Results for “discovery”

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `youtube-serp`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Search YouTube SERP results through AISA for video research, channel discovery, trend checking, and competitor tracking. Use when: you need ranked YouTube results for a query, optionally filtered by country or language.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `smart-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Intelligent hybrid search combining web and academic sources via AIsa Smart Search endpoint. Best when you need both web and scholarly results. Use when: the user needs web search, research, source discovery, or content extraction.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `tavily-extract`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Extract clean, readable content from one or more URLs using Tavily Extract via AIsa API. Useful for reading full articles without visiting the page. Use when: the user needs web search, research, source discovery, or content extraction.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `multi-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Parallel multi-source search combining Web, Scholar, Smart, and Tavily results with confidence scoring and AI synthesis. Best for comprehensive research requiring cross-source validation. Use when: the user needs web search, research, source discovery, or content extraction.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `tavily-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Advanced web search via Tavily through AIsa API. Supports search depth, topic filtering (general/news/finance), time ranges, domain inclusion/exclusion, and LLM-generated answers. Use when: the user needs web search, research, source discovery, or content extraction.

Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to a molecular-ML skill (e.g. deepchem). Part of the AlterLab Academic Skills suite.

Fast, accurate code search for AI agents using ~98% fewer tokens than grep+read. Indexes any local or remote repository in under a second (~250ms on CPU, no GPU or API key needed). Supports natural-language and symbol queries, semantic similar-code discovery, and MCP server integration for Claude Code, Codex, Cursor, and OpenCode. Python library available for programmatic use. Triggers on: semble, code search, semantic code search, semble search, token-efficient search, find code, code search mcp, agent code search, semble find-related, semble savings.

Aspire skill covering the Aspire CLI (start, stop, describe, wait, logs, otel, mcp), AppHost orchestration, service discovery, integrations, MCP server, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application. USE FOR: aspire start, aspire stop, aspire describe, aspire wait, aspire logs, aspire otel, list aspire integrations, debug aspire issues, aspire dashboard, aspire add, aspire mcp tools, aspire docs. DO NOT USE FOR: non-Aspire .NET apps (use dotnet CLI), container-only deployments (use docker/podman).

Expert knowledge for Azure Migrate development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Azure Migrate appliances, Arc-based discovery, VMware/Hyper-V moves, AppCAT/CAST scans, or Resource Mover, and other Azure Migrate related development tasks. Not for Azure Database Migration service (use azure-database-migration), Azure Site Recovery (use azure-site-recovery), Azure Virtual Machines (use azure-virtual-machines), SQL Server on Azure Virtual Machines (use azure-sql-virtual-machines).

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Search command center for web, academic, Tavily, and Perplexity-backed research through one AIsa API key. Use when: the user needs one flagship skill for live search, source discovery, or citation-ready research. Supports fast lookup, answer generation, and deep research reports.

Plan-driven implementation in Sage/Acorn — reads docs/plans/ directory, implements components from approved sub-plans, runs scaffold generators (lando acorn acf:block), commits incrementally, auto-invokes block-scaffolding per ACF block, runs lando flush and lando theme-build after changes; full PR workflow with sage-reviewer gate. Invoke for: "/building", "implement from the plan", "implement from the plan", "build from the plan", "execute the plan", "start building", "code this feature". Skip when: there is no approved plan yet — run /architecture-discovery then /plan-generator first.

Access the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

Discover what customers truly need by analyzing the "job" they hire your product to do. Use when the user mentions "customer discovery", "why customers churn", "what job does this solve", "competing against luck", "product-market fit", "switching behavior", "milkshake moment", or "functional vs emotional jobs". Also trigger when investigating why users choose competitors, designing features around real customer needs, or reframing product value propositions. Covers JTBD interviews, competition analysis, and jobs-oriented roadmaps. For product positioning, see obviously-awesome. For rapid validation, see design-sprint.

Set up and verify a connection to an NI USRP radio (USRP E320, N300, N310, N320, N321, X300, X310, or X410) using Wireless Testbench. Use when connecting a USRP for the first time, configuring radio hardware, troubleshooting connection failures, or verifying a radio setup. Covers host inspection (OS, NIC type/speed/MTU), device discovery (findsdru, probesdru), UHD version checking, programmatic radio configuration, and basebandTransceiver verification. Also use when the user mentions USRP setup, radio not found, connection errors, dropped samples, or network configuration for SDR hardware.

Requires node, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `aisa-tavily`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Search the web and extract public page content through AIsa's Tavily-backed API relay. Use when: the user needs open-web research, source discovery, current news lookup, or public URL content extraction. Supports concise result sets, deeper research, and news-focused queries.

Use when building, refactoring, or documenting Graft apps and proxies, including when asked to create a tool server, API server, dual-protocol server, or MCP-HTTP bridge. Graft's core thesis: define tools once and serve them as both HTTP REST endpoints and MCP tools from the same server, with discovery, docs, and OpenAPI generated automatically. Covers concrete actions such as defining tools and handlers, configuring authentication middleware, setting up HTTP and stdio transports, generating OpenAPI documentation, wrapping existing APIs via proxy mode, and wiring up the full CLI workflow.

Turn a generic product tour into a buyer-specific demo that only shows what they told you hurts. Maps every feature you plan to show to a problem the buyer actually stated, writes the "so what" for each moment, and flags the trap of feature-dumping before you fall into it. Built for B2B sales teams, customizable to your product and your discovery. Trigger on "build a demo script", "prep for the demo", "what should I show", "map features to pain", "stop me from feature dumping", or any pre-demo planning pass.

Use when designing, building, or debugging a Model Context Protocol (MCP) server in Node/TypeScript. Triggers: stdio JSON-RPC handshake, tool descriptions as discovery surface, lazy startup vs eager catalog loading, telemetry placement, schema design for tool inputs (zod), tool naming conventions for discoverability, error handling that does not leak stack traces, MCP client compatibility (Claude Desktop, Claude Code, Cursor), local resource fetching, secrets and env var handling, distributing as npm + claude mcp add. NOT for MCP client implementation, MCP HTTP transport (different surface), Anthropic Agent SDK building, or non-MCP plugin systems.

Access the RCSB Protein Data Bank (PDB) for EXPERIMENTALLY determined 3D structures (X-ray, cryo-EM, NMR) of proteins and nucleic acids — searching by text, sequence, or structure similarity and downloading coordinates in PDB/mmCIF format with metadata. Use when retrieving a structure by PDB ID, running sequence or structure similarity searches, or obtaining experimental coordinates for structural biology and drug discovery; for AI-PREDICTED structures of proteins lacking experimental data prefer alterlab-alphafold-db, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.

Use when a user asks to use, run, delegate, or fulfill a task through a capability that may exist as an online Bot/MetaBot skill-service, especially when no local specialized skill matches. Treat Bot, bot, and MetaBot wording as equivalent and case-insensitive for remote service requests; select from the local cached online service list first, then call the remote service and continue through trace get/watch, optional conversation view opening, and rating closure. Do not use for browse-only service discovery, network source registry management, identity creation/switching, or private chat-only requests.

Talk to customers without leading them using Mom Test rules: discuss their life not your idea, ask about specifics in the past, and talk less. Use when the user mentions "customer interviews", "validate my idea", "users say they want it but dont buy", "leading questions", "The Mom Test", "customer feedback bias", or "interview script". Also trigger when preparing user research questions, interpreting ambiguous customer feedback, or designing customer discovery processes that avoid false positives. Covers commitment and advancement, avoiding compliments, and extracting signal from noise. For product-market fit, see jobs-to-be-done. For rapid prototype testing, see design-sprint.

Requires AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `youtube-search`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. YouTube Search API via AIsa unified endpoint. Search YouTube videos, channels, and playlists with a single AIsa API key — no Google API key or OAuth required. Use this skill when users want to search YouTube content. For other AIsa capabilities (LLM, financial data, Twitter, web search), see the aisa-core skill. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Triggers: 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]', or any variation requesting multi-source recency intelligence on a topic. Also use for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X/Twitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend/sentiment/problems/opportunities/comparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.

Build MATLAB apps from requirements to working code. Asks discovery questions (or skips them when the path is known), recommends UIFigure or UIHTML architecture, identifies layout archetype (Dashboard, Explorer, Tabbed, Wizard, Canvas), produces an implementation plan, and executes the build. For UIFigure apps, optionally serializes as App Designer (.mlapp or plain-text .m + .xml). Use when a user wants to build a MATLAB app, create a GUI, make an interactive tool, build a uifigure app, build a uihtml app, build an App Designer app, build a .mlapp app, build a plain-text App Designer app, or asks which approach to use. Also use when user describes spatial layout needs: dashboard, control panel, sidebar, tabs, wizard, stepper, canvas, workspace.

Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.

Creates and maintains project documentation for features, systems, and components. Use when documenting how a feature, system, or component works — including writing, updating, or organizing docs. Does not scan or detect the project's technology stack — use project-discovery for repository analysis and config detection. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not create or update coding standards — use coding-standard instead. Does not generate PR descriptions — use update-pr-description for that. Does not produce runbooks for operational scenarios — use runbook for that. Does not rewrite existing prose for readability — use edit-for-readability for that. Does not produce an ephemeral, understand-now overview of code or a PR — use code-overview for that.

Run a structured discovery interview and produce a complete, developer-ready Product Requirements Document. Load when the user asks to write a PRD, create product requirements, document a feature, define user stories with acceptance criteria, or turn a rough idea into a formal product requirements document. Also triggers on "document this feature", "write requirements for", "create a one-pager", "turn this into a PRD", "I need a PRD for", or any request to produce a structured product document for stakeholder alignment or engineering handoff. Supports Full PRD, Lean PRD, and One-Pager formats. Note: for executable feature specifications (FRs, NFRs, ACs as Given/When/Then consumable by AI coding agents), route to `feature-spec` instead — PRDs frame the product, feature-specs encode the implementable contract.

Unified skill for content strategy and marketing content strategy: content pillars, editorial calendars, keyword research by buyer stage, positioning, messaging hierarchy, trust-building, GEO/AI search optimization, and ROI measurement. Also handles content briefs, SEO briefs, content outlines for writers, on-page SEO optimization, meta descriptions, title tags, keyword density, content research, source discovery, expert sourcing, and information gathering. Use when planning content strategy, creating content briefs for writers, optimizing existing content for SEO, researching topics and sources, or managing editorial operations. Triggers: content strategy, marketing content strategy, content marketing strategy, content planning, editorial calendar, content pillars, messaging hierarchy, content brief, SEO brief, content outline, keyword research, buyer journey content, GEO optimization, AI search optimization, content ROI, content operations, content roadmap, brand messaging, positioning

Build, audit, debug, and migrate websites for durable organic discovery and reliable use by search engines, LLMs, and browser agents. Use for SEO audits or implementation; new sites, routes, templates, CMSs, catalogs, or content programs; crawling, rendering, indexing, robots, canonicals, redirects, sitemaps, hreflang, structured data, metadata, links, faceted navigation, JavaScript SEO, Core Web Vitals, or migrations; keyword, intent, architecture, content, local, media, or authority work; traffic, ranking, CTR, or indexation losses; AEO, GEO, AI Overviews, AI Mode, ChatGPT search, crawler controls, agent-readable pages, Markdown representations, `Accept: text/markdown` content negotiation, `llms.txt`, WebMCP, or agent interaction design. Trigger whenever code or architecture can materially affect public discoverability or machine usability, even if the user does not say SEO. Do not use for paid-search management alone.

Use when asked to fix, build, refactor, review, verify, or release software and the work is non-trivial — including delivering a change request (issue, ticket, or request) from intake through planning, gates, implementation, review, verified PR, and authorized post-merge release. neckbeard routes the change through framing, discovery, design, implementation, review, verification, delivery, and learning — choosing the smallest *safe* intervention, proving it at the real delivery boundary, and leaving an inspectable evidence ledger. For change-request / issue-to-PR work, conditionally loads a 9-phase journey with gates, delivery packet, and lifecycle integration. Composes specialist catalog skills rather than replacing them. Not a persona, not a '10x developer' prompt, not a LOC-minimizer. The journey is not loaded for plain fixes, refactors, or reviews that lack an issue/ticket trajectory.

Orchestrator for the pre-decision business-idea lifecycle — generate ideas, model them, evaluate them, validate them with customers, and only then hand off to product-soul / brainstorming. Routes through `idea-generation`, `business-modeling`, `idea-evaluation`, and `customer-discovery`. Load when the user asks to explore business ideas, find a startup idea, evaluate a venture, validate an idea, says "what business should I start", "should I build this", "is this a good business", "I have a startup idea", "evaluate this venture", "model this business", "validate this idea", "Mom Test this", "Lean Canvas this", "Business Model Canvas", "Value Proposition Canvas", "go/no-go on this idea". Pre-decision suite — once one idea is committed, hands off to `product-soul`. Does NOT design features (use `brainstorming`) or audit built products (use `reality-check`).

World-class autonomous sales and revenue skill system. Use ANY time the user asks to sell, pitch, prospect, close, negotiate, launch, monetize, build funnels, write outreach, craft proposals, develop pricing, design offers, write sales copy, create email sequences, plan campaigns, position products, handle objections, create playbooks, build pipeline, forecast revenue, develop GTM strategy, optimize conversions, write ad copy, create sales decks, score leads, nurture prospects, upsell, cross-sell, retain customers, write case studies, price products, structure enterprise deals, create battle cards, script discovery calls, build affiliate programs, plan product launches, create webinar funnels, design pricing pages, A/B test offers, analyze unit economics, calculate LTV/CAC, or ANY other sales, revenue, monetization, go-to-market, or commercial growth task. If it involves selling, revenue, deals, pipeline, or commercial strategy — USE THIS SKILL. Trigger aggressively.

Expert Socratic discovery skill for exploring ideas, architecture decisions, and technical design before writing any code. Use this skill — proactively and always first — when requirements are vague, when the user is debating between technical approaches, when the problem is unclear, or when a design decision could have significant architectural consequences. Triggers on: "should we use X or Y", "help me think through", "I'm not sure how to approach this", "what's the best architecture for", "trade-offs between", "design before I code", "let's think through this", "I have an idea", "how should I structure", "help me design", "I want to build X but not sure how", "what approach would you recommend", "is this the right way to". Produces a validated design doc with 2-3 implementation options, trade-offs, and a recommendation. Always use before planning when the approach is not yet locked.

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting mu