「model」の検索結果

Expert-level process simulation covering Aspen Plus, HYSYS, steady state and dynamic simulation, thermodynamic model selection, and simulation troubleshooting.

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Expert-level chemical process safety covering HAZOP, LOPA, relief system design, consequence modeling, inherently safer design, and process safety management.

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Expert-level statistics knowledge. Use when working with probability distributions, hypothesis testing, regression, Bayesian statistics, statistical inference, experimental design, ANOVA, or statistical modeling. Also use when the user mentions 'p-value', 'confidence interval', 'hypothesis test', 'regression', 'ANOVA', 'chi-square', 't-test', 'Bayesian', 'maximum likelihood', 'statistical significance', 'correlation', 'normal distribution', or 'sampling distribution'.

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Expert-level nuclear physics knowledge. Use when working with nuclear structure, radioactive decay, nuclear reactions, fission, fusion, binding energy, nuclear models, radiation, or nuclear applications. Also use when the user mentions 'radioactive decay', 'half life', 'binding energy', 'fission', 'fusion', 'alpha decay', 'beta decay', 'gamma decay', 'nuclear reaction', 'cross section', 'Q value', 'nuclear reactor', or 'radiation dose'.

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Build AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.

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Execution pipeline for Codex. Detect epic/task/bug mode and implement with Codex models only. Usage: /implement <issue#>

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Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.

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Shared in-workflow quality gates for code/PR skills — the gate model, trust tiers, and the three primitives (evidence-before-done, independent judge, anti-gaming). DO NOT TRIGGER directly — referenced by consumer skills via relative path.

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[Planning] Use when you need to start a new project from scratch with full waterfall inception — idea, research, domain modeling, tech stack, and implementation plan.

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[Content] Use when you need to evaluate business idea viability: Business Model Canvas, financial projections, risk matrix, go-to-market, execution plan.

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Model visible context

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Practical security review and hardening guidance for application code in Python, JavaScript/TypeScript, and Go. Use this skill whenever the user asks for a security review or audit, wants to harden authentication (password hashing, sessions, JWT, OAuth2/OIDC, MFA), fix OWASP Top 10 issues (injection, XSS, IDOR, CSRF, SSRF, deserialization), threat-model with STRIDE, clean up secrets and .env handling, or set up dependency scanning (pip-audit, npm audit, osv-scanner, gitleaks, SBOM). Also trigger for "make this secure", "is this safe to deploy", bcrypt/argon2id, rate limiting logins, RBAC/ABAC, CSP and security headers, cookie flags, and secure-by-default coding help.

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Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).

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Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 on SecNumCloud infrastructure). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.

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Brand mention and authority scanner for AI visibility. Analyzes brand presence across platforms that AI models rely on for entity recognition and citation decisions. Produces a Brand Authority Score (0-100) with platform-specific recommendations.

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Legacy. Use only when images (photos, illustrations, screenshots) must reach TypeSafe's Jev (System One) model as text, because the request can't send the `TypeSafe-Danger-Zone-Images` header that lets Jev read images directly. Triggers on "turn this photo into a Jev state", "describe these images for Jev", "decode images for System One", or a Jev project whose decision depends on what a picture shows. Handles 1 to about 100 images; produces a neutral, complete decode and a JSON state, checked against Jev.

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Finds and quotes TypeSafe's docs on Jev, including Noul, Choice, Score and Box questions, models and limits. Use when a question, review, design or claim involves Jev or System One, when briefing a subagent on Jev work, or when relaying a subagent's Jev finding.

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Use when rating whether a project, repo, skill, workflow, plugin, agent product or post uses TypeSafe's Jev (System One) model well -- "is this a good use of Jev?", "rate this Jev integration", "jevaluate <url>", or a link to something named after Jev; also when checking whether something that calls itself Jev actually uses it.

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Use when adding auth, webhooks, uploads, queues, secrets, tenant boundaries, or public endpoints — produces trust boundaries + abuse cases mapped to files, BEFORE implementation.

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Use BEFORE planning/coding against a DB schema, API/GraphQL shape, DTO/Model/Entity, or vendor package — read the real source, emit an Evidence Report, stop inventing fields.

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Security audit — vulnerability scan, pentest review, attack-surface sweep; explicit request only, not regular feature work. Pre-implementation threat pass → threat-modeling.

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Use when PHPStan, Rector, or ECS output appears — "phpstan says mixed", type errors, "fix code style", "run rector" — even when Eloquent/Laravel/model code is also mentioned.

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Translate an image brief into provider-specific prompt grammar per model. Use when writing or refining an image-generation prompt for Ideogram, Flux, Gemini, GPT Image 2, or Recraft.

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Use when decomposing a company objective into team OKRs, auditing a draft OKR tree, or stress-testing an existing one for measurability and laddering.

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