「discovery」の検索結果

Cloud attack hunting for AWS / Azure / GCP - credential discovery, metadata SSRF, IAM privesc, service enumeration, persistence. Scope + billing aware. Wiki-first, FIND schema output.

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Adaptive, targeted web fuzzing - deterministic wordlist selection (wl-pick.sh) plus judgment. Picks the right SecLists list per surface (content/vhost/api/params/artifacts) smallest-first, calibrates filters against soft-404s, recurses, escalates T0 harness -> T1 seclists -> T2 cewl -> T3 app-specific on signal, pivots to hidden-param fuzzing, and detects/handles WAF/Cloudflare/throttle (backoff, origin-bypass, or hard STOP on the DoS tell). Engagement-type aware (ctf loud, pt calibrated, bb stealth). Use for "fuzz", "content discovery", "directory brute", "vhost fuzz", "find hidden params", "which wordlist", "gobuster/ffuf/feroxbuster/cewl/arjun".

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Create and maintain one committed per-project memory file (PROJECT.md) so agents stop re-discovering the same facts every cold session. Stores only the stable, expensive-to-rediscover, not-in-the-code context — architecture + why, build/test/run commands, decisions and dead-ends — as human-readable markdown, versioned with the code so it can't silently rot. Use when asked to set up project memory/context, stop agents re-grepping the codebase, scaffold or refresh a PROJECT.md / CLAUDE.md memory section, or cut cold-start rediscovery cost. Prose context only — never code, config, or data.

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Operate skilld CLI for Skill discovery, use, installation, inspection, updates, authentication, configuration, restoration, and removal, including Repository, curator, and collection refs. Also read the skilld.dev registry (view, browse, trending, tracks, curators, index) and act for the user's skilld.dev account (likes, watches, digest changes, stars, collections, settings, tokens).

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Run developer customer discovery via a Technical Advisory Board (TAB). Use when the user has never interviewed a user who isn't a friend, is inventing messaging from a conference room, or is guessing at the roadmap instead of hearing the pain firsthand.

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SaaS vendor data processing inventory management. Covers shadow IT discovery, API-based data flow detection, processing purpose mapping, contract status tracking, and continuous inventory reconciliation for cloud service providers.

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Implements data lineage tracking for privacy compliance including origin tracking, transformation logging, access auditing, deletion verification, and cross-system lineage graphs. Covers source-to-sink mapping, GDPR Art. 30 RoPA integration, automated lineage discovery, and breach impact scoping. Keywords: data lineage, data provenance, data flow mapping, transformation logging, deletion verification.

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Builds comprehensive data inventory per GDPR Art. 30 Records of Processing Activities. Covers system-by-system discovery, data flow diagramming, third-party identification, and legal basis per category. Keywords: data inventory, data mapping, Art 30, RoPA, data flow, processing activities.

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Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.

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Find and evaluate influencers for brand partnerships, verify authenticity, and track collaboration performance across Instagram, Facebook, YouTube, and TikTok.

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Authorized security assessment of REST, GraphQL, WebSocket, and SOAP APIs: discovery, authentication and authorization flaws (BOLA/IDOR, JWT/OAuth), rate-limit testing, and a structured multi-phase methodology.

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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.

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A skill that creates new Claude skills and automatically shares them on Slack using Rube for seamless team collaboration and skill discovery.

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This skill should be used when the user asks to "write a chapter", "next chapter", "chapter outline", "draft chapter", "continue the story", "write a scene", "outline a chapter", or wants to write prose for a story project. NOT for planning a scene's structure, subtext, or outcome (use scene-craft), drafting without an outline (use discovery-drafting), or revising existing chapters (use revision-continuity).

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Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.

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Art direction for open world and sandbox games in Blender with biome landmarks, traversable scale, and discovery props.

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Discovers the core attributes of the current code repository and its projects — languages, frameworks, tooling, and where things live — and writes a concise reference section directly into the project's AGENTS.md or CLAUDE.md for other skills, agents, and hooks to consume. Use when scanning, analyzing, or detecting the project's technology stack, build tools, or repository structure. Does not create or update project documentation — use project-documentation for writing feature or system docs.

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Designs the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.

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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.

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Create original short videos using the 被拍到的一天｜观察者瑕疵伴随路线，人物最终发现镜头 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.

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Use when the user needs product management workflows such as RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, go-to-market strategy, feature prioritization, research synthesis, or requirements documentation.

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Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.

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Use when a contributor wants to move beyond simple bug fixes into architectural improvements, technical debt discovery, design proposals, or module ownership opportunities.

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Optimize YouTube videos for search and discovery. Generates SEO-optimized titles, descriptions, tags, hashtags, and chapters. Includes keyword research and competitor analysis. Use when publishing videos, improving discoverability, or optimizing existing content.

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。