「yc」の検索結果

Vehicle/automotive image editing for cars, trucks, SUVs, motorcycles — car scenes, reflections, tires refinement with snow/mud/grass, segment windshield/wheels/body/windows/hubcaps, atmospheric effects (dust, fog, snow, light leaks, lens flare), and lighting harmonization (hot-day, cold-day, hot-night, cold-night presets). Powered by Bria.ai's dedicated automotive pipeline. Use this skill whenever the user mentions a car, vehicle, truck, SUV, motorcycle, auto photo, car reflections, tire enhancement, muddy tires, vehicle background, automotive marketing, car dealership visual, vehicle configurator, or car scene generation. ALWAYS prefer this skill over general image tools when the subject is a vehicle — faster and more accurate for automotive workflows.

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。

Decide which plugin component type to use and how to organize components at scale. Covers the component lifecycle (discovery and activation phases), decision framework for choosing between commands, skills, agents, hooks, and MCP servers, and organization patterns for each component type. Use when asking "which component type should I use", "command vs skill vs agent", "when to use a hook vs MCP server", "component lifecycle", "how to organize plugin components", "plugin structure patterns", or "scale a plugin with many components".

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。

Orchestrate the full plugin development lifecycle from blank canvas to marketplace-ready. Use when creating a new plugin, improving an existing plugin, fixing validation errors, or taking a plugin through assessment, research, design, creation, debugging, optimization, and verification. Complements /plugin-creator:plugin-creator which provides the detailed new-plugin creation workflow with discussion capture, parallel research, and atomic implementation.

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 skill lifecycle by tracing call chains, detecting circular dependencies, finding instruction contradictions, identifying duplicated datasets, analyzing bidirectional coherence, discovering scriptable sequences, and learning patterns. Use when checking skill coherence, validating skill workflow, finding semantic gaps in plugin structure, or auditing plugin before marketplace submission. Generates audit reports to .plugin-creator/audits/ with findings by dimension.

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。

Core marketing concepts, psychology, and frameworks for effective marketing. Use for foundational knowledge on funnels, customer journey, positioning, value propositions, and marketing psychology.

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 the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle emails." For in-app onboarding, see onboarding-cro.

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。

Psychrometric Analysis Agent

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。

Manage agent fleet through CRUD operations and lifecycle patterns. Use when creating, commanding, monitoring, or deleting agents in multi-agent systems, or implementing proper resource cleanup.

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。

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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。

VNX-specific domain expertise for the intelligence system, central state databases, and dispatch lifecycle. Use when working on quality_intelligence.db / runtime_coordination.db / dispatch_tracker.db, code_snippets FTS5, snippet_metadata linkage, success_patterns / antipatterns, T0 state projection (t0_state.json), intelligence_injections lifecycle, dispatch → receipt → events → archive flow, central state architecture, project_id propagation across VNX, or any code that reads/writes VNX governance state. Triggers include: editing intelligence injection code, querying central DB for cross-project insights, debugging "intelligence not appearing in T0 state", modifying schema in `schemas/quality_intelligence.sql` or `schemas/runtime_coordination_v*.sql`, working with `scripts/quality_db_init.py` or `scripts/lib/coordination_db.py`.

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。

Read and manage Linear issues, projects, cycles, teams, comments, and labels. Use this whenever the user wants to list their issues, search issues, create or update an issue, move an issue between states, add a comment, check a project or cycle, look up a team, or ask "what's on my plate in Linear" — even if they don't say "API" or "GraphQL". Also use it for any linear.app URL or an issue identifier like "ENG-123". Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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。

Plan and batch-produce an entire email system for a product. Maps every lifecycle touchpoint (onboarding, activation, conversion, retention, transactional, win-back), generates all emails with quality gates, outputs Loops-ready copy. Use when "create all emails", "email system", "build email sequences", "lifecycle emails", "onboarding sequence", "set up transactional emails", "plan all the emails for [product]", or any request to systematically produce a complete set of emails for a platform.

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。

Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for IPA runtime analysis, Frida hooks, Objective-C or Swift method tracing, Keychain inspection, SSL pinning bypass, URL scheme handling, and iOS request-signing recovery. Use when the user asks to hook an IPA, trace Objective-C or Swift runtime behavior, inspect Keychain or plist state, bypass pinning, analyze deeplinks or universal links, or replay accepted iOS requests. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。

Expert Architecture Decision Record (ADR) creation and lifecycle management based on Olaf Zimmermann's methodology. Use when creating ADRs, reviewing architectural decisions, evaluating decision readiness, writing MADR templates, assessing decision quality, or managing ADR logs. Covers the full lifecycle from readiness (START criteria) through creation, MADR formatting, completion (ECADR criteria), and ongoing maintenance.

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。

Clone un site web à l'identique à partir d'une URL : capture Playwright complète (screens full-page + fold par fold sur desktop/tablet/mobile, crops par section, états hover), extraction de tout ce que le navigateur expose (fonts, variables CSS, palette, typo, layout, breakpoints, animations, assets, console/network), reconstruction section par section, puis boucle de vérification par diff pixel et console propre. Utilise ce skill DÈS QUE l'utilisateur veut copier, cloner, répliquer, reproduire, « refaire pareil », s'inspirer fortement d'un site, d'une landing page, d'un template ou d'une page précise (« fais-moi la même chose que X », « copie ce site », « reproduis cette landing », « clone neverboring.ai », « je veux exactement ce design »), même s'il ne dit pas « clone ». Aussi pour auditer visuellement un site existant (screens complets, design tokens, fonts utilisées).

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。

Evaluate user-facing interfaces against Nielsen's 10 Usability Heuristics. Complements /mycelium:service-check (Downe = service-level quality, Nielsen = interface-level quality).

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。

Smoke-test skill that confirms the Mycelium plugin loaded correctly. Returns a deterministic marker string for plugin-shape validation. Not for end-user invocation in normal use.

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。

Bring Mycelium into a project that already has code. Detects that the repo predates the framework, asks before touching anything, then reads the codebase to draft what it CAN establish (delivery, solution shape) and — the actual point — names what it cannot (purpose, strategy, real user evidence). The output is a discovery backlog with a head start, never a filled canvas.

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。

Measure Mycelium's own scaffold token cost (CLAUDE.md + engine + harness + canvas + memory) and surface a structured estimate. One-shot audit; pair with /framework-health for trend tracking.

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。

Render `.claude/diamonds/active.yml` as the decisions each diamond has recorded, in order, grouped by mode (discovery, delivery). Read-only. Default format Mermaid stateDiagram-v2, drawn by a script. Recommended at the end of `/mycelium:diamond-assess` so every assessment closes with a visual state-of-play. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared render fleet conventions.

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。

Draft a short, honest account of where using Mycelium was confusing or slow, for the user to edit and choose whether to send. Reports experience; never prescribes fixes. Based on Hoskins's friction logging.

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。

Run eval scenarios to benchmark Mycelium effectiveness. Execute tasks using reflexion loop, validate against success criteria, record metrics.

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。

Record findings from completed offline human tasks (interviews, observations, outreach) back into the canvas. The re-entry point after /mycelium:handoff.

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 real user interviews aren't possible (solo/hobby/dogfood projects) but persona work is still needed. Enforces epistemic discipline: adversarial spectrum, pre-committed stop conditions, speculation tagging. NOT a substitute for /mycelium:user-interview when real users are available.

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。