「scout」の検索結果

Vendor (copy-import) a single external Claude Code skill into your own skills repo correctly — with license compliance and full provenance/attribution. Deterministic, idempotent primitive: given an upstream ref (owner/repo/path@sha or a GitHub URL) it fetches the skill, detects and gates on the license, copies the folder, injects a provenance frontmatter block (license + metadata: author/source/upstream_commit/vendored_at/provenance), ships the upstream LICENSE, adds a ## Credits section, and records a CREDITS.md ledger entry. Use when the user asks to "vendor a skill", "import a skill", "clone a skill", "borrow someone's skill", "add an external skill with attribution/credit", or スキルを取り込む / vendoring / 他人のスキルを自分のリポに取り込む. This is the import primitive that a skill-scout/curator orchestrator calls; it does NOT discover or evaluate — it correctly imports ONE skill you already chose.

Analyze CGM blood glucose data from Nightscout. Use this skill when asked about current glucose levels, blood sugar trends, A1C estimates, time-in-range statistics, glucose variability, or diabetes management insights.

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End-to-end execution of a scouted third-party provider access campaign. Use when a Provider Access Launch task is ready_to_submit or scouted easy_execute and needs account or app registration, scopes, reviewer assets, Vault credential custody, the canonical connection, and real verification.

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Decision-ready scouting record for a user-connected provider access campaign. Use when an assigned Provider Access Launch task needs its easiest official route, minimum scopes, cost, account needs, approval path, implementation consumer, and easy-execute verdict established before submission. NOT for client Local Listings distribution.

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。

AI regression scouting routing. Use when agents, prompts, skills, model/tool routing, harness fixtures, or generated-output expectations change and need regression scenarios; do not use for ordinary product-code regressions.

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新しいスキルを作る前に、ローカル環境やマーケット、GitHub、ウェブから既存候補を探し、用途との一致度や安全性を確認して再利用・拡張・新規作成の判断を助けます。

• 作業に合うスキルを探したいとき
• 新規作成前に重複を確認したいとき
• 拡張する既存スキルの候補選び

This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.

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Audit AWS, Azure, and GCP environments against the CIS Foundations Benchmarks by running automated scans with tools like Prowler and ScoutSuite, interpreting failed controls, and tracking remediation for continuous compliance. Use when conducting a cloud security audit, validating CIS benchmark compliance (CIS v5 AWS, v4 Azure, v4 GCP), or setting up continuous cloud compliance monitoring.

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Auditing Microsoft Entra ID (Azure Active Directory) configuration to identify risky authentication policies, overly permissive role assignments, stale accounts, conditional access gaps, and guest user risks using AzureAD PowerShell, Microsoft Graph API, and ScoutSuite.

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Orchestrate free opencode workers from Claude Code to cut token costs. Use when delegating grunt work to a single worker or a parallel swarm (scout/coder/tester) with worktree isolation, benchmarking against opencode, or when the user says "spawn a worker", "swarm", "delegate to opencode", or "/oc".

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Triage customer support tickets/emails/chats into categories, priority, and next action; draft responses and create reproducible steps; use for Zendesk/Intercom/Help Scout exports or pasted threads.

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。

Cloud security attack methodology covering AWS, Azure, and GCP. Includes credential harvesting (IMDS, ~/.aws, env vars, leaked CI secrets, instance roles), enumeration with cloud-specific tools (pacu, ScoutSuite, Prowler, ROADtools, gcp_enum), privilege escalation paths (IAM PassRole, AssumeRole chains, Lambda/Functions privilege flips, Azure Owner-on-self, GCP serviceAccountTokenCreator), persistence techniques (IAM user/key creation, AAD app registration, GCP svc account key creation, EventBridge/Logic Apps backdoors), data exfiltration (S3/Blob/GCS, snapshot share, RDS/CosmosDB/Cloud SQL exfil), cloud-native lateral movement (cross-account assume, Azure AD multi-tenant, GCP project hierarchy), serverless attacks (Lambda env vars, layer hijack, Step Functions), Kubernetes-on-cloud (EKS/AKS/GKE-specific paths to node and AWS metadata), and CSPM evasion (CloudTrail blind spots, GuardDuty mute, Sentinel rule shaping). Use when the engagement scope is cloud accounts, when you've stolen cloud credentials, or when assessing cloud posture.

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Interact with Kleros Curate registries across Ethereum Mainnet, Gnosis Chain, and Sepolia. Use when the user mentions Curate, Light Curate/LightGeneralizedTCR/LGTCR, Stake Curate/PermanentGTCR/PGTCR, Scout, token-curated registry/TCR, token lists, address/CDN tags, Goldsky, MetaEvidence, Verify Your List, or Curate calls such as addItem, removeItem, challengeItem, challengeRequest, and fundAppeal. Covers registry discovery and queries; policy/schema inspection; submissions, removals, challenges, evidence, appeals, rewards, execution; factory deployment; and frontend verification on all three networks. Also trigger for registry/list operations paired with Kleros, arbitrator, dispute, juror, or PNK context, and whenever the user names or tests kleros-curate. Do NOT use for non-Kleros registries or generic IPFS uploads without Curate context; route standalone uploads to kleros-ipfs-upload.

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Explore SQLite and PostgreSQL databases: list tables, inspect schemas (columns/types/constraints), preview data, generate Mermaid ER diagrams, and run safe read-only queries. Triggered by requests to explore a database, view table structures, describe tables, generate diagrams, or query data, and by keywords like database exploration, schema, ER diagram, or SQL query.

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Use when the user asks Codex to research, find learning materials, process "素材：" links, "请你读" / "精读" a material, build a material radar, or use multi-source information retrieval for career learning and Agent infra tracking. Do not use the career/Agent-infra routing bias for user-directed `整理笔记` into a named note.

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Write readable, maintainable code through disciplined naming, small functions, and clean error handling. Use when the user mentions "clean up this code", "this function is too long", "code smells", "naming conventions", "boy scout rule", "single responsibility", or "unit test quality". Also trigger when reviewing a pull request for readability, untangling a messy function, debating comment styles, or improving error-handling patterns. Covers SRP, comment discipline, formatting, and unit testing. For refactoring techniques, see refactoring-patterns. For architecture and dependency rules, see clean-architecture.

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。

Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip

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。

Autonomous content pipeline - scout announcements in your field, triage by trend momentum and personal angle, produce posts/blogs/videos in your voice with ledger-based dedup, hard volume caps, and screenshot-verified publishing

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。

Answer one named planning question with observations tied to the inspected sources; do not write or coordinate the roadmap.

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。

亚马逊 Sif 关键词情报侦察系统（Skill 1）。用户输入 ASIN，获取 Sif 三张报表（关键词调研/反查流量词/查广告词）， Python 完成分层、机会评级、缺口分析，三表交叉输出 PD 主攻词单。支持「用户手动导出」与「AI 浏览器导出」两种方式。 路径通过工作区 .sif-config.json 解析，不写死本机路径。Agent 以亚马逊关键词策略顾问角色解读结果并引导参数。 若 ASIN 有历史记录则自动触发 sif-keyword-tracker。触发：sif关键词、PD备战关键词、ASIN关键词调研、运行skill1。

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Sif 关键词动态跟踪（Skill 2）。同一 ASIN 有历史时由 sif-keyword-scout 自动触发， 按 1~7 天窗口对比 PD 主攻词单，输出词库更新报告。Agent 必须写 insights_tracker.md， 且在写 insights 前完成暂停点 ③ 与用户确认投放策略。

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Explores codebase structure, affected files, blast radius, related tests, and local conventions for a focused topic. Use when review or planning needs structural lookup without bloating main context.

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Analyzes current conversation context to recommend the best skills and subagents for the task at hand. Use proactively when unsure which tool, skill, or agent to use.

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Use when looking for a meta-analysis topic before any protocol exists. Starts from a professor's publication profile or from a clinical question, finds gaps, assesses feasibility and returns a ranked topic list. Running the review itself is /meta-analysis.

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。