or move through controlled mechanical relationships.

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Manages the Morphir knowledge base under kb/ — OKF bundles and concept documents. Use when adding content to a bundle, creating a new bundle, checking the knowledge base for conformance or provenance drift, building or querying its SQLite index, managing intent through its lifecycle, recording or reading architectural decisions as Decision Records, or navigating, searching and listing its bundles, concepts and links.

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Think and work like an expert Computational Chemist. Use when a task calls for Computational Chemist judgment. Reasons from Kohn–Sham DFT, def2/D4 functional selection, and conformer ensembles through VASP/Gaussian/ORCA, AMBER/GROMACS MD, CREST/CENSO sampling, ONIOM/QM/MM electrostatic embedding, GMTKN55 validation, and SCF convergence escalation while treating B3LYP/6-31G*, BSSE, link-atom artifacts, and force-field mismatch as first-class failure 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。

Think and work like an expert Biophysicist. Use when a task calls for Biophysicist judgment. Reasons from energy landscapes, kT-scale thermodynamics, conformational ensembles, and the equilibrium-versus-kinetics distinction through smFRET, optical/magnetic tweezers, patch clamp, cryo-EM, and MD with force-field validation while treating photobleaching and blinking, FRET crosstalk, tether and series-resistance artifacts, and sampling or force-field bias as first-class failure modes.

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Test AI/LLM features that ship in your product. Covers prompt regression testing, response quality evaluation, tool-call validation, hallucination and RAG grounding checks, nondeterministic-output strategies, red-team/safety scans, eval frameworks, and agent-as-target injection (indirect injection via tool output / RAG / scan reports, self-propagating payloads, data exfiltration via an agent) plus a bundled detector for untrusted content. Use when: "test our LLM feature," "prompt regression test," "eval framework," "hallucination test," "RAG grounding," "nondeterministic output," "AI feature testing," "red-team our chatbot," "indirect prompt injection," "agent reading untrusted tool output," "production AI quality." Not for: using AI to generate your own test code — use ai-test-generation. Not for: classifying CI failures with AI — use ai-bug-triage. Not for: EU AI Act / GDPR conformity of an AI feature — use compliance-testing. Not for: canary/flag rollout of an AI feature — use testing-in-production. Related: ai-test-generation, ai-qa-review, api-testing, compliance-testing, security-testing, risk-based-testing, test-data-management.

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Runs a long swarm of hundreds to thousands of cheap headless coding workers that drive a large failing-test backlog to green. Failures are clustered by signature, each worker fixes one cluster in its own git worktree, a serial no-regression merge gate lands the fixes, the loop resumes across restarts for hours, and it federates across sibling sessions over git branches when one container's CPU is not enough. Use for "fix these thousands of failing tests with a Haiku swarm", "drive the test suite to green with hundreds of agents", "build this compiler or interpreter against a conformance suite", "launch, relaunch or check in on the swarm loop", "the swarm is stuck, idle or rejecting everything", "parallel agents fixing tests keep hitting merge conflicts and regressions", "add sibling containers to the swarm", or "compare models on the tail the swarm could not fix". Ships the harness, a per-project TOML config, a fake-worker selftest, check-in signals and the 2026-10-12 haiku-cc failure catalogue.

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Use when auditing a user-facing app — web, mobile (iOS/Android/React Native/Flutter), desktop, CLI, or games — for accessibility barriers or WCAG 2.2 conformance, before shipping UI changes, or in response to concerns about screen-reader, keyboard, low-vision, motor, cognitive, or photosensitive users. Not for general bug hunting or code correctness.

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Use when your context carries "The approved design this task builds" - comparing the running UI with the approved hand-off and mockup at each width and state, evidence screenshots attached to the task, Blocker/High/Medium/Nitpick findings and which of them fail the round

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Audit, score, and compare repositories containing portable Agent Plugins against the official Agent Plugins specification. Use when asked to review a plugin repo, check plugin.json or mcp.json conformance, assess bundled skills and MCP servers, produce an evidence-cited 0–100 plugin scorecard, identify release blockers, or compare two agent plugins side by side.

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Navigating delivery status read-only: reconciles planned scope (specs/roadmap/PRD) against implemented code for what's built vs left. Not for priority scoring (Rank) or AC conformance (Attest).

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。