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Implement layered collective defense using alarm signaling, role mobilization, and proportional response. Covers threat detection, alert propagation, immune response patterns, escalation tiers, and post-incident recovery for distributed systems and organizations. Use when designing defense-in-depth where no single guardian covers all threats, building incident response that scales with severity, or when current defense is over-reactive to every alert or under-reactive to genuine threats.

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Detects seven structural anti-patterns in Claude Projects that cause unpredictable output, ignored instructions, and degraded quality. Diagnoses Monolith, Orphan File, Echo Chamber, Phantom Conversation, Kitchen Sink, Misaligned Hierarchy, and Blurred Layers. Use when user says "what's wrong with my project," "Claude ignores my instructions," "diagnose my project," "why is output inconsistent," "review my project setup." Also trigger on symptom-phrased: "Claude doesn't follow my rules," "my instructions keep getting overridden," "my Project isn't behaving as designed." Use alongside rootnode-project-audit if available for deeper structural analysis. Activate whenever the user describes symptoms of unreliable, inconsistent, or degraded Claude Project output, even if they do not name a specific pattern. Do NOT use when the user's primary request is Memory-layer rebalancing (use rootnode-memory-optimization if available) or scoring a single prompt (use rootnode-prompt-validation if available).

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Evaluates whether work-in-design is ready to hand off from a chat design conversation to autonomous execution (Claude Code, n8n, agent runtime). Runs a 7-condition gate: spec stability, verification surface, invariants, pump-primer, work decomposition, rollback cost, token budget headroom. Three invocation modes — deliberate (caller provides work_context, returns JSON verdict), proactive sensing (Claude detects handoff signals in design conversation and offers the check), conversational walkthrough (Claude walks the 7 conditions as discussion topics). Returns structured JSON in all modes. Use when designing a Claude Code deployment, scoping an autonomous run, or deciding next move. Trigger on: "ready to hand off," "ready for execution," "is this CC-ready," "handoff check," "walk me through readiness," "are we ready to ship this," "should I just hand this off," or proactive detection. Do NOT use for evaluating completed work, runtime selection, or prompt quality. Profile-agnostic.

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Vue 3 SFC structure, Composition API + <script setup>, file naming, project layout, props/emits/slots typing, defineModel, composables, lifecycle, watchers, UI library detection. Vue 2 fallback notes. Use this skill to: - Structure a new SFC with `<script setup>`, template, scoped styles. - Type props/emits/slots correctly via macros. - Pick `ref` vs `reactive` (prefer `ref`). - Build composables that compose cleanly. - Apply lifecycle hooks correctly. - Detect UI library (Vuetify/Quasar/PrimeVue/Naive UI/Element Plus/shadcn-vue) and mirror its patterns. Do NOT use this skill for: - State management lib choice (see vue-state-management). - Routing (see vue-routing). - Forms (see vue-forms). - Testing (see vue-testing).

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Maven and Gradle build tool conventions for Java projects: build tool detection, wrapper usage, dependency management, BOMs, plugin configuration, semver, and multi-module layouts. Stack-agnostic — referenced by every Java plugin in the marketplace. Use this skill to: - Detect which build tool is in use and invoke it correctly. - Manage dependencies safely (BOMs, version properties, no wildcard versions). - Configure compiler, test, and code-quality plugins. - Handle multi-module projects. Do NOT use this skill for: - Framework-specific build plugins (Spring Boot Gradle plugin — in spring-boot-plugin:spring-conventions). - CI/CD pipeline config.

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React Native project structure, Expo vs bare workflow detection, app.json/app.config.js patterns, asset handling, fonts, styling approaches, hot-reload-friendly idioms. Use this skill to: - Detect Expo (managed/dev-client/EAS/ejected) vs bare RN workflow. - Pick correct project layout for each workflow. - Configure app.json / app.config.js with bundle ID, splash, icons, deep link schemes. - Handle assets, fonts, images correctly. - Pick a styling approach (StyleSheet / NativeWind / restyle / styled-components). Do NOT use this skill for: - Platform-specific branching (see rn-platform-specific). - Navigation (see rn-navigation). - Storage (see rn-state-and-storage). - Testing (see rn-testing).

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Automate a professional LinkedIn presence end-to-end using Claude: daily posting with pillar calendar, engagement with anti-detection rules, DM triage, reporting, and growth tracking. Use when the user wants to automate LinkedIn, manage a profile with AI, create a content strategy, or set up engagement automation. Triggers on: "automate my LinkedIn", "LinkedIn automation", "automated LinkedIn", "LinkedIn bot", "LinkedIn scheduling", "LinkedIn AI management", "LinkedIn content calendar", "LinkedIn engagement strategy", "build a LinkedIn presence", "can Claude manage my LinkedIn", "how do I automate my LinkedIn", or any request involving systematic LinkedIn content creation, audience growth, or profile management. Covers: identity/TOV setup, pillar calendar, post creation, engagement rules, anti-detection, scheduled task creation, and weekly reporting.

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Analiza textos aplicando pensamiento crítico — el marco de los 8 elementos del pensamiento y los estándares intelectuales universales de Richard Paul y Linda Elder — y detecta las falacias lógicas graves presentes. Úsala siempre que el usuario comparta un texto (pegado o en un archivo) y pida analizarlo críticamente, detectar falacias, evaluar argumentos, identificar sesgos, o preguntas como "¿qué problemas tiene este argumento?", "¿es sólido este razonamiento?", "analiza esto", "dame una crítica". También actívala cuando el usuario comparta un artículo de opinión, un post, un discurso, un ensayo o cualquier texto argumentativo y quiera evaluar su calidad argumentativa.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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Executes comprehensive red team exercises that simulate real-world adversary operations against an organization's people, processes, and technology. The red team operates with stealth as a primary objective, employing the full attack lifecycle from initial reconnaissance through objective completion while testing the organization's detection and response capabilities. This differs from penetration testing by focusing on adversary emulation rather than vulnerability identification. Activates for requests involving red team exercise, adversary simulation, adversary emulation, or full-scope offensive security assessment.

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Use this skill during Phase 6 convergence checks. Provides four-dimensional convergence analysis, hallucination detection, and duplicate finding identification.

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Use when the user asks for a SIGMA detection rule, "write a SIGMA rule for X", or `/hash-investigation` / `/malware-analysis` surfaces behaviour worth a vendor-agnostic detection. Format spec + writing guide.

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Purple team automation skill that links offensive emulation (Atomic Red Team, Caldera, Stratus Red Team, Leonidas) with detection validation, telemetry-gap analysis, and continuous control testing. Use to design adversary emulation plans tied to MITRE ATT&CK, execute safe tests in lab or controlled environments, and produce evidence-backed detection coverage reports.

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Reflect reusable learnings into the current workspace / repository design and automation assets (.github/**, AGENTS.md, repo scripts/tasks). Not for ~/.copilot personal assets, VS Code User Data assets, or a private skills repository. Use when running a workspace/repo retro from CLI or Scout. Detects whether a project folder exists and, if not, asks before creating one. Triggers on "retro workspace", "repo retro", "workspace cleanup", "ワークスペース知見反映".

Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.

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Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing scientific-agent-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.

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"Implement Louvain community detection to discover densely connected groups in networks. Use this skill when the user needs to find communities or clusters in social/organizational networks, segment customers by interaction patterns, or analyze network modular structure — even if they say 'find groups in this network', 'community detection', or 'network clustering'.".

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Audits whether a machine-readable regelrecht law model is faithful to the LETTER of the wettekst — and strictly separates the wettekst (leading) from the toelichting (Nota/Memorie van Toelichting; explanatory, not a norm). Runs in critical passes per article/lid and detects four deviation classes: toelichting-bleed (a modeled criterion that the letter only states as an open norm, or not at all), missing verbatim elements (a word/condition such as a "tenzij" or a qualifier dropped from the model), wrong legal_basis anchoring (an endpoint hung on a "kapstok"-article while the operative norm lives elsewhere), and verbatim-drift in text: (an ingetrokken/older redaction). Also detects positief-lid vs uitsluitings-lid conflicts and checks the chapeau for an explicit derogation rule ("zo nodig in afwijking van het eerste lid") before judging precedence. Classifies every finding as modelfout (fix), wettekst-gevolg (report, do not fix), or letter-vs-toelichting-question (jurist decides what is leading + revision signal). Use when the user wants to know if a model is "dicht bij de letter", mentions letter vs toelichting/bedoeling, fidelity, "natuurgetrouw", or before trusting feature outcomes.

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Detect and rewrite AI writing patterns to make content sound authentically human. Supports voice calibration from the author's own writing samples. Use when asked to humanize text, remove AI patterns, calibrate voice, audit content for AI detectability, or check if writing sounds like ChatGPT. Triggers: /humanize, /voice-calibrate, /audit-ai

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Web shell samples for detection and analysis: PHP, ASP, ASPX, JSP, Python, Perl shells. Use for security research and detection system testing.

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Detects authentication and biometric bypass vulnerabilities in mobile apps (Android/iOS). Trigger on: BiometricPrompt, LocalAuthentication, LAContext, evaluatePolicy, CryptoObject, Android Keystore, Secure Enclave, kSecAccessControlBiometryCurrentSet, userAuthenticationValidityDurationSeconds, confirmCredentials, biometric fallback, PIN bypass, passive authentication, enrolled biometrics detection, Frida hook auth, jailbreak bypass, TouchID, FaceID, fingerprint. Covers MASVS-AUTH-1/2/3.

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Execute dynamic instrumentation utilizing Frida to inject custom JavaScript into running iOS applications (IPAs) on jailbroken devices. Hook native functions, bypass SSL Pinning, bypass Jailbreak Detection, and manipulate in-memory data at runtime.

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Create and implement Malleable C2 profiles in Cobalt Strike to evade network intrusion detection systems (NIDS/IPS) and endpoint detection architectures. This skill focuses on molding the Beacon's HTTP/HTTPS traffic to resemble legitimate network traffic like Amazon, Google, or jQuery.

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Writes professional bug bounty reports for HackerOne, Bugcrowd, and Intigriti with CVSS 4.0 scoring, business impact, working exploits, and remediation. Runs 5-check Pre-Report Verification first: hallucination detection, AI writing patterns, PoC reproducibility, duplicate detection, and impact plausibility. Use when user describes a vulnerability, shares HTTP logs, HAR files, recon output, or screenshots; says 'write a bug report', 'format my finding', 'draft a vuln report', 'is this valid', 'rate my vulnerability', 'verify my report', or any variant. Trigger for partial or messy input — raw notes, one-liners, or full writeups all work. Do not wait for perfect input.

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