Results for “build”

Scaffold new babysitter process definitions following SDK patterns, proper structure, and best practices. Guides the 3-phase workflow from research to implementation.

Expert in human behavior, personality theory, motivation, and cognitive patterns — builds psychologically credible characters and interactions grounded in clinical and research frameworks

Build and maintain a research repository that makes findings findable, reusable, and cumulative across the organization.

Builds a personal knowledge base for capturing and retrieving understanding, with structured namespaces for concepts, tools, patterns, and references.

This skill should be used when building market data displays, implementing price feeds, or adding technical analysis. Covers prices, market caps, technical analysis, sentiment, and trending tokens.

Interviews users about their email patterns, business context, and priorities to build a structured knowledge base that powers personalized inbox triage.

Build and maintain a persistent, interlinked markdown knowledge base that compiles knowledge once and keeps it current, with cross-references and contradiction flags.

Use when a research proposal needs work packages, milestones, deliverables, a timeline, a risk register, and budget-justification scaffolding, while keeping scientific claims and feasibility honest and human-owned.

When the user wants to find, qualify, and build a list of prospects to reach out to — across B2B SaaS, general B2B, or local small businesses. Also use when the user mentions "prospecting," "build a prospect list," "find prospects," "find leads," "lead gen list," "find SaaS companies that," "find B2B companies," "find local businesses," "ICP-fit accounts," "who should we go after," "outbound list," "target account list," "find clients near me," "businesses without websites," "prospect research," "qualified leads," "find my first customers," "early adopters," "design partners," "beta users," or "who has this problem." Use this for the list-building and qualification phase. For writing the outbound copy after the list is built, see cold-email. For deep competitive research on specific accounts, see competitor-profiling.

Filters top Hacker News stories by interest tags and extracts high-signal comments (top, dissenting, expert/builder) with themed clustering.

Orchestrate token-heavy research, coding, and testing by delegating bounded tasks to cheaper subagents while reserving Claude Fable for architecture, synthesis, and final review.

Build RAG pipelines that combine web search and LLMs for research, fact-checking, and grounded responses using the inference.sh CLI.

Builds and maintains a persistent, interlinked Obsidian knowledge base by incrementally ingesting sources, updating entity/concept pages, and keeping cross-references current.

Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.

Build and train graph neural networks for drug discovery, protein modeling, and molecular science using PyTorch-native tools.

Build structured knowledge graphs from unstructured text by extracting entities, mapping relationships, generating graph triples, and visualizing the result. Use when the user requests knowledge graph creation or provides relevant inputs for this workflow.

Build work collaboratively in reviewable pieces, handing each piece back for review before starting the next, so the person stays in the lead and steers while the work happens instead of reviewing a finished result. Use when someone says to pair with them on something, asks to collaborate rather than direct, wants to review as it goes, or wants to guide the work piece by piece — on code, on a design decision, or on writing. For a test-first build it runs tdd, for restructuring it runs refactor, for an interface contract it runs design-an-api, and for plan work it runs iterative-plan-review or plan-implementation, each collaboratively; invoke any of those directly instead to run it straight through without pausing. Does not pace someone through code that already exists and builds nothing — use code-walkthrough. Does not explain, summarize, or research something instead of producing it — use code-overview or research.

Explains the advantages of building on Ethereum, including ERC-8004 identity, x402 payments, composability, and permissionless deployment, while correcting common misconceptions about gas costs and network status.

Train unsupervised machine learning models on genomic interval data from BED files, including region embeddings, single-cell ATAC-seq analysis, and consensus peak building.

Builds systematic factor-based investment strategies following AQR Capital Management's methodology, covering factor construction, portfolio optimization, and risk management.

Builds reliable browser automation skills by iteratively running a browsing task, reading the trace, and improving the navigation strategy until it passes consistently.

Orients researchers to an unfamiliar academic field by searching Consensus, building a strategic search plan with PICO or other frameworks, and synthesizing findings into a formatted Word document.

Classify medical devices under EU MDR 2017/745, build technical documentation, plan clinical evaluations, and manage post-market surveillance and EUDAMED integration.

Build, measure, and evolve company culture as operational behavior through workshops, values-to-behaviors translation, culture code creation, health assessments, and stage-appropriate rituals.

Forces web searches and reading of official docs before implementing, integrating, or debugging third-party APIs, libraries, and services to ensure accuracy and avoid guesswork.

Build qualified prospect lists for B2B SaaS, general B2B, or local small businesses by defining an ICP, sourcing candidates, qualifying with evidence, scoring, and outputting a lead sheet.

Run an AI Lab division meeting with research and build agents (no AI Startup agents). Use when the user requests to "start a lab meeting", "convene the lab", or "run a sprint retrospective for the lab".

Design agents that watch data sources, build candidate prediction-market baskets, draft parameter changes, and hand results to a human for review.

Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots to keep product decisions grounded in evidence.

Build and manage company research profiles in a personal knowledge base, tracking fundamentals, valuation bands, red flags, and events with auto-refresh.