「ept」の検索結果

Freshly judge whether a finished change and its claims meet original acceptance: PASS, FAIL or NOT_PROVEN. Use when: asked for a go/no-go, sign-off or independent verdict.

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Generate images using Google Gemini's image generation capabilities. Use this skill when the user needs to create, generate, or produce images for any purpose including UI mockups, icons, illustrations, diagrams, concept art, placeholder images, or visual representations.

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Implement authentication and authorization using @delon/auth. Use this skill when adding login/logout flows, JWT token management, role-based access control (RBAC), route guards, HTTP interceptors, and session management. Integrates with Firebase Auth and custom permission systems. Ensures secure token storage, automatic token refresh, and consistent authorization checks across components and services.

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Implement caching strategies using @delon/cache. Use this skill when adding memory cache, LocalStorage cache, SessionStorage cache, or cache interceptors for HTTP requests. Supports TTL-based expiration, cache invalidation, cache grouping, and persistent storage. Optimizes performance by reducing redundant API calls and database queries.

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Use when you need to run a binary, trace execution, or observe runtime behavior. Runtime analysis via QEMU emulation, GDB debugging, and Frida hooking - syscall tracing (strace), breakpoints, memory inspection, function interception. Keywords - "run binary", "execute", "debug", "trace syscalls", "set breakpoint", "qemu", "gdb", "frida", "strace", "watch memory"

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Character consistency across AI-generated images with reference sheets and LoRA techniques. Covers turnaround views, expression sheets, color palettes, and style consistency tricks. Use for: character design, game art, illustration, animation, comics, visual novels. Triggers: character design, character sheet, character consistency, character reference, turnaround sheet, expression sheet, character art, consistent character, character concept, reference sheet, character creation, oc design, character bible

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Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image

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Knowledge and utilities for creating animated GIFs optimized for Slack. Provides constraints, validation tools, and animation concepts. Use when users request animated GIFs for Slack like "make me a GIF of X doing Y for Slack."

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Implement SAFe methodology in Jira. Use when creating Epics, Features, Stories with proper hierarchy, acceptance criteria, and parent-child linking.

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Lens — Add a cognitive lens to any problem. It accepts a task description and produces an enhanced description that surfaces hidden dimensions, prerequisites, and lines of inquiry—the things you do not know you do not know. Use when the user asks to brainstorm, analyze, generate a skill, distill, or fuse, or when the input is too simple and needs to be expanded.

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SkillAlchemy — One thought conceived, one goal achieved. Accept any idea or distillation target and produce an installable SKILL.md. It uses Lens to clarify the problem and LEAP to run distillation or fusion. This is the sole user-facing entry point. Use when the user asks to distill, generate a skill, fuse skills, or says, "I want to build X, but I do not know where to start."

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 and maintain a Markdown guide that helps humans understand a repository through real behavior, concepts, and evidence. Use when a user asks to understand a repo, generate or update repo-docs, answer repo-architecture/onboarding questions, seed docs for a new project, sync docs after code changes, or delete generated repo docs.

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Apply the NIST AI Risk Management Framework (AI RMF 1.0) and adjacent guidance to AI / ML systems — model lifecycle governance, fairness and bias evaluation, robustness, transparency, accountability, third-party model risk, monitoring for drift, and AI incident response. Broader than prompt-injection (which is the security slice). Use when the user mentions 'AI risk,' 'AI governance,' 'NIST AI RMF,' 'AI compliance,' 'ML governance,' 'model risk management,' 'AI fairness,' 'AI bias,' 'algorithmic accountability,' 'AI Bill of Rights,' 'EU AI Act,' 'AI transparency,' 'model card,' 'AI red team,' 'AI safety,' 'responsible AI,' 'model drift,' 'concept drift,' 'AI monitoring,' 'AI incident,' or needs to assess or govern an AI / ML system.

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Same-epoch comparison of training runs across wandb, neptune, tensorboard, or mlflow. Aligns runs at the student's current step (never current-vs-final-of-baseline) and separates proxy metrics from downstream targets. Use when the user asks to compare runs, check if a run is improving, track lag against a baseline, rank experiments, or evaluate run-vs-run performance.

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Execution cost estimation, slippage curve modeling, and optimal trade sizing based on AMM liquidity depth

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DEX liquidity depth assessment, slippage estimation, and pool composition analysis for Solana tokens

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Traditional market microstructure concepts applied to crypto — order book dynamics, market making theory, price formation models, execution quality measurement, and CEX vs DEX structural differences

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Create comprehensive GitHub issues with TDD plans, acceptance criteria, and agent instructions for autonomous PR lifecycle management

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Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.

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。

Implement evaluated findings: load code-style rules, apply the accepted fixes, then close out the change with full or quick QA. Use after $evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "apply evaluated findings", or "dispatch findings to 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。

Implement evaluated findings: load code-style rules, apply the accepted fixes, then close out the change with full or quick QA. Use after /evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "apply evaluated findings", or "dispatch findings to 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。

Capture an out-of-scope improvement opportunity so it doesn't get lost. Use when the user asks to "note improvement", "save improvement", "track this for later", "remember this improvement", "note this idea", "log improvement", "backlog this", or "park this idea". Also invoke proactively when noticing something improvable during work that falls outside the current task's scope, or after deliberately shipping a simpler approach that accepts a known ceiling — briefly mention it to the user and offer to note it.

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 multi step work the way a careful senior engineer would. Triage before code, a short PRD in prds/, task.md kept current, docs synced after, and lessons recorded when a mistake repeats. Use when a task spans several files or steps, changes architecture, needs a plan the user can review, or when the user says "write a PRD", "plan this", "track this in task.md", or "what did we learn".

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。

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

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。