「concrete」の検索結果

Resume work on the FIFA Skeletal Tracking Light 2026 challenge using vailá modules (SAM 3, soccer pitch keypoints, calibration, per-frame DLT). Use whenever the user reopens the project after closing the terminal, asks "where did we stop on FIFA?", or wants the next concrete commands to run from `~/data/vaila` with `.venv` already created.

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 vague idea into a concrete, actionable spec through a short Socratic dialogue, then hand the result off to an existing moflo surface — a /flo ticket, a spell, or memory. Use BEFORE you have a defined unit of work, when the goal is still fuzzy.

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-before-done — exercise the current change end-to-end against its acceptance criteria (the SDD plan's, or the ticket's) and report a per-criterion PASS/FAIL verdict, then record the outcome to memory. This is the concrete action that satisfies moflo's verify-before-done gate (`gates.verify_before_done` / `/flo -v` / `/flo -sd`). Use before `gh pr create` when the change must be proven to work, not just tested in passing. Verifies only — it does not fix; on FAIL it reports the gaps and stops.

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This skill should be used when the user gives a SaaS product's landing page URL, homepage, or name and asks for a full teardown or deep-dive of how the product works across web, desktop, and mobile — including per-platform feature inventories and user journeys, tech stack, pricing/plan mapping, how the platforms integrate and sync (and the business logic behind those choices), hardware integrations, business-scale/revenue signals, and concrete release artifacts per platform — with a structured Markdown report as the deliverable. Trigger on phrases like "research this SaaS", "do a platform teardown of X", "map every feature of this product across web/mobile/desktop", "reverse-engineer how this app works everywhere", or a bare URL plus a request to understand the whole product.

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。

Fact-forcing gate that blocks Edit/Write/Bash (including MultiEdit) and demands concrete investigation (importers, data schemas, user instruction) before allowing the action. Measurably improves output quality by +2.25 points vs ungated agents.

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。

Develop an independent visual language and turn the human-chosen direction into a production-ready graphic, brand, campaign, publication, web, or product system. Use when originality and differentiation matter enough to escape generic AI and Big Tech aesthetics. Guides a minimum-evidence start, ten isolated typography-led HTML design directions made only from type, color, and space, visual-first human judgement, explicit commitment, and concrete Round 2 production. Do not use for routine UI fixes, small components, or applying an existing design system.

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。

Use when a bounded Yardlet task needs a concrete implementation plan before code changes

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。

Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.

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。

Root-causes one specific mistake the agent just made, such as not committing despite instructions to, by checking it against the loaded AGENTS.md, CLAUDE.md, skills, and Beads memories, delegating the investigation to a separate agent where the harness supports one; proposes concrete context improvements, and lets the user choose which to apply and how to remediate. Use when the user invokes `/learn` or `$learn`, with or without a description of the mistake, including `/learn /qs`-style shorthand naming the corrective command. For a broad end-of-session documentation sweep, use `documentation-updates` (`/reflect`) instead.

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。

Re-asks the most recent question through the agent's interactive questionnaire tool instead of plain text, and keeps using that tool for every later question with concrete options. Use when the user invokes `/qs` or `$qs`, or asks for a question to be re-posed as selectable choices.

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 friction you just hit into a concrete change to the workflow, so the same problem doesn't recur.

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。

Cross-consistency review of a drafted spec's diagrams. Verifies that C4 components appear in the dependency graph, class-diagram changes have matching DDL, every AC resolves to a concrete sequence, and the dependency graph is acyclic. Read-only. Run after `/spec-lint` passes and before implementation.

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Workflow Phase 3 — Research and Solution Exploration. Surfaces 2–4 candidate solution approaches with concrete tradeoffs, grounded in current library docs (fetched through the provider named in `.claude/docs-provider.json`) — never in training-data recall. Output lives at `docs/research/<slug>.md`. Candidate ranking and the memo execute in main context; doc/source gathering MAY be delegated to read-only advisory subagents (seed.md §4.2-A).

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。

Review proposed abstractions, dependencies, and configuration for concrete consumers and maintenance cost when simplifying a design or checking overengineering.

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。

Design deployment-focused distillation systems that balance model size, accuracy, calibration, and cascade escalation under real resource limits. Best for teacher-student compression, threshold design, and failure-aware deployment. Activate on "model compression", "teacher- student", "distillation score", "cascade model", "edge deployment", or "model calibration". NOT for generic deep-learning overviews, prompt optimization, or training work without a concrete distillation objective.

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。

What you can build and do with an always-on AI agent that has episodic memory. Covers concrete product ideas, workflows, emergent capabilities from persistence plus memory, and real-world examples of deployed persistent agents. Helps you go from "I have the architecture" to "here's what it actually does for me." Activate on: "what can an always-on agent do", "persistent agent use cases", "agent applications", "proactive agent ideas", "what to build with episodic memory", "always-on agent product", "personal AI assistant ideas", "/always-on-agent-applications". NOT for: building the architecture (use always-on-agent-architecture), designing inputs (use always-on-agent-inputs), safety and privacy (use always-on-agent-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。

Architecture and systems design for building always-on AI agents with episodic memory. Covers the memory hierarchy (core/recall/archival), persistence layers, agent server infrastructure, vector stores, and framework selection. Provides concrete deployment patterns for agents that maintain identity and learn across sessions. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-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。

Use for tradeoff analysis, or when the user must pick between concrete options: "A or B?", "which library should we use?", "build vs buy", "compare these approaches". Scores options on weighted criteria, tests sensitivity and reversibility. Skip for open ideation or single-option reviews.

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。

Domain-Driven Design system for software development. Use when designing new systems with DDD principles, refactoring existing codebases toward DDD, generating code scaffolding (entities, aggregates, repositories, domain events), facilitating Event Storming sessions, creating bounded context maps, or performing code reviews with a DDD lens. Covers both strategic design (bounded contexts, subdomains, context maps, ubiquitous language) and tactical design (entities, value objects, aggregates, domain services, repositories). Supports all major architecture patterns (Hexagonal/Ports & Adapters, CQRS, Event Sourcing, Clean Architecture) with language-agnostic guidance and concrete examples in Python and TypeScript.

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。

Harness-first workflow for evaluating and improving terminal UIs through real interaction. Use this whenever the user wants to review a TUI experience, compare before/after behavior, identify UX friction, validate navigation or filtering flows, inspect visual stability, or turn observed interaction problems into concrete follow-up tests and improvement recommendations. Prefer this skill any time a TUI should be exercised through a reproducible PTY/session harness instead of only reading code.

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Before improving anything, show the spread of what can concretely be done on this specific input — a short menu of 3-5 interventions from lightest to heaviest touch, then wait for the user to pick. Domain-agnostic; works on any material the user hands over. Trigger when the user says "spread", "show me my options", "what can you do with this", or asks to improve or fix something without specifying how. Do NOT trigger when the user has already named a specific fix — then just do the fix.

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。

At the close of an epic (or a difficult issue), post a postmortem roundup comment — what went well, what was hard (backed by evidence), and 1–3 concrete proposals to improve the skills/flows — then offer to turn accepted proposals into their own `workflow`-labeled tasks, and end with a `🔭 Next` handoff (a paste-ready next-session prompt + the next epic to start). Use at epic close, after the "verify the whole thing" rollup, or when asked to "do a retro", "postmortem this epic", "what did we learn", "how do we improve the workflow".

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。