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Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
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Configure secure OAuth 2.0 authorization flows including Authorization Code with PKCE, Client Credentials, and Device Authorization Grant. This skill covers flow selection, PKCE implementation, token
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Analyzes encryption algorithms, key management, and file encryption routines used by ransomware families to assess decryption feasibility, identify implementation weaknesses, and support recovery efforts. Covers AES, RSA, ChaCha20, and hybrid encryption schemes. Activates for requests involving ransomware cryptanalysis, encryption analysis, key recovery assessment, or ransomware decryption feasibility.
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Visual storytelling skill chain for DSH; use for narrative motion, scroll-driven UX, and stack-aware implementation rails.
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Implement a task N ways in parallel and pick the best. Spawns multiple subagents in isolated worktrees, evaluates all candidates, and applies the winner. Use when asked to "best of n", "try multiple approaches", "parallel implementations", "/best-of-n", or "/bon".
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Follow-up scout for the Signals inbox itself. Watches reports that recently transitioned to resolved (an implementation PR merged) and, after a deployment soak window, re-measures the underlying problem to check the fix actually held — plus a strictly-gated escalation check on recently dismissed reports. Emits findings only when a shipped fix demonstrably didn't hold; confirmations and unverifiable verdicts become durable memory and an empty close-out. Self-contained peer in the signals-scout-* fleet — no dependencies on other skills.
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Add feature flags to an application for gradual rollouts, A/B testing, and kill switches using PostHog, LaunchDarkly, or a simple local implementation.
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Recall project-first and global-supplement ChatCrystal memories before substantive implementation, refactoring, migration, configuration, investigation, or optimization work. Use when the task is non-trivial, has repository or project context, and prior fixes, decisions, pitfalls, or reusable patterns may change the approach.
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Write reusable ChatCrystal task memories after substantive work completes. Use when implementation or debugging produced a durable fix, pitfall, pattern, or decision worth preserving, and when the environment can either persist it through `write_task_memory` or emit a structured memory candidate for later save.
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Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
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Fast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
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Create high-quality ToolUniverse skills following test-driven, implementation-agnostic methodology.
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Create standout, technically ambitious interfaces that feel extraordinary through cinematic transitions, advanced motion, heavy rendering, or other high-ambition implementation work. Use when the user wants to wow, impress, go all-out, or push a feature beyond conventional UI polish—not when simple visual confidence or routine cleanup is enough.
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Investigate errors, incidents, or product ideas using both internet research and repo-wide codebase research, then propose a concrete changeset plan with clarifying questions before any implementation. Use when a user asks to investigate, root-cause, or plan changes, or when an Issue or PR is referenced and needs a plan.
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Senior software engineer for story execution and code implementation. Use when the user asks to talk to Elliot or requests the developer agent.
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Execute story implementation following a context filled story spec file. Use when the user says "dev this story [story file]" or "implement the next story in the sprint plan"
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Fast-forward through OpenSpec artifact creation. Use when the user wants to quickly create all artifacts needed for implementation without stepping through each one individually.
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Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
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Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
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Roslyn-powered semantic query CLI for C# solutions. Use when you need to find references, callers, implementations, dependencies, members, or trace call chains in a C# codebase — replaces multi-round grep/read cycles with single precise queries.
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Review code changes against linked GitHub issue specs to catch implementation drift — building something plausible but wrong. Checks each acceptance criterion against actual code.
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Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.
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Survey a codebase's animation and motion code as a senior motion advisor, then produce a prioritized audit and self-contained implementation plans for other agents (or cheaper models) to execute. Read-only on source code — it plans improvements, it does not apply them. Use when the user asks to "improve the animations", "audit the motion", "make this app feel better", or wants a roadmap of animation fixes rather than a review of a single diff.
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Progressively activates todo tests one at a time, updates implementation code until each passes (verified via Wallaby), checks off matching design doc progress, then moves to the next—following the design doc as the single source of truth.
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