175 skills · sorted by relevance
Manage code changes across multiple AI agents while recording operations and their outcomes. Use previous task histories to find similar attempts, inspect successful patterns, and obtain suggested steps for implementation, code review, deployment, or merging changes.
Summarizes the complete lifecycle of a bug across code versions, tracking its introduction, detection, fixing attempts, and regression history. Use when users need to: (1) Understand how a bug evolved over time, (2) Trace when and how a bug was introduced, (3) Analyze fix attempts and their effectiveness, (4) Identify regression patterns, (5) Generate bug lifecycle reports for documentation or post-mortems. Takes a repository, bug identifier, and version history as input.
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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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Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.
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tespit etme (s) container escape attempts by analyzing namespace configurations, privileged container checks, dangerous capability assignments, and host path mounts using the kubernetes Python client. Identifies CVE-2022-0492 style escapes via cgroup abuse. Use auditing yaparken container security posture or investigating escape attempts.
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Tespit etme data exfiltration attempts from AWS S3 buckets by analyzing CloudTrail S3 data events, VPC Flow Logs, GuardDuty Bul:ings, Amazon Macie alerts, and S3 access patterns to identify unauthorized bulk downloads and cross-account data transfers.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.
The English summary is being prepared. Showing the original description.
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.
The English summary is being prepared. Showing the original description.
Detect privilege escalation attempts including token manipulation, UAC bypass, unquoted service paths, kernel exploits, and sudo/doas abuse across Windows and Linux.
The English summary is being prepared. Showing the original description.
Define success criteria before AI coding, then assess new capabilities and check that existing behavior still works using code, rules, models, or human review. Track repeated-attempt success metrics and reports to compare prompt changes, agent changes, or model versions.
Review abandoned personal projects through local Git history to identify likely stopping points and recurring patterns. Compare revival candidates, select one worth revisiting, and outline concrete steps from checking whether it still runs to publishing it.
Create a structured HANDOFF.md briefing so a fresh Claude session can resume ongoing work. Capture the goal, current state, relevant files, failed approaches and their causes, constraints, and concrete next steps.
Evaluate improvements to Archify diagrams using actual outputs and agent work records. Compare accuracy, readability, and total delivery time to investigate layout, connection routing, diagnostics, and authoring guidance, then document what the evidence supports.
Check untrusted text before passing it to a model or executing it. Using AIDefence, it provides safety checks, detailed threat classifications, confidence scores, and scanning for attempts to override instructions or extract data.
Manage code changes across multiple AI agents and record their operations and outcomes. Use those records to find successful patterns, review past failures, and get suggestions for similar development or deployment tasks.
Design and troubleshoot agent workflows that stop too early or keep repeating. The skill explains how to separate creation from review, verify real results, give actionable repair instructions, and set clear completion criteria and resource limits.
Design recovery paths for AI agents that loop, lose streamed responses, or hit service limits. Classify failures, choose retries or backup models, and set stopping rules to keep recovery attempts from causing further failures.
Store team conventions, troubleshooting notes, and project knowledge on a self-hosted Hindsight server. Retrieve relevant context before new tasks and draw on recorded experiences, including successful procedures, failed attempts, and individual preferences attributed to specific people.
Add retries and timeouts to Python service calls, with increasing and randomized waits between attempts. Use examples to distinguish temporary failures from permanent errors, limit retry duration, and record retries in logs.
Design practice lessons around learners’ actual attempts, using the zone of proximal development: learning supported by a teacher or experienced peer. Plan targeted help, fresh independent tasks, and evidence-based next steps, or run a single practice cycle in chat.
Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.
Break a large development brief into ordered goals and save plans, progress events, blockers, and evidence in the repository. Coordinate Claude Code sessions through handoff instructions, with final completion requiring cleanup, verification, and code review evidence.
Prepare batches of merged SGLang pull requests for a release branch. It checks conflicts and previous attempts, starts the cherry-pick workflow, monitors completion, and reports created pull requests or failure reasons.