Autoscaling Configuration

Build terminal user interfaces with Go and Bubbletea framework. Use for creating TUI apps with the Elm architecture, dual-pane layouts, accordion modes, mouse/keyboard handling, Lipgloss styling, and reusable components. Includes production-ready templates, effects library, and battle-tested layout patterns from real projects.

GNU Make skill for C/C++ build systems. Use when writing or debugging Makefiles, understanding pattern rules and automatic dependency generation, managing CFLAGS/LDFLAGS, converting ad-hoc compile commands into maintainable Makefiles, or diagnosing incremental build issues. Activates on queries about Makefiles, make targets, pattern rules, phony targets, dependency tracking, recursive make, or make errors.

Build targeted prospect lists by analyzing LinkedIn profiles, extracting job titles, companies, locations, and recent activity. Identifies decision-makers, tracks job changes for warm outreach, and enriches contact data. Use when users need to find prospects, build lead lists, or track decision-maker movements.

End-to-end research ideation pipeline: literature grounding → multi-track idea generation (3 personas: innovator/pragmatist/critic) → iterative refinement → ELO tournament ranking → update evo-memory (IDE) → user selects direction → expand into manuscript-quality proposal. Use when: user wants to find a research direction, brainstorm ideas, evaluate idea novelty, design a novel solution, rank/compare research ideas, or generate a research proposal. Do NOT use for finding/searching/reading papers (use paper-navigator), literature survey reports (use research-survey), or planning a paper (use paper-planning).

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.

Guide post-trade compliance monitoring and trade surveillance system design. Use when building alert logic to detect churning, front-running, cherry-picking, layering, spoofing, wash trading, or marking the close, implementing post-trade best execution review, evaluating allocation fairness with pro-rata verification or dispersion analysis, designing exception-based monitoring workflows with escalation paths, correlating trading with MNPI events for insider trading detection, building personal trading surveillance for preclearance and blackout enforcement, determining SAR or blue sheet or CAT reporting triggers, or tuning surveillance thresholds to reduce false positives. Also covers turnover ratios, cost-to-equity ratios, and investigation case management.