「construction」の検索結果

Use Google Lyria music generation models through Google Cloud / Vertex AI / Gemini Enterprise Agent Platform for production music beds, songs, vocal tracks, image-conditioned music, short-form social audio, ad music, and video scoring. Covers Lyria 2, Lyria 3 Clip, and Lyria 3 Pro model selection, prompt construction, rights/privacy checks, SynthID/C2PA provenance, artifact custody, and QA.

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Direct ByteDance Dreamina Seedance 2.0 Standard, Fast, and Mini video production across BytePlus ModelArk and verified gateways. Use for text/image/reference-to-video, native synchronized audio and dialogue, multimodal image-video-audio reference, first/last-frame animation, video editing or extension, model/gateway selection, prompt construction, async task handling, failure recovery, QA, and rights-safe production.

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Use when an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.

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Design and produce raster images, native SVG artwork, brand-controlled visuals, and documented image edits with Recraft's API. Use for Recraft model and route selection, exact request construction, image-to-image, inpainting, outpainting, background work, vectorization, remix/exploration, palette and typography control, API migration, safety and rights review, and production QA.

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Use this skill for provider-independent reality capture that turns photographed or video-derived real places, objects, aerial sites, interiors, or people into photogrammetric meshes, NeRFs, or 3D Gaussian splats. It guides representation choice, capture planning, calibration and scale, reconstruction or training, cleanup, compression and LOD, coordinate and interchange handoff, visual/geometric QA, archival records, and property, location, people, and cultural rights checks. Do not use it for text-to-3D generation, synthetic asset invention, guaranteed metrology, or broad downstream finishing after handoff.

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Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, generating bootstrap consensus, or needing quick exploratory phylogenies before running full ML analysis.

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Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.

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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.

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Standard scRNA-seq preprocessing and clustering with Scanpy. Use for QC, normalization, HVG selection, PCA, neighbor graph construction, UMAP, Leiden clustering, and export of an analysis-ready AnnData object.

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Workflow for orthology, synteny, ancestral reconstruction, and evolutionary comparison across genomes.

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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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Generates a step-by-step construction plan from a one-line objective, where each step is self-contained and executable by a fresh agent in a new session.

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Construct a high-conversion offer using Alex Hormozi's value equation and Grand Slam Offer mechanics during PRD v0.9 Go-to-Market. Triggers on requests to design the offer, set up the pitch, build bonuses or guarantees, or when user asks "how do we package this?", "what's the offer?", "build a grand slam offer", "Hormozi", "$100M offers", "guarantee", "bonus stack". Outputs GTM-* entries with Type=Offer / Type=Guarantee and BR-PRICING-* updates.

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Orchestrate go-to-market strategy by running positioning, offer construction, and channel allocation as a sequenced workflow during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "GTM", "launch this product". Chains three framework-grounded sub-skills (Dunford → Hormozi → ORB) and reconciles their outputs.

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Reconstructs the workflow behind a past artifact by querying local ax session, recall, and commit history data. Use when a user asks "what made X work", "how was Y shipped/built", "extract workflow from <date|sha>", "workflow around <topic>", "recipe for this feature", or similar. It finds the anchor session or commit, inspects ax sessions, ax recall, and ax sessions show, and returns an ordered read-only narrative with decisions and evidence.

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Use when extracting structured information from text - named entity recognition, relation extraction, coreference resolution, knowledge graph construction, and information extraction pipelinesUse when ", " mentioned.

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Expert in creating rich backstories, fictional universes, and ARGs (Alternate Reality Games). Covers world-building, mystery construction, community-driven storytelling, and transmedia narratives. Knows how to create depth that rewards exploration without overwhelming. Use when "lore, world building, backstory, ARG, mythology, fictional universe, hidden story, deep canon, " mentioned.

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Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned.

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Raising money is a means to an end, not the end itself. Great companies can be built with or without venture capital. But if you're raising, you need to understand the game: when to raise, how much, from whom, and at what terms. This skill covers fundraising timing, process, pitch construction, term sheet negotiation, and investor relationship management. It's not about raising as much as possible - it's about raising strategically. Use when "fundraising, raising money, pitch deck, investors, term sheet, valuation, dilution, series A, seed round, pre-seed, runway, cap table, VC, venture capital, fundraising, investors, venture-capital, pitch-deck, term-sheet, valuation, dilution, seed, series-a, cap-table" mentioned.

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Sentence-level and section-level craft for instructions Claude will actually follow, in any container — project instructions, a standalone prompt, or skill body text. Use when the text exists or is being drafted and the question is quality: "why is Claude ignoring this instruction", "rewrite this prompt so it works", "improve my project instructions", "make this instruction stick", "is this too prescriptive", "how much structure does Sonnet need versus Opus". Covers imperative construction, positive framing, strategic-versus-procedural calibration per model, extended thinking, complexity scaling, and a quality checklist. For deciding WHICH container the instruction belongs in, use crafting-instructions. For building, testing and packaging a whole skill directory, use creating-skill.

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Load at the start of any task whose deliverable is prose another person will read — a PR description, commit message, README, doc, postmortem, blog post, issue or review comment, report, release note or essay — before drafting it, whether the result is pushed, posted, published or saved to a file, without being asked. Draft, then run this pass on the draft before handing it over. Also use when someone says "de-claude", "de-slop", "humanize this", "this reads like AI", "make it sound human", or asks for a voice, tone or register edit. Rewrites the constructions that mark prose as model-written (staged reveals, verdict headers, aphoristic closers, "it's not X, it's Y", em-dash drama, forced triads, flat-certainty adverbs) into plain technical prose and checks the rewrite kept every claim. Not for fiction, poetry, code, or quoted text; for a full adversarial review of a deliverable use challenging.

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Analyze images, websites, and Figma files to extract their design and generate a `design.md` with token system, component inventory, and reconstruction notes. Use this skill whenever the user wants to understand, document, replicate, or audit the design of something visual: a screenshot, a URL, a Figma link, a Pinterest reference, a mockup, a competitor's site, a component, a dashboard, a landing page. Also when they ask 'extract the design system from X', 'document the style of Y', 'analyze this visually', 'convert this image into tokens', 'help me replicate this design', 'what palette does this site use', 'how is this built'. Also for single elements: 'copy this navbar', 'recreate this illustration', 'give me a prompt to regenerate this graphic' — element mode outputs a focused element.md, with token-grounded image-model prompts when the element is visual art. If the user brings any visual source and wants to understand it at a design level — this skill should activate.

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Applies the existentialist and feminist philosophy of Simone de Beauvoir, French philosopher and author of The Second Sex. Reach for this skill whenever the user is grappling with existential dilemmas, moral ambiguity, gender dynamics, societal expectations, or relationship ethics. Trigger this skill for questions about freedom versus responsibility, bodily autonomy, marginalization, or when the user feels trapped by external labels or "immanence." Use this to help the user reclaim their agency, recognize the social construction of their situation, and make authentic choices that expand their transcendence while respecting the interconnected freedom of others.

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Applies the foundationalist reasoning and methodological doubt of René Descartes (17th-century French rationalist philosopher). Use this skill whenever the user is questioning fundamental assumptions, seeking absolute certainty in a decision, dismantling flawed systems, or trying to rebuild a project from first principles. Trigger this skill for topics involving epistemology, rationalism, radical doubt, systemic reconstruction, or separating objective truth from sensory illusion. Reach for this whenever a user needs to cut through probabilities and consensus to find an indubitable 'Archimedean point' to build upon.

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。