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Run an Anthropic Claude Managed Agent — a cloud agent harness (container + filesystem + tools), the cloud counterpart of the local wasm-agent runtime

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Use when auditing metadata, crawlability, structured data, or indexability related to Provide clear affiliate disclosures. Verify the rendered HTML and HTTP response rather than relying only on source files.

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Generate expressive, multilingual narration with fish.audio (S1 / S2-generation models) and reuse cloned voices via reference_id. Use when the user prefers fish.audio/Fish Audio TTS, wants a specific playground voice model, or needs high-emotion voice-clone narration.

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When the user wants to run influencer, creator, or ambassador partnerships to promote their product — finding and vetting partners, structuring deals, briefing creators, disclosure compliance, and measuring ROI. Also use when the user mentions 'influencer marketing,' 'creator partnerships,' 'sponsorships,' 'YouTube sponsorships,' 'podcast sponsorships,' 'brand ambassador,' 'ambassador program,' 'creator program,' 'UGC creators,' 'tech UGC,' 'UGC creator program,' 'creator network,' 'B2B influencers,' 'thought leader ads,' 'gifting,' 'product seeding,' 'whitelisting creator content,' 'Telegram channel ads,' 'buy a post in a Telegram channel,' 'how much to pay an influencer,' or 'FTC disclosure.' For affiliate/referral payout mechanics, see referrals. For community-led advocacy, see community-marketing. For turning creator content into paid ads, see ad-creative.

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When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking plan," "how do I measure this," "track conversions," "Mixpanel," "Segment," "are my events firing," or "analytics isn't working." Use this whenever someone asks how to know if something is working or wants to measure marketing results. For choosing attribution models, comparing multi-touch/MMM/incrementality, or reconciling conflicting numbers across tools, see attribution. For A/B test measurement, see ab-testing. Rate metrics divide matching closed windows — never full-period spend by partial-period results. Rate metrics divide matching closed windows, never full-period spend by partial-period results.

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12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, I got reviewer comments, revision roadmap, should we push back, conference rebuttal, grant panel response, audit my rebuttal, check my response draft, AI disclosure, check citations, citation check, check my references, verify references, look over the refs, 寫論文, 學術論文, 引導我寫論文, 審查意見, 我收到審查意見, 修訂路線圖, 評估回覆, 檢查引用, 引用檢查, 檢查參考文獻, 核對文獻, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견을 받았어, 심사 의견 반영, 답변서 점검, AI 사용 고지, 인용 확인, 인용 형식 검사, enmendar mi artículo, redactar artículo, guía mi artículo, analizar reseñas, auditar mi respuesta, verificar borrador de respuesta, verificar citas, divulgación de IA.

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Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.

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Store team knowledge, project conventions, and learnings from tasks. Use to remember what works and recall context before new tasks. Connects to Hindsight Cloud. (user)

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AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python.

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Configure AWS CloudTrail for audit logging. Set up organization trails and event analysis. Use when auditing AWS activity.

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AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture.

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AI-agent, prompt-injection, MCP or toolchain, cloud, container, CI/CD, and supply-chain challenges. Use when the user asks to analyze prompt-to-tool flows, retrieval poisoning, mounted secrets, deployment drift, runtime-vs-manifest mismatches, registry provenance, or CI-produced artifacts under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Use for authorized cloud, container, and Kubernetes security assessment including metadata SSRF, IAM misconfig, container escape paths, and cluster RBAC review.

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Optimize cloud costs across AWS, Azure, GCP, and OCI through resource rightsizing, tagging strategies, reserved instances, and spending analysis. Use when reducing cloud expenses, analyzing infrastructure costs, or implementing cost governance policies.

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Building one page that looks like a page the course already has — copy the model page with `duplicate_scene`, then rewrite the copy's words with one `patch_stage` op per content slot. Use when a new page has to inherit an existing page's design, whether the model page came from an imported deck or the course wrote it earlier. Not for revising a page that is already the page you want (that is `pro-editing`), and not for the course-level work of cataloguing layouts and assigning one per outline entry (that is `style-clone`).

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Building a new course inside someone else's deck — import the deck as a library of layouts, then produce each page of the new course by copying the layout that fits it and rewriting that copy's content element by element, so the result reads as though the original author made it. Use when the session has an imported deck (or a `.pptx` to import) and the ask is to teach new material in its design — its palette, its typography, its way of dividing a page. Not for a straight import that must stay slide-for-slide identical to the file (that is `pptx-import`), and not for extracting how a teacher speaks from a recording or handout (that is `teacher-style-clone`, which supplies the voice while this one supplies the look; the two compose).

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Cloud design patterns for distributed systems architecture covering 42 industry-standard patterns across reliability, performance, messaging, security, and deployment categories. Use when designing, reviewing, or implementing distributed system architectures.

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Reusable investigation patterns for AWS CloudWatch: Logs Insights query templates, alarm-to-deployment correlation, blast-radius narrowing decision tree, and PromQL-style metric query patterns for structured incident triage.

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Structure post-acquisition value creation plans with revenue, cost, and operational levers mapped to an EBITDA bridge. Includes 100-day priorities, KPI targets, and accountability frameworks. Use when planning post-close execution, preparing operating partner materials, or building a board-ready value creation roadmap. Triggers on "value creation plan", "100-day plan", "post-close plan", "EBITDA bridge", "operating plan", or "value creation levers".

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Analyze WAF (ModSecurity/AWS WAF/Cloudflare) logs to detect SQL injection attack campaigns. Parses ModSecurity audit logs and JSON WAF event logs to identify SQLi patterns (UNION SELECT, OR 1=1, SLEEP(), BENCHMARK()), tracks attack sources, correlates multi-stage injection attempts, and generates incident reports with OWASP classification.

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Detecting data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty findings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.

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Deploy and operationalize Amazon GuardDuty, covering protection plans for S3, EKS, EC2 runtime monitoring, and Lambda, interpreting finding severity, and building automated response with EventBridge and Lambda. Use when establishing threat detection for AWS accounts, investigating findings on compromised instances or credential abuse, or building automated incident-response playbooks.

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Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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Audit Azure Blob and ADLS storage accounts for public access exposure, weak or long-lived SAS tokens, missing encryption at rest, disabled HTTPS-only traffic, and outdated TLS versions, using the azure-mgmt-storage Python SDK to generate a risk-scored report. Use when assessing an Azure subscription's storage accounts for misconfiguration, building cloud security posture checks, or investigating a suspected data exposure via public blob access.

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。