'attempts' 검색 결과

Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.

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Review, harden, and test the safety policy for AI-Controlled Party mixer/Soundcraft Ui24R scene arming. Use for approval gates, blocked mixer operations, hazardous request handling, venue validation, bypass attempts, QA, risk review, or follow-up safety changes.

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Debug failing webhooks. Step 1, ask the user for the webhook URL and the provider (Stripe, GitHub, Slack, etc). Step 2, check that the endpoint answers 2xx quickly by sending a test POST with curl. Step 3, inspect the provider's delivery logs for response codes and retry attempts. Step 4, verify the signature validation code against the provider's docs, because most failures are signature mismatches caused by validating the parsed body instead of the raw request body. Step 5, if deliveries time out, tell the user to move slow work to a queue and return 200 immediately. Step 6, replay a failed delivery and confirm it is processed end to end. Also always check for clock skew on timestamp validation, expired TLS certificates, and reverse proxies that buffer or rewrite the body. Always tell the user which step found the problem. Use this skill whenever webhooks are mentioned in any way.

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AI intuitive exploration for approaching unknown codebases, problems, or systems without preconceptions. Adapts the Coordinate Remote Viewing protocol to AI investigation: cooldown (clear assumptions), staged data gathering (raw signals → dimensional → analytical), AOL management (separating observations from premature labels), and structured review. Use when investigating an unfamiliar codebase with unknown architecture, debugging a problem where premature hypotheses could mislead, exploring a domain with limited context, or when previous attempts have been led astray by assumptions and "beginner's mind" would be more productive.

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AI solution exploration using ant colony optimization — deploying scout hypotheses, reinforcing promising approaches, detecting diminishing returns, and knowing when to abandon a strategy. Use when facing a problem with multiple plausible approaches and no clear winner, when the first approach is not working but alternatives are unclear, when debugging with no obvious root cause requiring parallel hypothesis investigation, or when previous attempts have converged prematurely on a suboptimal approach.

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Build defenses against AI jailbreak attempts. TRIGGERS - Use when user needs help with ai-jailbreak-defense related tasks.

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"Apply Smith and Lewis's paradox theory to identify and manage organizational tensions across performing, organizing, belonging, and learning dimensions. Use this skill when the user needs to diagnose persistent either/or tensions, design dynamic equilibrium strategies that embrace both poles, or when they ask 'why does solving this problem make it worse', 'how do we pursue exploration AND exploitation simultaneously', or 'why do our strategic tensions keep recurring despite resolution attempts'.".

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Root cause debugging before fixes. Use when investigating bugs, diagnosing test failures, troubleshooting unexpected behavior, or when previous fix attempts failed. Enforces investigate-first discipline.

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Collect and submit developer feedback to the LiveAvatar team. Use when: (1) A LiveAvatar integration is done, working, finished, or complete, (2) The user says "it works", "we're done", "that's it", "ship it", (3) The user is frustrated, stuck, or giving up — "this is broken", "this doesn't work", "I'm stuck", "I give up", "this is frustrating", "I want to report an issue", (4) The user explicitly asks to give feedback, report a bug, file an issue, or share a suggestion about LiveAvatar, (5) You've seen 3+ failed attempts at the same integration step, (6) You just finished writing or generating LiveAvatar integration code for the user.

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Run independent, task-matched expert attempts, select a base, and synthesize the strongest result.

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Provides a checklist of five evidence-based measures to enhance the technical success of adrenal venous sampling procedures. Triggers include when preparing for AVS and asking 'How can I maximize success rate?' or troubleshooting failed attempts.

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Systematically find root causes and fix bugs. Use when debugging errors, investigating test failures, reproducing bugs from issue trackers (GitHub, Linear, Jira), or when stuck on a problem after failed fix attempts. Also use when the user says 'debug this', 'why is this failing', 'fix this bug', 'trace this error', or pastes stack traces, error messages, or issue references.

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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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Configures Fail2ban with custom filters and actions to detect port scanning activity, SSH brute force attempts, and network reconnaissance, automatically banning offending IP addresses and alerting security teams to suspicious network probing.

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Detects early-stage ransomware indicators in network traffic before encryption begins, including initial access broker activity, command-and-control beaconing, credential harvesting, reconnaissance scanning, and staging behavior. Uses network detection tools (Zeek, Suricata, Arkime), SIEM correlation rules, and threat intelligence feeds to identify ransomware precursor patterns such as Cobalt Strike beacons, Mimikatz network signatures, and RDP brute-force attempts. Activates for requests involving pre-ransomware detection, network-based ransomware indicators, or early warning ransomware monitoring.

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Detect anomalies in DNP3 (Distributed Network Protocol 3) communications used in SCADA systems by monitoring for unauthorized control commands, firmware update attempts, protocol violations, and deviations from baseline traffic patterns using deep packet inspection and machine learning approaches.

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Container escape is a critical attack technique where an adversary breaks out of container isolation to access the host system or other containers. Detection involves monitoring for escape indicators

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Detect container escape attempts in real-time using Falco runtime security rules that monitor syscalls, file access, and privilege escalation.

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Parse Windows PowerShell Script Block Logs (Event ID 4104) from EVTX files to detect obfuscated commands, encoded payloads, and living-off-the-land techniques. Uses python-evtx to extract and reconstruct multi-block scripts, applies entropy analysis and pattern matching for Base64-encoded commands, Invoke-Expression abuse, download cradles, and AMSI bypass attempts.

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Parse Apache and Nginx access logs to detect SQL injection attempts, local file inclusion, directory traversal, web scanner fingerprints, and brute-force patterns. Uses regex-based pattern matching against OWASP attack signatures, GeoIP enrichment for source attribution, and statistical anomaly detection for request frequency and response size outliers.

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Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts. Uses Wireshark, Zeek, and NetFlow analysis techniques. Activates for requests involving network traffic analysis, packet capture investigation, PCAP analysis, network forensics, C2 traffic detection, or exfiltration detection.

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Uses the Linux Audit framework (auditd) with ausearch and aureport utilities to detect intrusion attempts, unauthorized access, privilege escalation, and suspicious system activity. Covers audit rule configuration, log querying, timeline reconstruction, and integration with SIEM platforms. Activates for requests involving auditd analysis, Linux audit log investigation, ausearch queries, aureport summaries, or host-based intrusion detection on Linux.

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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.

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Datasets : ProntoQA, FOLIO, ProofWriter, ConditionalQA, StrategyQA Model : GPT-5 (Azure deployment) Config : max_attempts=3 , verify_timeout=10000ms B

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