'artifacts' 검색 결과

Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Azure Container Registry SDK for Python. Use for managing container images, artifacts, and repositories.

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Quality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven clustering before analysis.

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Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

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Interactive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or choosing an annotation viewer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.

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Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.

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Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.

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Use when wrangler deploys silently fail or produce wrong artifacts, secrets upload as empty strings, custom domain DNS is not resolving, route assignment broke after rename, observability/tail logs are needed, D1/KV/R2 bindings are missing, OAuth scope errors block a command, cookies on a redirect are not attaching, or the worker exceeds CPU time. Triggers: "Tenant or user not found", "Authentication error" on wrangler d1 push, secret length is 0 after upload, route returns origin not worker, "compatibility_date too old", 1101 errors, custom_domain assignment lost on script rename. NOT for AWS Lambda / Vercel Edge / Deno Deploy (different runtimes), Cloudflare Zero Trust, or R2-specific tuning.

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Conduct Critical Decision Method (CDM) interviews to elicit expert reasoning strategies (L3 knowledge) from practitioners about specific challenging past incidents. Use when capturing expertise for skill development, preserving knowledge before practitioner departure, building post-incident learning artifacts, or investigating decisions in complex sociotechnical systems. Outputs structured incident reconstructions with cues, decisions, alternatives considered, and counterfactual analysis suitable for distillation into skills or training material.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Verifies build output integrity after code changes. Runs builds, validates artifact structure, checks for regressions in bundle size, asset references, and static export completeness. The last line of defense before a bad deploy ships.

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Verify every visual-evidence artifact (screenshot/GIF/recording) attached to a PR carries a provenance manifest binding it to real daemon-backed truth — daemon port, run id, transcript head hash, agent node id, commit, and an honest real/fixture/mock source label — and that operator-control-panel PRs cover the required set of proof states. Use when gating a PR's proof artifacts before merge, auditing whether a "LIVE" visual claim is backed by a real daemon run versus a reused or fixture-backed mock, or defining the required state-coverage set for a control-panel change. NOT for the capture technique itself — headless Playwright, `screencapture -x -l`, non-interruptive capture (use port-daddy-agent-skill's visual-evidence doctrine), designing the receipt body schema an artifact attaches to (use agent-work-receipt-designer), or the broader dogfood/Potemkin product-quality bar (use multi-agent-authoring-product-bar).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create a GitHub pull request from staged task changes or an already-committed current branch. Deliver source/config/ reference changes together with their computed source-owned `bin_artifacts` closure. PR validation routing is owned by the default-branch trusted workflow; snapshots, gamedata, and manifests are Release-derived only.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Toolkit for styling artifacts with curated themes (slides, docs, reports, HTML landing pages, and more). Use when you need consistent typography and color systems or want to generate a new theme on the fly.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Applies Anthropic brand colors and typography to artifacts that need a consistent look-and-feel. Use when brand colors, style guidelines, visual formatting, or company design standards apply.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Rebuild a degraded AI image to remove crunchy textures and repeated-edit artifacts while preserving identity, style, and composition. Use for clean remakes, not ordinary retouching or upscaling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Directs screens and proves they fit — game menus and HUD, web dashboards, single-page artifacts, store-page layouts and plugin panes. Writes the design brief (layout, hierarchy, spacing and type scales, every state, focus and hit size, a check against generic-looking output) and probes the rendered page at phone-to-desktop widths in every theme. Trigger to design or lay out a menu, HUD, dashboard, settings screen or panel, or review its mockup before the build; when a page scrolls sideways on a phone, text is clipped or a dark theme breaks; to check that a page or pane fits from 320 to 1440 px in light and dark; for UI direction before a build, even when nobody says design; or say uismith (direct, probe, fit-first audit). The probe needs optional Playwright. A critique, scored review or brand pass of a built page, palettes and type are brandscribe's; sprites and icons pixelsmith's; Godot scene proof godotsmith's; words commscribe's.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Writes, fixes and red-teams prompts — system prompts, templates, meta-prompts, agent or bot instructions — as copy-paste-ready artifacts, and picks the model tier for a prompt, task or plan's subtasks. Trigger to write, improve, debug or rewrite a prompt; which model should run this; a prompt that costs too many tokens, to cut with behavior held; or say promptwright (model, optimize — tune against test cases, slim, refresh). Not for a fan-out's units (dispatchwright), installed local models (lmstudiorunner), skills or their slim (skillwright), sourced comparisons (researchscribe), handoffs (handoffwright) or a pre-build interview (grillwright).

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Keeps the user's identity and secrets out of anything public. Trigger on what should not be published in a repo, branch, history, diff, PR text, draft or pasted file, to find a home path, email, real or client name, or secret; for a commit's email, a codename or per-repo identity; for injected instructions in skills, hooks or prompts; for artifacts Claude made; for files named like secrets; after a leak, to plan a history rewrite; or say shieldwarden (scan, plan, rewrite, land, after, policy, check, identity, signposts, refresh). Rules and hooks are gatewarden's; rotation and gh sign-in keywarden's; brand words brandscribe's; a skill's own posture skillwright's; schedules agentwright's.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create distinctive, production-grade frontend interfaces with high design quality. Generates creative, polished code that avoids generic AI aesthetics. Use when the user asks to build web components, pages, artifacts, posters, or applications, or when any design skill requires project context.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.