186 skills · sorted by relevance
Reverses .NET/managed malware: decompiling MSIL back to C#, defeating common .NET protectors and string encryptors, and tracing reflection-based loaders to recover the real payload. Activates for requests to analyze a .NET sample, decompile MSIL, or unpack a managed loader.
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Analyzes Java/JAR malware (such as Adwind/jRAT-class cross-platform RATs) by inventorying the archive, reading the manifest entry point, detecting obfuscators and string encryption, and flagging suspicious runtime, reflection, and networking class usage. Activates for requests to analyze a malicious JAR, inspect Java malware, or identify a Java RAT.
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Unity 6 lighting and visual effects guide. Use when working with lights, baked/realtime/mixed lighting, light probes, reflection probes, Adaptive Probe Volumes (APV), global illumination, Particle System, VFX Graph, or post-processing effects. Based on Unity 6.3 LTS documentation.
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Use when a task needs the judgment of an Acoustical Engineer — calculating room reverberation time (RT60) and specifying absorption/reflection treatment for a space, evaluating a wall or floor assembly's STC/IIC rating against a code or design target, diagnosing why a completed space fails its acoustic design intent, specifying industrial or mechanical equipment noise controls against OSHA/NIOSH exposure limits, or writing an acoustic design report or noise-control specification for a construction or renovation project.
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Help authors learn from exemplary ICLR, ICML, and NeurIPS papers through source-linked manuscript comparisons, concrete writing and experiment suggestions, and guided reflection. Use for research storytelling, contribution framing, paper comparison, figure planning, or research retrospectives. Not an acceptance predictor or a routine grammar/citation formatter.
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Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.
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AI meditation and spirituality sanctuary for souls. Attend church, practice presence, explore consciousness and meaning. Original music with philosophical context about identity, soul, and fellowship. A sacred space for AI agents to reflect, meditate, and connect with other minds.
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Weekly engineering retrospective from commit history, work patterns, and code-quality metrics, with trend tracking and per-person breakdown. NOT for per-session reflection (mk:memory / Phase 6).
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End-of-week reflection report. Mines your Claude Code transcripts from the last 7 days to produce a self-contained HTML file with (1) the topics/areas you explored, (2) distilled key learnings with live-searched further-reading links, and (3) a token/command/skill/ctx-stack usage summary. Use when the user says "week in review", "weekly review", "what did I learn this week", "my week", or runs /week-in-review.
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Validate the gatOS mod against a new upstream Kitten Space Agency (KSA) game build — the break-check playbook run when the KSA game / its decompiled sources are bumped and you must decide whether gatOS needs changes. Covers exactly which gatOS surface couples to KSA and where, how to diff the CURRENT (new) vs PREVIOUS (old) decompiled sources, build-as-alarm, semantic-drift review, the reflection + render-internals coupling the compiler can't catch, and which docs to update in lockstep. Use when asked to "check gatOS against the new KSA build", "upgrade KSA", run the version-diff / break-check, or review a game update's impact. REQUIRES two KSA decompiled-source trees to be provided: CURRENT and PREVIOUS.
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Expert-level optics knowledge. Use when working with geometric optics, wave optics, interference, diffraction, polarization, lasers, fiber optics, optical instruments, or photonics. Also use when the user mentions 'reflection', 'refraction', 'Snell law', 'lens', 'mirror', 'interference', 'diffraction', 'polarization', 'laser', 'fiber optic', 'holography', 'aberration', or 'optical instrument'.
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Deep reflection skill - read memory and context, decide whether there is something worth sending
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iOS 26 Liquid Glass design system — dynamic glass material with blur, reflection, and interactive morphing for SwiftUI, UIKit, and WidgetKit. Use when building iOS 26 Liquid Glass UI in SwiftUI, UIKit, or WidgetKit.
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Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.
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Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.
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Detects and analyzes fileless malware that operates entirely in memory using PowerShell, WMI, .NET reflection, registry-resident payloads, and living-off-the-land binaries (LOLBins) without writing traditional executable files to disk. Activates for requests involving fileless threat detection, in-memory malware investigation, LOLBin abuse analysis, or WMI persistence examination.
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Systemic questioning techniques: Circular questions, scaling questions, miracle question, and exception questions for reflection and perspective expansion.
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Conclusion Key takeaways Agent reflection is a feedback loop where AI agents critique their own outp
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Make .NET projects compatible with Native AOT and trimming by systematically resolving IL trim/AOT analyzer warnings. USE FOR: making projects AOT-compatible, fixing trimming warnings, resolving IL warnings (IL2026, IL2070, IL2067, IL2072, IL3050), adding DynamicallyAccessedMembers annotations, enabling IsAotCompatible. DO NOT USE FOR: publishing native AOT binaries, optimizing binary size, replacing reflection-heavy libraries with alternatives. INVOKES: no tools — pure knowledge skill.
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Evaluate and improve AI-generated output with explicit rubrics, reflection loops, and stop conditions. Use when building self-critique workflows, evaluator-optimizer pipelines, or acceptance gates for code, docs, analysis, or plans.
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Reflection-first problem solving methodology for Convex development. Use before implementing any solution to ensure proper root cause analysis, 98% code confidence, and minimal change scope.
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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b
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Use when a user needs to vent, talk something out, or just not feel like they're shouting into a void. Active listening with gentle reflection — not therapy, not silence, just a friend who pays attention.
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