「reflection」の検索結果

Server-Side Template Injection (SSTI) occurs when user input is embedded directly into a template engine (Jinja2, Twig, Freemarker, Pebble, Velocity, Smarty, Mako) and evaluated, enabling remote code execution. Detect via math expressions `{{7*7}}` returning `49`, or `${7*7}`, `<%= 7*7 %>`. Leads to full RCE via template sandbox escape, Python `__class__.__mro__` traversal, and Java reflection chains. Tools: tplmap, Burp Suite.

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CORS misconfiguration allows attacker-controlled origins to read sensitive cross-origin responses when servers echo the `Origin` header in `Access-Control-Allow-Origin` or set it to `*` with `Access-Control-Allow-Credentials: true`. Detect via `Origin: https://attacker.com` reflection in `Access-Control-Allow-Origin` response header, wildcard `*` on credentialed endpoints, and null origin acceptance. Tools: OWASP ZAP, Burp Suite, manual `fetch()` with `credentials: include`.

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CORS misconfiguration testing playbook. Use when analyzing cross-origin trust, credentialed browser reads, origin reflection, preflight policy bugs, and browser-based access to authenticated APIs.

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Bypass the Windows Antimalware Scan Interface (AMSI) using memory patching, reflection, and obfuscation techniques. Execute undetected PowerShell, VBScript, JScript, and .NET assemblies in-memory without triggering Microsoft Defender or third-party AV/EDR solutions. Use this skill during Red Team engagements when loading offensive tools (Mimikatz, Rubeus, SharpHound) in memory on defended Windows endpoints.

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Deep API security assessment beyond surface scanning. Covers the full OWASP API Security Top 10 (2023): Broken Object Level Authorization (BOLA / IDOR), Broken Authentication, Broken Object Property Level Authorization (mass assignment + excessive data exposure), Unrestricted Resource Consumption, Broken Function Level Authorization (BFLA / vertical privilege escalation), Unrestricted Access to Sensitive Business Flows, Server-Side Request Forgery via API parameters, Security Misconfiguration, Improper Inventory Management (shadow/zombie/deprecated endpoints, v1/v2 drift), and Unsafe Consumption of third-party APIs. Works across REST, GraphQL, gRPC, SOAP, and MCP servers. Discovers APIs from OpenAPI/Swagger specs, GraphQL introspection, gRPC reflection, .well-known endpoints, JS bundles, and traffic capture. Uses kiterunner, ffuf, schemathesis, restler-fuzzer, openapi-fuzzer, graphql-cop, clairvoyance, batchql, inql, jwt_tool, postman, mitmproxy, and manual http(action="request", ...) payloads. Every technique includes actual payloads, commands, and verification logic. Chains from /pentester or /codebase when API endpoints are discovered, chains into /web-exploit when classic injection points are found in API parameters, chains into /post-exploit when RCE is achieved, and chains into /ai-redteam when an LLM/AI endpoint is discovered (chat APIs, completion endpoints, RAG search, agentic tool-use, MCP servers).

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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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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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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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A pattern catalog for autonomous agents — ReAct, plan-and-execute, reflection, multi-agent debate, toolformer-style tool use, and when each fits. Use when choosing the control pattern for an agent task.

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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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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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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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Deep reflection skill - read memory and context, decide whether there is something worth sending

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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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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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