「skill discovery」の検索結果

Builds Agent2Agent (A2A) servers and clients, the open protocol (originally from Google, now under the Linux Foundation) that lets AI agents from different frameworks call each other. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a task.

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API Fuzzing for Bug Bounty workflow skill. Use this skill when the user needs Provide comprehensive techniques for testing REST, SOAP, and GraphQL APIs during bug bounty hunting and penetration testing engagements. Covers vulnerability discovery, authentication bypass, IDOR exploitation, and API-specific attack vectors and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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API Fuzzing for Bug Bounty workflow skill. Use this skill when the user needs Provide comprehensive techniques for testing REST, SOAP, and GraphQL APIs during bug bounty hunting and penetration testing engagements. Covers vulnerability discovery, authentication bypass, IDOR exploitation, and API-specific attack vectors and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Sub-skill de agent-ready-cloudflare: Implement OAuth/OIDC Discovery

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Advanced Amazon product search and filtering powered by Nexscope data, supporting multi-dimensional criteria including category, price, monthly sales, keywords, BSR rank, review count, rating, package dimensions, weight, fulfillment type, and more. Trigger when users mention Nexscope product selection, Amazon product search, advanced product selection, BSR filtering, sales rank filtering, keyword-based product discovery, category search, competitor screening, niche product discovery, historical rank filtering, or similar terms. Even if the user does not explicitly mention "Nexscope," trigger this skill whenever the request involves multi-criteria Amazon product search, sales-metric-based filtering, or advanced product selection beyond simple keyword search.

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Use Nexscope data to search and filter Amazon products, supporting multi-dimensional criteria including price, monthly sales, BSR ranking, gross margin, ratings, fulfillment method, badges, seller origin, and more across multiple Amazon marketplaces. Trigger when the user mentions Amazon product selection research, product filtering, sales filtering, product discovery, BSR analysis, niche product discovery, competitor analysis, market opportunity assessment, product-level market size estimation, gross margin screening, Nexscope product selection, Amazon product selection, sales filtering, BSR analysis, profit screening, market analysis, product selection tool. Even if the user does not explicitly mention "Nexscope", if their need involves filtering and analyzing Amazon product-level data for product selection, this skill should also be triggered.

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Multi-dimensional Amazon product search and filtering based on Nexscope data, covering 14 marketplaces, with support for historical monthly snapshot lookback. Trigger when the user mentions Nexscope product search, Amazon product filtering, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot review, product search, monthly sales/revenue, ABA keyword product discovery, price range filtering, new product discovery, multi-condition combined filtering, product search, competitor research, category analysis, brand bestsellers, seller analysis, seasonal products, historical snapshot. Even if the user does not explicitly mention "Nexscope", if their need involves Amazon product search, filtering, comparison, or product exploration by category/brand/seller dimensions, this skill should also be triggered.

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 Nexscope data to find and analyze competitors on Amazon, covering 12 marketplaces, with product metrics including sales, BSR, pricing, ratings, and growth trends. Trigger when the user mentions competitor lookup, competitor analysis, ASIN reverse lookup, competitive product research, finding similar products, market competitor discovery, product benchmarking, competitor sales estimation, analyzing competitor listings, competitor analysis, ASIN reverse lookup, competitor sales, competitor research, Nexscope, market competitor discovery, competitor trends. Even if the user does not explicitly mention "Nexscope" or "competitor lookup", if their need involves discovering and analyzing Amazon competitors by ASIN, keyword, seller name, brand, or category, this skill should also be triggered.

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Nexscope Product Database multi-condition filtering. Filter Amazon products by category, price, sales volume, revenue, reviews, rating, weight, BSR rank, LQS, seller type, and more across 10 marketplaces. Trigger when users mention Amazon product selection, product database filtering, BSR rank filtering, category-based product discovery, high-rating low-competition products, FBA product search, Amazon product discovery, or similar terms. Even if the user does not explicitly mention "Nexscope" or "product database," trigger this skill whenever the request involves filtering Amazon products by multiple criteria or discovering potential products.

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用户访谈与需求验收框架，覆盖原始证据、方案、优先级及回访交接。HeyGen 做 937 次访谈找到 PMF 的完整方法论。覆盖访谈招募、问题设计、 访谈执行、数据合成、决策驱动。从 gingiris-user-interview 提炼。

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🇺🇸 KOL Outreach & Influencer Marketing Playbook — Complete SOP from discovery to ROI tracking. Battle-tested with 200+ KOL campaigns at AFFiNE (60k stars). Includes pricing benchmarks, email templates, content packages, platform algorithm guides, and data-driven evaluation frameworks. 🇨🇳 KOL 外联与红人营销实战手册 — 从发现筛选到 ROI 追踪的完整 SOP。基于 AFFiNE（60k stars）200+ 次 KOL 合作实战验证。包含报价基准、邮件模板、内容包模板、各平台算法指南、数据评估体系。 🇯🇵 KOLアウトリーチ＆インフルエンサーマーケティング実践ガイド — 発見からROI追跡までの完全SOP。AFFiNE（60k stars）での200回以上のKOLキャンペーン実績。価格ベンチマーク、メールテンプレート、コンテンツパッケージ、プラットフォームアルゴリズムガイド。 🇰🇷 KOL 아웃리치 & 인플루언서 마케팅 플레이북 — 발굴부터 ROI 추적까지 완전한 SOP. AFFiNE(60k stars) 200+ KOL 캠페인 실전 검증. 가격 벤치마크, 이메일 템플릿, 콘텐츠 패키지, 플랫폼 알고리즘 가이드. Triggers: "KOL outreach" | "influencer marketing" | "KOL campaign" | "influencer outreach" | "creator partnership" | "KOL strategy" | "red人" | "红人营销" | "KOL合作" | "达人合作" | "influencer pricing" | "KOL ROI" | "content creator outreach" | "KOL筛选"

Decide which plugin component type to use and how to organize components at scale. Covers the component lifecycle (discovery and activation phases), decision framework for choosing between commands, skills, agents, hooks, and MCP servers, and organization patterns for each component type. Use when asking "which component type should I use", "command vs skill vs agent", "when to use a hook vs MCP server", "component lifecycle", "how to organize plugin components", "plugin structure patterns", or "scale a plugin with many components".

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Use when starting a new feature, gathering requirements for an unfamiliar domain, refining a vague idea into actionable scope, or when a user request is ambiguous or underspecified. Conducts SAM Stage 1 discovery — structured requirements gathering through user discussion, asking WHO/WHAT/WHEN/WHY and never HOW. Produces the ARTIFACT:DISCOVERY document containing feature requirements, NFRs, goals, anti-goals, references, and resolved questions. Supports backlog item self-initialization via a

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Deprecated alias. This skill was renamed to `deepline-research`. Use when a user or script still invokes `/deepline-pre-research`: immediately load and follow the `deepline-research` skill instead. Triggers: pre-research, source discovery, provider strategy, research data sources, ScrapeCreators, X/Twitter data, Reddit comments, public and private datasets, CRM data, workflow data, custom language, messaging language, pain language.

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This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.

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This skill should be used when the user asks to "create AGENTS.md", "update AGENTS.md", "maintain agent docs", "set up CLAUDE.md", or needs to keep agent instructions concise. Guides discovery of local skills and enforces minimal documentation style.

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Use this skill to create or update the root AGENTS.md file to register AgenticDev skills for AI agent discovery. Triggers include "register AgenticDev", "update AGENTS.md", "setup agent guide", or initializing a new project.

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[Deprecated] discovery and meta-skill merged. Use `cm-skill-index` instead.

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Strategic product leadership guidance for SaaS and technology companies. Covers product strategy, roadmap planning, product discovery, user research, growth product management, platform strategy, product analytics, and product launches. Use when defining product vision, prioritizing features, conducting discovery, analyzing metrics, or launching products. Use for "product strategy", "roadmap planning", "feature prioritization", "product discovery", "product metrics", "PRD writing".

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How agents discover and use skills. Use to understand skill invocation protocol.

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Create a new agent definition file following the agent-almanac agent template and registry conventions. Covers persona design, tool selection, skill assignment, model choice, frontmatter schema, required sections, registry integration, and discovery symlink verification. Use when adding a new specialized agent to the library, defining a persona for a Claude Code subagent, or creating a domain-specific assistant with curated skills and tools.

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One to three sentences describing what this skill accomplishes, followed by key activation triggers. This field is the primary mechanism agents use to decide whether to activate the skill — it is read during discovery before the full body is loaded. Start with a verb. Include the most important "when to use" conditions inline. Max 1024 characters.

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Pythonic wrapper around RDKit with a simplified interface and sensible defaults. Preferred for standard drug discovery work — SMILES/SELFIES/InChI conversion, molecule standardization and sanitization, descriptors, ECFP and other fingerprints, Tanimoto distance matrices, Butina clustering and diverse subset picking, Bemis-Murcko scaffolds and scaffold splits, BRICS/RECAP fragmentation, 3D conformer generation, SDF/CSV/Excel and cloud I/O, and parallel processing via n_jobs. Returns native rdkit.Chem.Mol objects, so it composes with RDKit throughout. Also trigger on datamol, `import datamol as dm`, dm.to_mol, dm.standardize_mol, dm.cluster_mols, or dm.pick_diverse. For advanced control or custom parameters, use the rdkit skill directly.

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Use Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.

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。