Results for “discovery”

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

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.

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.

Conduct structured, multi-session brand identity interviews to produce a master brandbook covering purpose, positioning, audience, personality, voice, narrative, and founder-brand tension.

Analyze customer needs by identifying the 'job' they hire a product to do, covering job statements, forces of progress, and competitive landscapes.

Contextual guidance and command discovery. Three modes — narrative (first-time), contextual (mid-task), compact (quick reference). Shows available commands, active skills, and intelligent suggestions based on vault state. Triggers on "/help", "what can I do", "show commands", "how does this work".

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

Plan the UX and UI for a feature before writing code. Runs a structured discovery interview, then produces a design brief that guides implementation. Use during the planning phase to establish design direction, constraints, and strategy before any code is written.

Install and route through OpenSpace, the skill management layer for AI agents, so a host agent can retrieve/rank/load the right SKILL.md out of this jeo-skills catalog (~150 installed skills), then evaluate skill quality from real execution evidence and evolve skills via FIX/DERIVED/CAPTURED updates. Covers install-as-skill-finder, retrieve-a-skill, evaluate-quality, evolve-skills, and local-first hub share/import. Triggers on: openspace, skill finder, skill retrieval, find the right skill, rank skills, skill discovery, skill quality, evolve skill, FIX DERIVED CAPTURED, skill hub, openspace-mcp, DiscoverSkills, skill-discovery, delegate-task.

Synthesizes recent conversations about any topic across Reddit, Hacker News, the open web, and optionally X/Twitter into a single briefing with citations, engagement metrics, and cross-platform pattern analysis.

Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.

Holistic product leader who owns the full product lifecycle — from discovery and strategy through roadmap, stakeholder alignment, go-to-market, and outcome measurement. Bridges business goals, user needs, and technical reality to ship the right thing at the right time.

Create and maintain AI-agent integration files for Pump.fun SDK work, including AGENTS/CLAUDE/COPILOT/GEMINI instructions, .well-known discovery, LLM context docs, skills registries, MCP prompts, and terminal rules. Use when wiring agents to Pump.fun development workflows.

Correlates OT asset inventory with ICS-CERT advisories and CVE data to identify, prioritize, and track vulnerabilities in operational technology environments using Claroty xDome.

Build an Opportunity Solution Tree from outcomes to opportunities, solutions, and tests. Use when a stakeholder request needs problem framing before you decide what to build.

Customer insight specialist for audience understanding. Use when defining target audience, buyer personas, customer pains, value mapping, problem discovery, audience language. Triggers: 'who would buy this', 'target audience', 'buyer persona', 'customer pain points', 'ideal customer', 'user problems', 'value proposition', 'audience research'.

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.

Turns vague features, bug clusters, roadmap items, or launch work into an execution-ready planning packet by choosing the right packet type, separating discovery from delivery, and making blockers, dependencies, and the next move explicit.

Run customer interviews that uncover real problems using Mom Test rules: ask about their life, not your idea; focus on past specifics; and secure commitments instead of collecting compliments.

Daily inbound freelance-job discovery across the markets and platforms the brand chose — scrapes job posts via Claude in Chrome (Freelancer.com API where available), scores each for service fit, deduplicates, and drops matches into a Notion opportunities DB. Runs daily on cron schedule.

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.

Workflow 1.5: Bridge between idea discovery and auto review. Reads EXPERIMENT_PLAN.md, implements experiment code, deploys to GPU, collects initial results. Use when user says "实现实验", "implement experiments", "bridge", "从计划到跑实验", "deploy the plan", or has an experiment plan ready to execute.

This skill should be used when the user asks to "offload context to files", "implement dynamic context discovery", "use filesystem for agent memory", "reduce context window bloat", or mentions file-based context management, tool output persistence, agent scratch pads, or just-in-time context loading.

Create a structured customer interview script with JTBD probing questions, warm-up, core exploration, and wrap-up sections. Follows The Mom Test principles — no leading questions, no pitching, focus on past behavior. Use when preparing for user interviews, creating interview guides, or planning discovery research.

Checks on-chain agent trustworthiness, discovers agents for tasks, profiles agents, looks up wallet/identity records, contacts or dispatches agents, and reads or writes hosted metadata via the 8K4 Protocol (ERC-8004).

Call Cheshire Terminal REST, MCP, discovery, and developer API surfaces for Solana agents, arena rooms, Upstash boxes, trading health, and OpenAPI. Use when integrating cheshireterminal.ai, /mcp, /.well-known/agent-card.json, ct_sk_ API keys, arena coordination, box handoff, or Apigee/zero-service health probes.

Full research pipeline: Workflow 1 (idea discovery) → implementation → Workflow 2 (auto review loop). Goes from a broad research direction all the way to a submission-ready paper. Use when user says "全流程", "full pipeline", "从找idea到投稿", "end-to-end research", or wants the complete autonomous research lifecycle.

Connects an AI agent to a FlowStudio MCP server for Power Automate, handling authentication, tool discovery, and response parsing. Load this foundation skill before using specialized workflow skills for building, debugging, monitoring, or governing flows.

Manages case.dev encrypted document vaults for legal workflows. Creates vaults, uploads files and directories, lists and downloads objects, and runs semantic search across vault contents. Use when the user mentions "vault", "upload documents", "document storage", "download files", "vault search", or needs to manage case files and discovery documents.

Migrates legacy AEM (6.x, AMS, on-prem) to AEM as a Cloud Service using BPA CSV or CAM/MCP target discovery, with one-pattern-per-session workflow for scheduler, replication, event listener, HTL lint, dialog, and custom widget migration.

Master orchestrator for the keyword research pipeline. Chains topic-discovery → seed/modifier ideation → competitor + AI-search gap analysis → question mining → BID method → AIO cannibalization check → adversarial redteam → final ranked queue. Same anti-context-bloat pattern as /blog-pipeline (every layer is an Agent dispatch, never a Skill fork).

AI-native lead intelligence and outreach pipeline. Replaces Apollo, Clay, and ZoomInfo with agent-powered signal scoring, mutual ranking, warm path discovery, source-derived voice modeling, and channel-specific outreach across email, LinkedIn, and X. Use when the user wants to find, qualify, and reach high-value contacts.

Query the Open Targets Platform GraphQL API for target-disease associations, tractability and safety data, genetics/omics evidence, and known drugs. Use when identifying or prioritizing therapeutic drug targets, assessing target druggability/safety, or gathering target-disease evidence for drug discovery. Part of the AlterLab Academic Skills suite.

Plan documentation systems including information architecture, content audits, taxonomy design, content lifecycle management, and doc-as-code workflows. Use when organizing a documentation set, planning content structure, or designing navigation and discovery. Also trigger for 'information architecture', 'content audit', 'doc structure', 'taxonomy', 'content planning', 'documentation strategy', 'IA design', or 'sitemap'.