'sharpening' 검색 결과

Maintain the 8 essential garden hand tools through sharpening, handle care, rust prevention, and seasonal storage. Covers bypass secateurs, hori-hori, hand fork, trowel, pruning saw, sharpening stone, watering can, and soil rake. Use after each garden session for quick cleaning, monthly during the growing season for sharpening and oiling, at end of season for winter storage preparation, before spring for pre-season readiness checks, or whenever a tool feels dull, stiff, or shows rust.

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계획이나 설계를 다시 검토하도록 깊이 있는 질문을 이어 가는 인터뷰를 시작합니다. 관련 grilling 스킬을 호출해 대화로 생각을 구체화하도록 돕습니다.

• 질문을 통해 계획 다시 검토하기
• 인터뷰로 설계에 관한 생각 살펴보기
• 대화로 초기 아이디어 구체화하기

계획이나 설계를 깊이 묻는 질문으로 구체화하면서 설계 결정 기록과 용어집도 작성합니다. 하나의 대화에서 검토와 문서화를 함께 진행할 때 사용합니다.

• 질문에 답하며 계획 구체화
• 대화로 설계 심층 검토
• 검토 중 설계 결정 기록

프레젠테이션과 문서에 넣을 이미지·스크린샷의 해상도, 선명도, 명료도를 개선합니다. 실행하려면 원본 배포처의 관련 파일 묶음을 설치해야 합니다.

• 프레젠테이션용 이미지 정리
• 문서용 스크린샷 명료도 개선
• 이미지 해상도 높이기

Improve screenshots and digital images by increasing resolution, sharpening details, and reducing noise and compression artifacts. Prepare images for presentations, documentation, blogs, or social posts, with folder-wide batch processing and backups of the originals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving screenshots for blogs and documentation
• Upscaling images for presentations
• Preparing images for social media

Review website JPEGs for progressive encoding, which displays a blurry preview before sharpening. Identify images that could use this format, consider file size and loading behavior together, and explain how to confirm the change in browser developer tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing how JPEGs appear during loading
• Checking JPEG fallbacks for WebP or AVIF
• Reviewing image delivery through a CDN or build process

Improve screenshots and digital images by increasing resolution, sharpening details, and reducing noise and compression artifacts. Prepare images for presentations, documentation, blogs, or social posts, with folder-wide batch processing and backups of the originals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving screenshots for blogs and documentation
• Upscaling images for presentations
• Preparing images for social media

Improve mildly blurry, dark, or noisy images with noise reduction, sharpening, and contrast or saturation adjustments. Export enhanced images at their original size or enlarged two or four times, with a report of applied changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Enlarge an image two or four times
• Reduce noise in a photograph
• Adjust contrast and color in dark or dull images

SUPERSEDED by /claudex-loop (formerly /crucible; its docs-aware mode covers this variant) — prefer that skill unless you explicitly want this one. Two-act plan hardening with living documentation. ACT 1 (you ↔ Claude) — Claude interviews you relentlessly about a plan, one question at a time, challenging it against your project's existing domain model and glossary (CONTEXT.md), sharpening fuzzy terms, stress-testing with concrete scenarios, cross-referencing code, and updating CONTEXT.md + ADRs inline as decisions crystallise. ACT 2 (Claude ↔ Codex) — Claude writes the locked plan to PLAN.md and OpenAI Codex adversarially reviews it in a read-only sandbox (VERDICT:APPROVED/REVISE), Claude revises and re-submits to the SAME Codex session until APPROVED or a MAX_ROUNDS cap, then you sign off before any code. Use when the user says "/grill-with-docs-codex", "grill me against the docs then have codex review", "stress-test this against our domain model then get a second model on it", or is about to build something high-stakes in a project with established terminology/ADRs and wants alignment, documentation, AND a cross-model sanity check. Builds on Matt Pocock's grill-with-docs (MIT). NOT for reviewing already-written code (use /codex:review) and NOT for trivial changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when deciding whether a project is a strong AISTATS fit, comparing AISTATS with NeurIPS, ICML, ICLR, UAI, COLT, JMLR, statistics journals, or application venues, identifying the statistical primitive of the contribution, and sharpening the AI-statistics framing before writing begins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when deciding whether a project fits AEJ: Economic Policy rather than J. Public Economics, AEJ: Applied, or AER, and when sharpening the policy question and its welfare stake for an AEJ: Economic Policy manuscript. Frames fit and the policy-question-first contribution; it does not design the identification or run estimation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when placing the contribution against the policy-evaluation literature and sharpening it as a policy answer for an AEJ: Economic Policy manuscript. Stakes the contribution and the policy-relevant "so what"; it does not write the full introduction or design the identification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when situating an Administrative Science Quarterly (ASQ) manuscript within organization-theory conversations and sharpening the gap/tension it addresses. Positions the argument; it does not develop the core theory or write the methods.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when deciding whether a security project fits ACM CCS versus IEEE S&P, USENIX Security, NDSS, PETS, or a crypto/theory venue, identifying the security contribution type, and sharpening the threat model and attacker capability before writing begins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when sharpening the theoretical contribution and the "so what" for an Administrative Science Quarterly (ASQ) manuscript once results exist. Frames the contribution; it does not develop the underlying theory or run analysis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when framing or sharpening a topic for 《会计研究》 (Accounting Research) — turning a generic "X affects Y" empirical idea into an accounting contribution anchored in China's institutional setting, accounting theory, and standard-setting / practice relevance. Use before literature review.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when deciding whether a project fits ACL versus EMNLP, NAACL, EACL, TACL, Computational Linguistics, COLM, or an ML venue, covering contribution typing for NLP work, long-versus-short paper choice, the annual theme track, Findings-tier expectations, and sharpening the computational-linguistics framing before writing starts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when choosing or sharpening a research question for an Accounting, Organizations and Society (AOS) manuscript — testing whether the question genuinely concerns the social, organizational, behavioral or institutional life of accounting, and picking the intellectual tradition the question demands. Screens fit; it does not build the theory (aos-theory-development).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when deciding whether a project is a strong AAMAS fit, comparing AAMAS with AAAI, IJCAI, NeurIPS, ICML, EC, and the JAAMAS journal, identifying whether the agents are truly the research object, naming the interaction primitive (solution concept, mechanism, coordination, negotiation), and sharpening the multiagent framing before writing begins.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Load this first for any task involving images, pictures, photos, scans, frames, volumes, or visual data — including reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, finding or counting objects, removing backgrounds, measuring, comparing, registering, stitching, aligning, cropping, resizing, transforming, or analyzing. Use for image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color processing, morphology, geometric transforms, deep learning for images, quality assessment, and 3-D volume processing. Also triggers when users describe image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration tasks (folder of images, characterize data, understand format). Handles microscopy (cells, particles) and general scientific imaging. Routes to sibling skills for medical imaging (DICOM, NIfTI), hyperspectral data, and visual inspection.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Work with hyperspectral and multispectral images in MATLAB. Covers reading/writing (ENVI, NITF, TIFF, Sentinel-2, Landsat, ASTER), ECOSTRESS spectral libraries, processing (calibration, atmospheric correction, denoising, sharpening, dimensionality reduction, endmember extraction, unmixing, target/anomaly detection, spectral indices, segmentation), labeling (Spectral Image Labeler app, ground truth objects, automation algorithms), and deep learning (pixel classification CNNs, unmixing autoencoders, transfer learning). Use when reading, writing, processing, analyzing, classifying, labeling, or applying deep learning to hyperspectral or multispectral images.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when positioning needs defining or sharpening — a statement built on Promise, Proof, and Path, a differentiation matrix against competitors, category entry points, a three-level message hierarchy, and tagline directions. Trigger on 'positioning', 'positioning statement', 'what makes us different', 'USP', 'why should customers choose us over them', 'we sound the same as everyone else'. Also use when the user can list features but not a reason to pick them. Not for — competitor teardown research, see `08-competitor-research-global`; how the brand sounds in writing, see `35-brand-voice-global`; the offer and its value stack, see `31-offer-design-global`; the launch built on it, see `59-go-to-market-global`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when enhancing, upscaling, restoring, denoising, sharpening, deinterlacing, stabilizing, motion-deblurring, colorizing, SDR-to-HDR converting, or frame-rate/slow-motion converting video (and secondarily images) with Topaz Labs — whether cleaning up AI-generated video, compressed UGC, or old archival footage, or building a generate-low-res-then-upscale delivery pipeline. Covers choosing the right named Topaz model for a specific footage problem, setting parameters, driving the Topaz Platform (Video/Image) REST API's async job lifecycle, judging when enhancement helps versus when it introduces artifacts (over-smoothing, temporal shimmer, face hallucination), and QA of enhanced output. Do not use for originating new video content, non-Topaz upscalers, or purely editorial cuts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provider-independent still-image post-production skill for RAW/rendered intake, nondestructive development, exposure, white balance, tone, color, ICC proofing, masking, repair, compositing, truthful retouching by genre, generative disclosure, sharpening, noise, resampling, variants, metadata, export, QA, rights, and ethical finishing. Use when an agent must plan, perform, direct, or review still photography retouching and finishing for print, web, social, archive, publication, or client delivery; do not use for image-generation APIs, video grading, or campaign-specific production strategy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.