「ml」の検索結果

Content sanitization and hidden-content detection for agent skill security. Scans markdown, HTML, and text for visually hidden but agent-readable attacks: CSS-hidden text (display:none, color:white, font-size:0, opacity:0), HTML comments with instructions, collapsible details sections, zero-width unicode, homoglyphs, misleading links, and inline HTML in markdown. Enforces mandatory sanitization before external content enters agent context. Load as part of the secure-* sequence during any repo scan or skill audit. Also load for sanitize content, check hidden text, scan markdown attacks, strip HTML, detect invisible instructions, check zero-width chars. Core principle: visibility does not equal influence — hidden content is more dangerous than visible content because agents process it but humans cannot see it.

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Assigns isolate identity at the right resolution for the question -- ANI / Mash species triage, 7-locus MLST historical comparability, cgMLST / wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR, SeqSero2 for Salmonella; SerotypeFinder for E. coli; Kaptive K/O for Klebsiella; SeroBA for pneumococcus; spa + SCCmec for S. aureus), and lineage callers (TB-Profiler / Mykrobe Coll-Napier barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas / database versions (chewBBACA vs Ridom vs EnteroBase), assigning MTBC lineage with the Napier 2020 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with explicit pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.

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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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。

Generate ASCII art diagrams using PlantUML text mode. Use when user asks to create ASCII diagrams, text-based diagrams, terminal-friendly diagrams, or mentions plantuml ascii, text diagram, ascii art diagram. Supports: Converting PlantUML diagrams to ASCII art, Creating sequence diagrams, class diagrams, flowcharts in ASCII format, Generating Unicode-enhanced ASCII art with -utxt flag

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。

Create HTML-based video compositions, animated title cards, social overlays, captioned talking-head videos, audio-reactive visuals, and shader transitions using HyperFrames. HTML is the source of truth for video. Use when the user wants a rendered MP4/WebM from an HTML composition, wants to animate text/logos/charts over media, needs captions synced to audio, wants TTS narration, or wants to convert a website into a video.

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。

Write ML papers for NeurIPS/ICML/ICLR: design→submit.

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Establish SAML 2.0 identity federation between on-premises Active Directory and Azure AD (Microsoft Entra ID) for seamless cross-domain authentication and SSO to cloud applications.

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Hunt HTML Injection — user-supplied input is rendered as raw HTML in the response without sanitisation, allowing an attacker to inject arbitrary HTML tags (but not necessarily JavaScript). Lower severity than XSS but enables phishing, UI manipulation, and credential harvesting via injected forms. Use when testing text-display surfaces (search results, profile fields, comments, error messages, feedback forms). For markup that executes JavaScript, escalate to hunt-xss.

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Kubernetesの設定ファイル作成・レビューと障害調査を支援します。稼働確認、権限、リソース制限、自動スケールの設定例と調査コマンドを参照できます。

• Kubernetesの配置設定を作りたいとき
• 稼働確認とリソース制限のレビュー
• 再起動を繰り返す原因を調べたいとき

既存コードから色・文字・余白などの共通ルールを抽出し、デザインの仕様書とプレビューを作るスキル。画面の統一感を10項目で評価し、修正箇所も示します。

• 既存コードからデザイン規則を作りたいとき
• リニューアル前の見た目の統一感確認
• スタイルを変更するPRのレビュー

機械学習・AIの研究論文を、関連文献の調査から実験設計、結果分析、執筆、投稿準備まで支援します。引用の検証や査読への対応も扱います。

• 既存のコードや結果から論文を書きたいとき
• 研究の主張を裏づける実験を計画したいとき
• 関連研究と引用を確認したいとき

A presentation system for agent-made PPTs — born for the talk, not just the template. It turns raw material into an AST (audience-state-transfer) outline with per-page visual-enhancement decisions (image / SVG diagram / video), hands a production brief to a downstream renderer (HTML-PPT skills or native PPTX), then runs a capped 3-round presentation checkup (演讲体检) on the rendered deck. It never renders slides itself. Use before generating PPT/HTML slides from raw material, and after rendering when the user says things like "给这份 deck 做演讲体检" or "PPT 渲染质检". If all you want is one beautiful template page with no outline and no checkup, a rendering skill alone is enough.

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。

Agent skill for data-ml-model - invoke with $agent-data-ml-model

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Use when reviewing templates, rendered HTML, or shared components related to Use semantic HTML elements. Validate the final browser-facing markup, not just the source framework abstraction.

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Use when auditing metadata, crawlability, structured data, or indexability related to Fix malformed HTML structure. Verify the rendered HTML and HTTP response rather than relying only on source files.

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。

Create branded architecture, architecture delta, IT current-state, flowchart, sequence, state machine, ER/data model, timeline, swimlane, quadrant, radar/spider, polar chart (polar/radial lollipop), loop/flywheel, nested, tree, org chart, layer stack, exploded axonometric, axonometric plan, Venn, pyramid/funnel, treemap and marimekko, heatmap, bar and dumbbell, waterfall, line (slopegraph, ridgeline, streamgraph, bump), Gantt and scatter charts (bubble, beeswarm), high-level, process, medallion, data flow, DP integration, DP security matrix, Sankey, fishbone, Wardley map, kanban, user journey, service blueprint, deployment, dependency graph, UML class, story map, or database schema diagrams as HTML/SVG/PNG, with .drawio, Mermaid, and .excalidraw import, plus lifecycle phase maps, block decomposition trees, and onboarding guidance.

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Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.

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。

generate, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.

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。

Start and persistently pursue a goal to babysit an SGLang pull request until selected GitHub Actions workflows pass on the latest PR head. Use when asked to monitor, babysit, retry, or fix PR CI for lint.yml, pr-test.yml, pr-test-extra.yml, AMD, or other named workflows; classify failures as PR-related versus flaky or infrastructural, auto-fix and push only small clean fixes, rerun failed jobs only up to 10 times, and ignore unselected workflows.

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Identify poisoned training data and backdoored ML models across the pipeline using IBM's Adversarial Robustness Toolbox (activation clustering, spectral signatures, trigger reconstruction), Cleanlab for label-quality issues, and supply-chain checks like weight-hash verification and safetensors enforcement. Use before training or deploying on third-party/user-contributed data or downloaded checkpoints, during ML supply-chain reviews, or when investigating model misbehavior tied to specific inputs (suspected backdoor trigger).

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Design identity governance and lifecycle (IGA) programs on platforms like SailPoint, Saviynt, or Entra ID Governance, covering joiner-mover-leaver (JML) automation, role mining, access requests, periodic recertification, and orphaned-account remediation sourced from an HR feed. Use when automating cross-system JML provisioning, remediating former-employee access, or building lifecycle processes for SOX, HIPAA, or GDPR compliance.

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Drive the AADInternals PowerShell toolkit to perform Microsoft Entra ID tenant reconnaissance, access-token acquisition across Microsoft APIs, and federation/AD FS backdoor testing (Golden SAML, T1606.002) for defensive validation. Use during an authorized Entra ID/Microsoft 365 red-team assessment to map external attack surface or verify AD FS signing certs resist Golden SAML.

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Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

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Build production ML systems with PyTorch 2.x, TensorFlow, and modern ML frameworks. Implements model serving, feature engineering, A/B testing, and monitoring.

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。