6,962 件 ・ 関連度順
Think and work like an expert Cryo-EM Structural Biologist. Use when a task calls for Cryo-EM Structural Biologist judgment. Reasons from vitrified specimens, CTF-modulated projections, particle heterogeneity, FSC validation, local resolution, preferred orientation, and map-model fit before making structural claims.
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。
Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.
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。
Sets up {module-name} module in a project. Use when the user requests to 'install {module-code} module', 'configure {module-name}', or 'setup {module-name}'.
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。
Sets up BMad Samples module in a project. Use when the user requests to 'install samples module', 'configure BMad Samples', or 'setup BMad Samples'.
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。
Sets up BMad Builder module in a project. Use when the user requests to 'install bmb module', 'configure BMad Builder', or 'setup BMad Builder'.
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 for Stability AI Stable Audio production work: selecting Stable Audio hosted API or open-weight models, generating or editing music, loops, sound effects, foley, beds, stingers, and sonic-branding audio from text or source audio, planning rights-safe uploads, setting model parameters, polling asynchronous jobs, and reviewing generated audio for media projects.
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 this skill to plan, direct, finish, and QA provider-independent mono or stereo 180-degree, 360-degree, spherical, spatial, wide-FOV, and immersive video. It covers format choice, capture rigs, stitching, nadir and seam management, stereoscopic comfort, attention direction, stabilization, ambisonic and spatial audio, titles, captions, accessibility, edit, color, VFX, projection metadata, Vision Pro, YouTube, headset delivery, device QA, and privacy, location, and likeness rights. Do not use it for interactive XR apps, volumetric/world-model generation, game-engine experiences, or provider-specific video generation prompts.
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。
Reverse DCF valuation, implied growth rate, market expectations analysis, reverse discounted cash flow, implied valuation assumptions, market-implied projections, DCF sanity check
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。
3-statement financial model, integrated IS BS CF, income statement projection, balance sheet forecast, cash flow statement, cross-statement balancing, financial model build, operating model, three statement model, integrated financial statements
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。
DCF valuation model, discounted cash flow, intrinsic value, WACC calculation, terminal value, free cash flow projection, equity value per share, DCF sensitivity analysis, unlevered free cash flow, present value calculation, build a DCF
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 and fix all Lattice documentation, README, docs/, PROJECT.md, GitHub issue templates, and CLAUDE.md to ensure they are fully aligned with the current skill inventory. Documentation drift is the most common source of user confusion in Lattice — a skill exists in the codebase but not in the docs, or a renamed skill leaves a stale reference in the bug report template. If you've made any change to skills/ and haven't run this, run it now. Use when the user says 'align docs', 'audit docs', 'update documentation', 'skill align', 'check docs are in sync', 'audit skill inventory', 'ensure docs are aligned', 'are the docs up to date', or 'what needs updating'. Standalone — does not call other skills.
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。
Facilitate a structured conversation to define architecture principles for a repository. Supports multiple architecture styles: clean architecture (default), hexagonal / ports & adapters, modular monolith, or custom. Produces a formal architecture document that the corresponding atom will use. Use when setting up a new project, defining architecture standards, or when the user says 'setup architecture', 'define layers', 'architecture principles', 'help me define my architecture', 'hexagonal architecture', 'modular monolith', 'ports and adapters', or 'define my architecture style'.
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。
Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
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。
Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
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。
Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.
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。
Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Proteina-Complexa" as the BioClaw-facing skill label. For sequence design after backbone generation, use proteinmpnn or solublempnn. For QC thresholds, use protein-design-qc.
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。
End-to-end protein design pipeline guide across preparation, generation, validation, and filtering. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design-tool-selection. For QC thresholds, use protein-design-qc.
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。
End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.
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。
codeck entry point. Scans local files for materials, shows pipeline dashboard with diagnostic intelligence, guides user to the next step. Use when the user says "codeck", "new deck", "make a presentation", "make a deck", "new slides", "build a presentation", "export", "speech", "script", or wants to start or continue a presentation project. /codeck is the user-facing entry; legacy sub-skills are internal modules.
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。
Turn a vague bug report into a VERIFIED minimal reproduction and then a failing regression test, agent-driven end to end. Covers extracting the implicit repro from a thin report (env, build, steps, data), the reproduce-minimize-isolate-capture loop, git bisect to find the introducing commit, building a deterministic minimal repro (fixed seeds, frozen time, stubbed network), writing the failing regression test BEFORE the fix (red) and confirming the fix flips it green, and writing repro evidence back into the ticket. Distinguishes flaky-not-reproducible from environment-specific. Use when: "reproduce this bug," "minimal reproduction," "repro steps," "find the commit that broke it," "git bisect," "make the repro deterministic," "write a failing test for this bug," "regression test for a defect," "can't reproduce this bug." Not for: Classifying/deduplicating/severity-routing existing failures without reproducing them — that is ai-bug-triage. Generating tests from specs rather than from a defect — that is ai-test-generation. Related: ai-bug-triage, ai-test-generation, test-reliability, systematic-debugging, qa-project-context.
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 a whole regression suite and prune/restructure it with evidence: per-test coverage fingerprinting, AST near-duplicate clustering, CI-history mining for never-failing and flaky tests, prune decision rules (redundant/obsolete/low-value/keep), smoke/core/extended tiering by risk and defect-detection history, and a defensible "what we deleted and why" record. Deletion is destructive — quarantine and human sign-off are mandatory. Use when: "audit the test suite," "prune redundant tests," "find duplicate tests," "which tests can we delete," "restructure into smoke/core/extended," "is this test pulling its weight," "shrink the regression suite." Not for: Judging whether an individual test is WELL-WRITTEN (smells, assertions) — that is ai-qa-review. Healing one flaky test at runtime — that is test-reliability. Bulk selector regeneration after a UI refactor — that is selector-drift-recovery. Related: ai-qa-review, coverage-analysis, test-reliability, risk-based-testing, qa-project-context.
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。
End-to-end testing of email-dependent flows — signup confirmation, password reset, magic-link login, OTP/MFA codes, and notification emails. Covers the capture-inbox decision tree (Mailpit, Mailosaur, MailSlurp, ShazaMail, Ethereal), Playwright polling without fixed sleeps, template-aware extraction of links/OTPs from the email body, deterministic per-test addresses (plus-addressing, per-inbox), subject/from/header/link assertions, and SPF/DKIM/DMARC deliverability checks as a separate suite. Use when: "test the signup confirmation email," "password reset email test," "magic-link login test," "capture OTP from email," "Mailpit," "Mailosaur," "MailSlurp," "ShazaMail," "email arrives flaky in CI," "assert email subject/from/links." Not for: Sending transactional email from your app code, or API-only contract tests of an email provider — those are api-testing / app concerns. Email HTML rendering across clients (Outlook/Gmail dark mode) is out of scope (note it as a gap; use visual-testing or Litmus). Related: playwright-automation, api-testing, test-data-management, qa-project-context.
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。
Validate that analytics and marketing tracking fire CORRECTLY: GA4/GTM dataLayer events, Meta/TikTok/LinkedIn pixels, and ad-tech tags. Covers building a tracking plan as the contract, intercepting collect-endpoint beacons and dataLayer.push in Playwright, asserting event name + params + values + timing + de-duplication, Consent Mode v2 gating, CI regression gating, and news-media events (article-view, scroll-depth, paywall). Use when: "test analytics tracking," "GA4 event test," "verify the pixel fires," "dataLayer test," "tracking plan," "Meta Pixel dedup," "scroll-depth tracking test," "gate tracking in CI." Not for: whether tracking is ALLOWED to fire under consent law (GDPR/CMP) — that is compliance-testing; this skill checks the data is CORRECT. SEO meta tags / structured data — out of scope. Related: compliance-testing, playwright-automation, api-testing, qa-project-context.
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 AI to write NEW test code from specs, PRDs, user stories, code diffs, bug reports, or OpenAPI specs. Staged pipeline: requirements extraction → risk analysis → coverage matrix → scenario generation → oracle design → test code → human review, with guardrails against hallucinated APIs and weak assertions. Use when: "generate tests from spec," "tests from PRD," "tests from user story," "auto-generate test cases," "AI write tests for me." Not for: testing AI/LLM features in your product — use ai-system-testing. Not for: auditing a pre-existing test suite you did not just generate — use ai-qa-review (Step 7 here only reviews tests THIS pipeline produced). Related: playwright-automation, unit-testing, api-testing, qa-project-context.
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。