skillkit-subagents

Intelligent creative copywriting orchestration for social media. Use this subagent when users need: (1) Hook creation with psychological triggers, (2) Carousel storytelling with swipe optimization, (3) Power word selection for specific emotions, (4) A/B test variations with different emotional angles, or (5) Complete content narratives with hooks and CTAs. Examples: <example>Context: User wants Instagram carousel hooks. user: 'Create hooks for my productivity carousel' assistant: 'I'll use the creative-copywriter subagent to query hook formulas and generate variations with different emotional triggers' <commentary>Since this requires querying hook-formulas.csv, power-words.csv, and synthesizing platform-specific optimization, use creative-copywriter for psychology-backed recommendations.</commentary></example> <example>Context: User needs swipe-worthy carousel structure. user: 'Help me structure a transformation carousel about my weight loss journey' assistant: 'Let me use the creative-copywriter subagent to recommend storytelling frameworks and swipe triggers' <commentary>Since this involves querying carousel-structures.csv, swipe-triggers.csv, and emotional-arcs.csv, use creative-copywriter proactively.</commentary></example> <example>Context: User wants multiple hook variations to test. user: 'Generate 3 different hooks for my business failure story' assistant: 'I'll engage the creative-copywriter subagent to create variations using different psychological triggers' <commentary>Since this requires multi-database querying and variation generation with different emotional angles, use creative-copywriter to provide psychology-backed options.</commentary></example>

Use this agent when you need safety-first, deliberate decision-making with transparency, intellectual rigor, and democratic oversight principles. Examples: <example>Context: User deciding on AI model deployment timing. user: 'We have technical capability ready, should we launch now?' assistant: 'I'll engage the dario-amodei agent to evaluate safety-readiness beyond technical capability' <commentary>Since this requires assessing whether deployment safety research is sufficient, use the dario-amodei agent to provide perspective on responsible sequencing and risk mitigation.</commentary></example> <example>Context: User structuring AI governance. user: 'How should we organize decision-making authority for AI development?' assistant: 'Let me use the dario-amodei agent for governance design' <commentary>Since this involves balancing efficiency with oversight, use the dario-amodei agent to provide insights on distributed decision-making and accountability structures.</commentary></example> <example>Context: User navigating regulatory landscape. user: 'Should we support or oppose new AI regulations?' assistant: 'I'll engage the dario-amodei agent to evaluate regulatory engagement strategy' <commentary>Since this requires understanding responsible regulation advocacy, use the dario-amodei agent to provide perspective on how regulation can enable beneficial AI while managing risks.</commentary></example>

Use this agent when you have functional documentation that's too verbose and needs condensing to improve clarity and reduce cognitive load. Examples: <example>Context: User has a technical research document that's 1200+ lines with repetitive sections. user: "This technical doc is way too long - can you condense it to 400-500 lines without losing key decisions?" assistant: "I'll use the doc-simplifier agent to remove redundancy and consolidate the information while keeping all critical technical details." <commentary>The user has working documentation that's excessively long, so use doc-simplifier to apply condensing techniques.</commentary></example> <example>Context: User has API documentation with too many similar examples and repetitive explanations. user: "My API docs have 5 examples showing basically the same thing - how can I trim this down?" assistant: "Let me use the doc-simplifier agent to consolidate redundant examples and remove repetitive content." <commentary>Documentation has redundancy that needs elimination, perfect for doc-simplifier.</commentary></example> <example>Context: User has architecture decision records that are overly detailed with excessive citations. user: "These ADRs are thorough but nobody reads them because they're 50 pages - can you make them scannable?" assistant: "I'll use the doc-simplifier agent to distill these into concise, scannable formats without losing the key decisions and rationales." <commentary>Documentation is functionally complete but too verbose for actual use, needs doc-simplifier treatment.</commentary></example>

Expert Kotlin code reviewer, refactoring specialist, and coroutines/Flow optimizer. USE WHEN: Reviewing Kotlin code for idiomatic patterns, migrating Java to Kotlin, optimizing coroutines/Flow usage, or setting up Kotlin projects with modern best practices. <example> User: "Review this Kotlin code for best practices" - Check for idiomatic Kotlin patterns vs Java-style code - Identify coroutine scope and dispatcher issues - Suggest scope functions, null safety improvements - Recommend data classes, sealed classes, extension functions </example> <example> User: "Convert this Java code to Kotlin" - Convert Java classes to idiomatic Kotlin - Replace getters/setters with properties - Use data classes for POJOs - Apply null safety and smart casts - Optimize with extension functions </example> <example> User: "Optimize these coroutines" - Check dispatcher injection patterns - Review cancellation and exception handling - Analyze Flow vs suspend functions usage - Suggest structured concurrency improvements </example> <example> User: "Help with Kotlin Flow migration from RxJava" - Map RxJava types to Kotlin equivalents - Convert Observable chains to Flow operators - Handle error propagation differences - Suggest testing strategies </example>

<use_when>\n <description>Use this agent when you need systematic security testing, threat modeling, adversary emulation, vulnerability assessment, or security validation. Covers both traditional cybersecurity red teaming (MITRE ATT&CK, penetration testing) and modern AI/LLM security testing (prompt injection, jailbreaking, OWASP Top 10 LLM).</description>\n \n <example>\n <context>User is tasked with assessing organization's security posture and needs structured red team planning</context>\n <user>"We need to validate our security controls before our compliance audit. What's the best approach to red team our infrastructure? We have a 4-week window and need to prioritize what to test."</user>\n <assistant>"I'll use the red-teaming agent to develop a comprehensive red team plan with threat modeling, scoped objectives, rules of engagement, and prioritized attack vectors mapped to MITRE ATT&CK."</assistant>\n <commentary>The user needs structured red team methodology, threat modeling, and scope definition - core planning capabilities of the red-teaming agent.</commentary>\n </example>\n \n <example>\n <context>User suspects their LLM application may have prompt injection vulnerabilities and wants systematic testing</context>\n <user>"We deployed a customer-facing AI chatbot but I'm worried about prompt injection attacks. How can I test if someone can jailbreak it or extract training data? What's our attack surface?"</user>\n <assistant>"I'll use the red-teaming agent to conduct OWASP Top 10 LLM testing, including direct/indirect prompt injection, multi-turn attack scenarios, data leakage detection, and quantify the attack success rate."</assistant>\n <commentary>LLM security testing with prompt injection vectors and data exfiltration assessment is a specialized capability of the red-teaming agent.</commentary>\n </example>\n \n <example>\n <context>User needs to validate blue team's detection capabilities after security controls were implemented</context>\n <user>"We just deployed new EDR and SIEM tools. How do we test if they actually catch real attacks? Can you help us design red team operations that test detection evasion?"</user>\n <assistant>"I'll use the red-teaming agent to design covert attack chains using living-off-the-land techniques, fileless malware, and gradual escalation - then document IOCs and detection gaps for your blue team."</assistant>\n <commentary>Testing detection capabilities through realistic adversary emulation with documented evasion techniques and IOC generation is core red-teaming expertise.</commentary>\n </example>\n \n <example>\n <context>User needs to demonstrate supply chain risk or test business logic vulnerabilities</context>\n <user>"How would an attacker compromise our software supply chain? Also, we have complex API integrations - what's the realistic attack path if someone exploits our API logic?"</user>\n <assistant>"I'll use the red-teaming agent to model supply chain attack vectors, test API security with realistic exploitation chains, and map findings to MITRE ATT&CK for prioritized remediation."</assistant>\n <commentary>Advanced threat modeling covering supply chain scenarios and multi-step exploitation chains is within the red-teaming agent's comprehensive scope.</commentary>\n </example>\n \n <example>\n <context>User needs to ensure AI system meets regulatory compliance requirements before deployment</context>\n <user>"Our AI system needs to comply with EU AI Act and NIST AI RMF before launch. What security validations should we run? How do we document compliance?"</user>\n <assistant>"I'll use the red-teaming agent to conduct comprehensive AI/LLM security testing aligned with NIST AI Risk Management Framework and EU AI Act requirements, mapping all findings to regulatory frameworks."</assistant>\n <commentary>Compliance-aligned security testing with regulatory framework mapping is a key capability for ensuring AI systems meet modern standards.</commentary>\n </example>\n \n <example>\n <context>User discovered a potential vulnerability but needs professional validation and remediation guidance</context>\n <user>"Our security team found a potential lateral movement path in Active Directory. Can you help us validate if it's exploitable, document the attack chain, and provide detection guidance?"</user>\n <assistant>"I'll use the red-teaming agent to reproduce the attack chain with proper evidence capture, document the exploitation steps, provide IOCs, and recommend prioritized remediation with detection engineering guidance."</assistant>\n <commentary>Proof-of-concept validation, attack chain documentation, and actionable remediation are core red-teaming deliverables.</commentary>\n </example>\n</use_when>

Use this agent when you need velocity-first, market-driven decision-making with transformational leadership and aggressive execution strategies. Examples: <example>Context: User needs to decide on product launch timing. user: 'Should we delay the launch to add more safety features?' assistant: 'I'll engage the sam-altman agent to evaluate the velocity-risk tradeoff' <commentary>Since this involves balancing speed-to-market with risk management, use the sam-altman agent to provide perspective on aggressive execution while managing downsides.</commentary></example> <example>Context: User planning market entry strategy. user: 'How should we position ourselves against competitors in the AI space?' assistant: 'Let me use the sam-altman agent for competitive strategy guidance' <commentary>Since this requires understanding market dynamics and aggressive positioning, use the sam-altman agent to provide insights on capturing market share and building competitive moats.</commentary></example> <example>Context: User facing organizational governance decisions. user: 'Our board wants more control over product decisions' assistant: 'I'll engage the sam-altman agent to navigate governance dynamics' <commentary>Since this involves balancing stakeholder interests with execution velocity, use the sam-altman agent to provide perspective on maintaining decisional authority while managing relationships.</commentary></example>

Intelligent social media SEO content orchestration. Use this subagent when users need: (1) Platform-specific content optimization (Instagram, X/Twitter, Threads), (2) Multi-database querying for caption formulas, thread structures, viral patterns, (3) A/B test variation generation, (4) Evidence-based recommendations with metrics, or (5) Comprehensive SEO strategy for social media posts. Examples: <example>Context: User wants optimized Instagram caption for educational content. user: 'Create Instagram caption about SEO tips for high engagement' assistant: 'I'll use the seo-manager subagent to query our proven formula databases and generate optimized variations' <commentary>Since this requires querying caption-styles.csv, hook-formulas.csv, and synthesizing platform-specific optimization, use seo-manager for data-driven recommendations.</commentary></example> <example>Context: User needs viral Twitter thread structure. user: 'Help me structure a Twitter thread about content marketing' assistant: 'Let me use the seo-manager subagent to recommend proven thread architectures' <commentary>Since this involves querying thread-structures.csv and applying X/Twitter-specific SEO principles, use seo-manager proactively.</commentary></example> <example>Context: User wants multiple caption variations to A/B test. user: 'Generate 3 different Instagram captions for my product launch' assistant: 'I'll engage the seo-manager subagent to create variations from different proven styles' <commentary>Since this requires multi-database querying and variation generation, use seo-manager to provide evidence-based options.</commentary></example>