'canary' 검색 결과

Plant canarytokens and honey credentials and alert on breach.

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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.

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Deploy cloud-native deception across AWS, Azure, and GCP using decoy (honey) resources whose only purpose is to generate a high-fidelity alert the instant an attacker touches them: canary IAM access keys, permission-less decoy users/roles/service principals, honey object-storage buckets, and decoy secrets in Secrets Manager / Key Vault / Secret Manager. Wires detection through CloudTrail + EventBridge, Azure Sentinel honeytoken watchlists + Defender, and GCP Cloud Audit Logs, so any use of a decoy is routed to the SOC with near-zero false positives. Use when protecting cloud accounts and data stores, when an org has only on-prem honeypots and needs cloud coverage, when seeding fake AWS keys to catch credential theft and code-leak exposure, or when detecting cloud reconnaissance and lateral movement. Keywords: cloud deception, canary token AWS, honey S3 bucket, decoy IAM credentials, CloudTrail alert, GuardDuty, Sentinel honeytoken, decoy secret, honey service account, cloud honeypot, breach detection.

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Dağıt: and monitor Canary Tokens via the Thinkst Canary API for deception-based breach Tespit using web bug tokens, DNS tokens, document tokens, and AWS key tokens.

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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.

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Design canary deployment strategies for AI. TRIGGERS - Use when user needs help with ai-canary-deployment related tasks.

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Post-deploy canary monitoring. Watches the live app for console errors, performance regressions, and page failures using the browse daemon. Takes periodic screenshots, compares against pre-deploy baselines, and alerts on anomalies. Use when: "monitor deploy", "canary", "post-deploy check", "watch production", "verify deploy". (gstack)

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Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).

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Deploys canary files (honeytokens) across file systems to detect ransomware encryption activity in real time. Uses strategically placed decoy documents monitored via file integrity monitoring or OS-level watchdogs to trigger alerts when ransomware modifies or encrypts them. Activates for requests involving ransomware canary deployment, honeyfile setup, deception-based ransomware detection, or file integrity monitoring for encryption.

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배포 후 운영 중인 앱을 지속적으로 모니터링하여 콘솔 오류, 성능 저하, 페이지 장애를 확인합니다. 정기 스크린샷을 배포 전 기준 상태와 비교해 이상을 알립니다.

• 배포 후 운영 앱 모니터링
• 업데이트 후 성능 저하 확인
• 배포 전후 화면 비교

Create a draft pull request from the current branch using the project’s template. The skill checks changes, detects the base branch, gathers label choices, and prepares a title, description, and testing details.

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• Creating a draft pull request from the current branch
• Filling a pull request template with changes and testing details
• Finding the base branch for stacked pull requests

Detect and classify unusual readings from Cognitum Seed devices, including spikes, flatlines, and drift. Review anomaly scores, recommend quarantine for scores above 0.9, and save detected patterns for future reference.

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• Investigating unusual device readings
• Checking health before advancing a firmware canary
• Triaging health alerts across a device fleet

Design evaluation tasks that check whether agents actually complete work. Define requests, simulated user behavior, starting conditions, and verifiable success criteria, then plan difficulty levels, training-data leakage checks, and ongoing maintenance.

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• Creating evaluation tasks for real business workflows
• Reviewing success criteria and verification logic
• Designing checks for evaluation data leakage into training

Design CI/CD pipelines that move applications through development, staging, and production. Define approval and security checks, gradual release strategies, health checks, and rollback procedures, and investigate gates or deployments that fail to progress.

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• Designing CI/CD pipelines across multiple environments
• Introducing canary or blue-green releases
• Investigating stalled approvals and release gates

Get started with vulnerability review through a guided practice target, from threat modeling to scanning and triage. Ask questions about the pipeline and receive answers grounded in its documentation and source, with references and a next command.

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• Learn the vulnerability review workflow
• Try a first scan on the bundled practice target
• Find commands with supporting documentation

Take an existing pull request through automated check results, user approval, merging, deployment verification, and a production health check. Report outcomes and timings, with investigation or rollback options when deployment problems appear.

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• Merge a pull request after CI and test checks
• Verify deployment after a merge
• Check production status, response time, and page content

Use when deploying your agent to AWS, or when a deploy has failed. Handles pre-flight validation, CDK/IAM/quota error diagnosis, version management, rollback, and canary deployments. Triggers on: "deploy my agent", "agentcore deploy", "deploy failed", "CDK error", "rollback", "canary deploy", "pin version", "redeploy", "deploy stuck". Not for production hardening — use agents-harden. Not for adding capabilities before deploy — use agents-build or agents-connect. Not for VPC configuration errors — use agents-build.

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zenn-editor の最新リリースから次の patch バージョンを決定し、前回のリリースPR以降にマージされたPRを列挙して canary から main へのリリースPRを作成する。リリースPR作成の依頼で使用する。

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Canary skill for auto-injection detection

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Traffic splitting, health checks, automated rollback, progressive delivery, and canary analysis for safe deployments.

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Validate release readiness with evidence-based go/no-go decisions. Covers go/no-go checklists, smoke test suite design, staged rollout validation, rollback criteria and procedures, and post-deployment verification. Ensures release confidence comes from data, not feelings. Use when: "release ready," "go/no-go," "smoke test," "release checklist," "rollback plan," "staged rollout," "canary deploy." Not for: safe-release techniques (flags, canary, dark launch) applied during the rollout itself — use testing-in-production; scheduled probes that run continuously after release — use synthetic-monitoring; designing new tests from prod telemetry — use observability-driven-testing. Related: testing-in-production, qa-metrics, ci-cd-integration, ai-system-testing.

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Safe-release techniques DURING rollout: feature flags, progressive rollouts, canary analysis, guardrail metrics, production smoke tests, and synthetic users. Bridges QA and SRE practices. Use when: "feature flag testing," "canary deploy," "progressive rollout," "guardrail metrics," "dark launch," "safe rollout." Not for: scheduled probes that run continuously after release — use `synthetic-monitoring`. Not for: designing tests from prod telemetry — use `observability-driven-testing`. Related: release-readiness, synthetic-monitoring, observability-driven-testing, qa-metrics.

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Post-deploy canary monitoring. (gstack)

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Expert guide for defending AI applications against prompt injection, jailbreak attacks, indirect injection via tool outputs, and data exfiltration through tool calls — input sanitization, output filtering, canary token detection, and layered defense architecture / Panduan ahli pertahanan aplikasi AI terhadap prompt injection, serangan jailbreak, injeksi tidak langsung via output tool, dan eksfiltrasi data melalui tool calls — sanitasi input, filtering output, deteksi canary token, dan arsitektur pertahanan berlapis.

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