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Found 1,943 Skills
Use after the final approved execution scope is complete, or when the user asks whether a feature is done, ready to ship, safe to merge, or needs a quality check. Runs the post-execution quality gate: specialist review, artifact verification, and human UAT against locked decisions and the final exit state. Use for prompts like "review this feature", "is this done?", "can we ship this?", "double-check the implementation", or "run UAT".
Designs structured benchmarks for comparing algorithms, models, or implementations. Selects appropriate metrics (latency, throughput, memory, accuracy), designs representative test cases, captures hardware/software context, produces comparison tables with tradeoff analysis, and includes reproduction instructions. Triggers on: "benchmark", "compare performance", "which is faster", "latency comparison", "memory comparison", "run benchmark", "design benchmark", "compare implementations", "evaluate algorithms", "performance comparison", "throughput test", "speed test". Use this skill when comparing two or more implementations, algorithms, or models.
Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, automated anti-pattern detection, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.
Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
Token-efficient persistent memory system for Claude Code that extends your session limits by 3-5x. Layered architecture with progressive loading, compact encoding, branch-aware context, smart compression, session diffing, conflict detection, session continuation protocol, and recovery mode. Activates at session start (if MEMORY.md exists), on "remember this", "pick up where we left off", "what were we doing", "wrap up", "save progress", "don't forget", "switch context", "hand off", "memory health", "save state", "continue where I left off", "context budget", "how much context left", or any session start on a project with existing memory files. This skill solves two problems at once: Claude forgetting everything between sessions, AND sessions hitting context limits too fast. It replaces thousands of wasted re-explanation tokens with a compact, structured memory load that gives Claude full project context in under 2,000 tokens.
Use when measuring or improving agent quality and performance — set up evaluators, online monitoring, CI/CD quality gates, observability, or cost optimization. Triggers on: "evaluate my agent", "add evaluator", "measure quality", "quality gate", "run evals", "agent too slow", "why is it slow", "reduce latency", "set up observability", "CloudWatch dashboard", "how much does my agent cost", "cost optimization", "logs not showing up", "logs missing", "spans not found", "eval failing", "eval error", "dev traces", "local traces", "agentcore dev traces", "traces to CloudWatch". Not for debugging errors or crashes — use agents-debug. Slow but correct routes here; broken routes to debug.
Discounted cash flow (DCF) valuation model built from Longbridge financial data — historical FCF (operating cash flow minus capex), projected FCF with growth assumptions, WACC (Beta / risk-free rate / equity risk premium), terminal value, intrinsic value vs current price, and margin of safety. Triggers: "DCF", "现金流折现", "内在价值", "自由现金流", "WACC", "折现率", "安全边际", "终值", "现金流贴现", "現金流折現", "內在價值", "自由現金流", "折現率", "安全邊際", "DCF model", "discounted cash flow", "intrinsic value", "free cash flow", "WACC", "discount rate", "margin of safety", "terminal value", "Gordon growth".
Migrate an application with hardcoded LLM prompts to a full LaunchDarkly AgentControl implementation in five stages: audit the code, wrap the call, move the tools, add tracking, attach evaluators. Use when the user wants to externalize model/prompt configuration, move from direct provider calls (OpenAI, Anthropic, Bedrock, Gemini, Strands) to a managed config, or stage a full hardcoded-to-LaunchDarkly migration.
Analyzes events through environmental lens using ecological principles, systems thinking, sustainability frameworks, and conservation biology to assess ecosystem health, biodiversity impacts, and long-term environmental sustainability. Provides insights on climate change, resource management, pollution, habitat conservation, and human-nature relationships. Use when: Environmental policy, climate decisions, conservation planning, resource extraction, pollution assessment. Evaluates: Ecosystem health, biodiversity, sustainability, climate impacts, carrying capacity, environmental justice.
Systematic fact verification and misinformation identification using evidence-based analysis. Use when: verifying claims, checking facts, identifying misinformation, evaluating source credibility, or when user asks to "fact check", "verify", "is this true", or mentions claims that need validation.
B2B go-to-market strategy, pricing models, ICP development, positioning, and competitive intelligence. Use when planning GTM strategy, setting pricing, defining ICP, or evaluating opportunities.
Retrieve historical market capitalization data for any stock using Octagon MCP. Use when tracking market cap changes over time, analyzing valuation trends, identifying peak and trough valuations, and comparing historical size classifications.