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Found 109 Skills
Learn how to manage conversation context in AMCP to avoid LLM API errors from exceeding context windows. This skill covers SmartCompactor strategies, token estimation, configuration, and best practices.
Precise, instant code structure queries for active development — answer 'who depends on this interface before I refactor it', 'how many modules break if I change this', 'what is the real impact radius of this feature change', 'which module is the true high-coupling hotspot in this legacy codebase'. Essential before any interface change, continuous refactoring task, sprint work estimation, or when navigating unfamiliar or large legacy codebases. Requires Python 3.10+ and shell. Use nexus-mapper instead when building a full .nexus-map/ knowledge base.
Use this skill when writing user stories, defining acceptance criteria, story mapping, grooming backlogs, or estimating work. Triggers on user stories, acceptance criteria, story mapping, backlog grooming, estimation, story points, INVEST criteria, and any task requiring agile requirements documentation.
This skill should be used when the user asks about 'TRON energy', 'TRON bandwidth', 'how much energy do I need', 'energy cost on TRON', 'bandwidth insufficient', 'resource delegation on TRON', 'rent energy on TRON', 'TRON transaction fee', 'why is my TRON transaction expensive', 'optimize TRON costs', or mentions Energy, Bandwidth, resource management, fee estimation, or cost optimization on the TRON network. This is a TRON-specific concept with no direct equivalent on EVM chains. Do NOT use for staking/voting — use tron-staking. Do NOT use for balance queries — use tron-wallet.
Generate a Software Maintenance Plan (SMP) and supporting maintenance documentation for SDLC projects. Compliant with ISO/IEC/IEEE 14764:2022. Covers Maintenance Strategy, MR/PR handling workflow, CCB process, maintenance cost estimation, and all...
Use after backlog decomposition to define prioritization, MVP and release slices, sequencing, readiness, traceability, and JIRA-ready outputs, then score backlog quality for planning and estimation. Supports `--quick-flow` for fast assumption-driven execution and `--full-flow` for question-driven verified execution. Explicit full or end-to-end spec refinement requests continue through the existing 18-stage refinement cascade.
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.
This skill provides AWS cost optimization, monitoring, and operational best practices with integrated MCP servers for billing analysis, cost estimation, observability, and security assessment.
Strategic analyst that maps competitive landscapes, identifies white space opportunities, and provides positioning recommendations. Use when users need competitive analysis, market positioning strategy, differentiation tactics, or "how do I stand out?" guidance across any domain (portfolios, products, services). NOT for market size estimation or financial forecasting.
Scan codebases for technical debt, score severity, track trends, and generate prioritized remediation plans. Use when users mention tech debt, code quality, refactoring priority, debt scoring, cleanup sprints, or code health assessment. Also use for legacy code modernization planning and maintenance cost estimation.
Competitive landscape analysis — builds a competitive structure research framework covering market positioning (Porter five-forces), peer cross-comparison (PE/PB/ROE/revenue growth), market share estimation, competitive advantage assessment (moat), and potential disruptor identification. Triggers: "竞争格局", "竞争分析", "行业竞争", "市场份额", "竞争对手", "护城河", "波特五力", "竞争优势", "競爭格局", "競爭分析", "行業競爭", "市場份額", "競爭對手", "護城河", "波特五力", "competitive analysis", "competitive landscape", "market share", "competitive moat", "Porter five forces", "industry competition", "competitive advantage", "market positioning", "moat analysis", "NVDA vs AMD", "who are the competitors".
Trade execution modelling framework (backtesting analysis only) via Longbridge — covers slippage models (linear / square-root market impact), VWAP/TWAP execution logic, market impact cost estimation (Kyle lambda), volume participation rate (POV) strategy. Helps quant traders build realistic execution assumptions in backtests. Triggers: "执行模型", "滑点模型", "VWAP执行", "TWAP执行", "市场冲击", "执行成本", "成交量参与率", "交易执行", "執行模型", "滑點模型", "VWAP執行", "TWAP執行", "市場冲擊", "執行成本", "交易執行", "execution model", "slippage model", "VWAP", "TWAP", "market impact", "execution cost", "volume participation rate", "Kyle lambda", "square root model", "POV strategy".