「termine」の検索結果

Build, calibrate, layer, and validate production Blender materials including bare and coated metal, brushed steel, rust, aged and wet wood planks, stone, concrete, plastic, glass, fabric, water, dirt, moss, and wetness. Use when material identity, UV direction, real-world texture scale, PBR channel handling, roughness response, anisotropy, normal/bump/displacement, transmission, volume absorption, aging, or surface layering determines realism.

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。

Determine whether an app is ready to submit, then drive the current App Store release flow with asc, including validation, staging, review submission, first-time availability, subscriptions, IAP, Game Center, and App Privacy checks.

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。

Determine the TRUE cause of a failed retort run before attributing it. Ground-truth every failure (run its tests, read its agent logs, inspect its workspace) and classify it as an infrastructure false-fail, a genuine model miss, or an environment issue — never trust the gate verdict or a log signature alone. Use whenever a run is recorded failed (test_coverage=0 / gate fail), a pass rate looks low, or you're deciding whether a "failure" is real before reporting or proceeding.

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。

Define measurable success metrics (KPIs) tied to product type during PRD v0.3 Commercial Model. Triggers on requests to define success metrics, set KPI targets, determine what to measure, establish go/no-go thresholds, or when user asks "how do we measure success?", "what metrics matter?", "what's our target?", "how do we know if this works?", "define KPIs", "success criteria". Consumes Product Type Classification (BR-) from v0.2. Outputs KPI- entries with thresholds, evidence sources, and downstream gate linkages.

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。

Determine technologies needed to build the product, making build/buy/integrate decisions during PRD v0.5 Red Team Review. Handles both greenfield and brownfield contexts. Triggers on requests to select tech stack, evaluate technologies, make build vs. buy decisions, discover existing assets, or when user asks "what technologies?", "select tech stack", "build or buy?", "what do we reuse?", "existing stack", "technical decisions", "what tools do we need?", "evaluate solutions". Consumes FEA- (features), SCR- (screens), RISK- (constraints). Outputs TECH- entries with decisions, rationale, and trade-offs. Feeds v0.6 Architecture Design.

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。

Learn the target framework from enabled knowledge tools and implement a baseline GPU kernel. Use this skill to understand compute semantics, determine the target platform and framework, search reference implementations, and produce a correct V0 baseline with performance records for later profile-driven optimization.

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。

When things go wrong - and they will - how you communicate determines whether you lose customers for a day or lose trust forever. Crisis communications isn't about spin or damage control. It's about being human when your company is at its most vulnerable. This skill covers incident response communications, public apologies, data breach notifications, service outages, PR crises, and the aftermath. The goal isn't to look good - it's to be good, and communicate that clearly. Use when "crisis, incident, outage, down, breach, apology, we messed up, customers are angry, PR disaster, viral complaint, status page, postmortem, trust recovery, bad press, crisis, incident, communications, apology, trust, status-page, outage, breach, postmortem, recovery" mentioned.

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.

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。

A/B and multivariate testing methodology. Design experiments, calculate sample sizes, determine statistical significance, avoid common pitfalls, and interpret results. Platform-agnostic framework applicable to landing pages, emails, ads, pricing, and product features. Use when the user asks about A/B testing, split testing, experiment design, statistical significance, or conversion experiments.

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。

App Builder - Application Building Orchestrator workflow skill. Use this skill when the user needs Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

App Builder - Application Building Orchestrator workflow skill. Use this skill when the user needs Main application building orchestrator. Creates full-stack applications from natural language requests. Determines project type, selects tech stack, coordinates agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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。

Determine what premises and evidence warrant.

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。

Before coding, determine what to ask. Max 2 yes/no questions. Never ask technical questions.

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。

Determine whether front/side/back/top reference templates can be satisfied by one rigid 3D Blender model, choose a canonical source policy, and emit constraints before rebuilding. Use when multiview bboxes, silhouettes, part counts, or depth ratios contradict each other.

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。

Map the epistemic structure of a subject to determine knowledge types and inform curriculum sequencing. Use when designing courses, restructuring programmes, or analysing knowledge architecture.

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。

Project onboarding - determine project type, complexity, tech stack, and route to correct phase

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。

Verify mini-program implementations and fixes with risk-calibrated evidence across static checks, unit tests, integration tests, state matrices, simulators, real devices, cloud environments, and release artifacts. Use when users ask to test, validate, accept, regression-check, quality-check, confirm readiness, or determine whether a mini-program feature is actually complete. Binds results to a source and build fingerprint, records commands and observable evidence, separates passed, failed, blocked, and not-run layers, prioritizes the next highest-information check, and never converts local success into device, cloud, release, or formal acceptance claims.

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。

Ownership-validation preflight for a warp route. For each chain in the route, determine the owner type (ICA / Safe / Squads / other multisig), confirm it exists on chain (or deploy if missing for ICAs), and reject EOAs. Run before /warp-deploy-init-route generates the deploy.yaml so the deployment never proceeds against an invalid owner 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。

Validates and summarizes the four required authorization forms in a personal injury matter (retainer, HIPAA release, insurance authorization, employment record release). Confirms signatures, dates, scope, and completeness to determine readiness for the investigation phase. Use when reviewing client onboarding packets, verifying authorization forms before discovery, or checking HIPAA and record-release compliance.

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。

A/B test design — produce an experiment spec with hypothesis, primary metric, MDE, sample size, run time, and decision rule. Also determines when NOT to A/B test and what to do instead. Use when asked to "design an A/B test", "should we test this", "experiment design", "how do we know if this works", "what's the sample size", or "set up an experiment".

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。

Composes three-part DACP bundles from skill library artifacts. Determines fidelity level, queries catalog, and records assembly rationale.

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。

Deploys the current project to a live HTTPS URL via Cybrix. Activates on any request to make the current project public, get a URL for it, deploy it, ship it, host it, publish it, put it online, or make it live — including casual phrasings like "send this to the internet", "share this as a link", "give me a URL", "make this accessible online". Activates whether the user mentions Cybrix by name or not (when no other host like Vercel, Netlify, Cloudflare Pages, Railway, or Render is named). Also activates on equivalent phrases in other languages — Russian ("задеплой", "опубликуй", "выложи в сеть", "запушь это в инет", "сделай ссылку"), Spanish ("despliega esto", "publica esto", "ponlo en línea"), German ("deploy das mal", "stelle das online"), and other common languages where the intent is clearly to make a project publicly accessible via URL. Uses heuristic detection to determine if a project is static or needs a server runtime — works with any framework, not just a known list. Automatically detects and handles environment variables so the build receives everything it needs. Returns a live URL on *.cbrx.cc or a user-configured custom domain. Does NOT activate when the user explicitly names a different host (Vercel, Netlify, Cloudflare, Railway, Render).

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。

Assess and refactor oversized or multi-domain skills. First determines whether splitting or references/ extraction is appropriate — then executes the correct action. Use when a skill exceeds token thresholds (SK006/SK007) or covers multiple independent domains. Performs candidate assessment before any structural changes; cohesive single-intent skills are redirected to references/ extraction instead of splitting. When splitting is warranted — domain analysis gate, split plan, new SKILL.md generation, validation, and backwards-compatible facade conversion.

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。

How to determine which backlog backend a project is configured to use, and what to do with the answer. Use when about to call a backend-specific tool, choosing between a GitHub-only and a Beads-native operation, or reporting which store a result came from.

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。