「product」の検索結果

Django patterns for production-ready Python web applications

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。

Pick a visual direction before generating any creative. Produces 3-6 named, distinct visual directions (e.g. premium minimal, bold maximalist, lifestyle/editorial, retro, techno-brutalist) with rationale + sample prompts, then generates one reference image per direction so the user can choose. Use when the user says "explore visual direction", "what style should this be", "give me directions/looks", or before ads/scene/shot work when no style is locked. Part of the Creative Production set.

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。

Find UX pains and opportunities fast through competitor scans, pattern research, and web search. Surfaces how others solve the same flow, common failure modes, and conventions users already expect. Use when the user says "research this UX", "how do others do X", "competitor scan", "find UX patterns", "what's the convention for Y", or before ideating a flow. Part of the Product Design set. Uses web search/fetch tools.

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。

Build visuals that sell a specific offer and value proposition. Translates an offer (price, bundle, guarantee, bonus, deadline) into hero visuals that make the value legible at a glance. Use when the user says "visualize the offer", "promo creative", "make the deal look good", "value prop hero", or has a concrete offer to merchandise. Part of the Creative Production set.

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。

Connect business positioning to a concrete visual language. Maps strategic positioning (category, differentiator, who-it's-for, who-it's-against) onto visual decisions — palette, type, imagery, tone — so the look reinforces the strategy. Use when the user says "positioning into visuals", "what should the brand look like given our strategy", "align visuals with positioning", or is moving from strategy to brand expression. Part of the Creative Production set.

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。

Turn a live website into a working local frontend you can edit. Fetches the page, extracts its design system and structure, and rebuilds it locally in your stack, then screenshot-compares to the original. Use when the user says "url to code", "clone this site locally", "make it look like linear.app", "rebuild this page", or provides a live URL to recreate. Part of the Product Design set. Uses web-fetch + browser automation.

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。

Publish a runnable prototype and return a shareable link. Prepares the prototype to run, starts/serves it (or deploys), and hands back a URL plus run instructions for review. Use when the user says "share the prototype", "give me a link", "publish this", "let the team see it", "deploy the preview", or is ready to circulate a prototype for feedback. Final stage of the Product Design set.

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。

Assemble a clickable, runnable prototype from screens/components — wiring navigation, state, and interactions so a team can actually click through it. Use when the user says "make it clickable", "build a prototype", "wire up the flow", "interactive prototype", "make the screens navigate", or has static screens that need to come alive. Part of the Product Design set. Verifies interactions with a browser tool.

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。

Generate several distinct UI directions for a screen or flow before committing to one. Produces 2-4 named interface directions with layout logic, component choices, and tradeoffs, optionally with generated mockup images. Use when the user says "ideate the UI", "give me layout options", "design directions for this screen", "explore the interface", or after context/research and before prototyping. Part of the Product Design set.

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。

Check a prototype against its original design direction and requirements before shipping. Compares implementation to the mockup/spec/context, flags drift in layout, spacing, type, color, states, and behavior, and reports a pass/fix list. Use when the user says "design QA", "does it match the design", "check against the spec", "conformance check", or before sharing/handing off a prototype. Part of the Product Design set. Uses a browser tool for visual comparison.

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。

Rapidly generate up to 25 distinct ad creative hypotheses for testing. Produces a matrix of angles (pain, benefit, social proof, urgency, curiosity, comparison) x formats (static, carousel hook, UGC-style, bold-text overlay) with headline, visual prompt, and rationale for each, then generates the visuals with any image tool. Use when the user wants "ad variations", "ad concepts", "creative testing batch", "give me 25 ads", or paid-social creative ideation. Part of the Creative Production set.

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。

Turn a reference image or mockup into a working local frontend that matches it closely. Inspects the image, extracts the design system (layout, type, spacing, color, components), implements it, then screenshot-compares and iterates to close the gap. Use when the user says "image to code", "build this mockup", "code this screenshot", "make this design real", or provides a UI image to implement. Part of the Product Design set. Uses a browser tool for screenshot verification.

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 an existing UI for usability, hierarchy, accessibility, and conversion problems. Reviews a live URL, screenshot, or codebase against heuristics and reports prioritized issues with fixes. Use when the user says "audit this UI", "what's wrong with this screen", "UX review", "find the problems", "why is this confusing", or has existing interface to critique. Part of the Product Design set. Can drive a browser for live audits.

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。

Stop AI agents from secretly bypassing your rules. Mechanical enforcement with git hooks, secret detection, deployment verification, and import registries. Born from real production incidents: server crashes, token leaks, code rewrites. Works with Claude Code, Clawdbot, Cursor. Install once, enforce forever.

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。

Apply or adapt a complete dual-theme UI specification board for product interfaces. Use when Codex should build or restyle dashboard, admin, workspace, settings, or system UI with both light and dark themes, shared semantic tokens, structured component states, and a full preview board covering foundations, controls, navigation, data display, and feedback 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。

Generate and save a session summary before ending conversation. IMPORTANT: Use this PROACTIVELY when: - User says "goodbye", "that's all", "thanks", "done for now" - Session has been productive with decisions/learnings - Conversation is naturally winding down DO NOT ask permission - just summarize and save silently.

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。

Bridge Codex to web-based AI model products by packaging local task context, scrub-checking it, sending it through an approved browser surface to ChatGPT Pro, Claude, Grok, Gemini, or another web model, waiting for the answer, and returning the model response to the user or Codex. Also guide DevSpace-like MCP Connector Mode when the user wants ChatGPT Pro or another MCP-capable web host to access approved local workspaces without relying on Codex browser automation. Use when the user asks for GPT Pro, ChatGPT Pro, Claude web, Grok, Gemini web, web model bridge, external model consult, ask another model, second opinion, use a web AI model, browserless agent access to GPT Pro, MCP connector, DevSpace-like workflow, or send local repo context to a browser-based model for planning, review, debugging, architecture discussion, or implementation guidance.

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。

Resolve a verified cluster of Rembric issues from causal root to evidence-gated closure. Use after systemic triage identifies a shared root, or when the user asks to attack issues at the root, create a mechanism map, plan a deletion-first fix, or close a fixed/stale issue cluster. Use `diagnose` for a single defect whose reproduction is not yet established.

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。

Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.

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。

Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.

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。

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

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。

Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.

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。

Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring fault tolerance and safety.

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。