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Found 1,906 Skills
Use when you need to research, analyze, and plan technical solutions that are scalable, secure, and maintainable.
Iteratively improve any output until measurable criteria are met. Use when the user wants to refine existing work against specific standards — whether it's code, prose, data, config, or any other artifact. Triggers on phrases like "improve this", "make it better", "iterate", "refine", "keep improving", "not good enough yet", "optimize this", "polish this", "tighten this up", or when the user provides criteria and wants repeated improvement until they're satisfied. Also use when the user gives feedback on output and expects you to keep refining, even if they don't say "improve" explicitly.
Critiques ML conference papers with reviewer-style feedback. Use when users want to anticipate reviewer concerns, identify weaknesses, check claim-evidence gaps, or find missing citations.
Autonomously optimize an existing AI skill by running it repeatedly against binary evals, mutating one instruction at a time, and keeping only changes that improve pass rate. Based on Karpathy-style autoresearch, but applied to SKILL.md iteration instead of ML training. Use when optimizing a skill, benchmarking prompt quality, building evals for a skill, or running self-improvement loops on reusable agent instructions. Triggers on: skill-autoresearch, optimize this skill, improve this skill, benchmark this skill, eval my skill, run autoresearch on this skill, self-improve skill.
This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits. A core context engineering skill — also activates when the user mentions "context engineering" or "context-engineering" in the context of managing token budgets and session longevity.
Graham cigar-butt (NCAV / net-net) single-stock diagnostic. Combines a 100-point static cheapness score (NCAV, PE, PB, dividend yield, debt coverage, earnings stability) with a dynamic adjustment layer (industry cycle, earnings trend, insider activity, NCAV trajectory) to separate real bargains from value traps. Pulls data from Longbridge CLI/MCP first, falls back to WebSearch only for gaps, runs cross-statement reconciliation (勾稽校验) before scoring, and footnotes every figure to its source. Triggers: "格雷厄姆", "捡烟蒂", "烟蒂股", "烟蒂投资", "NCAV", "净流动资产", "清算价值", "安全边际", "价值陷阱", "深度价值", "撿煙蒂", "煙蒂股", "煙蒂投資", "淨流動資產", "清算價值", "安全邊際", "價值陷阱", "深度價值", "Graham", "cigar butt", "net-net", "liquidation value", "value trap", "margin of safety", "deep value", "Benjamin Graham".
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Performs gap analysis on NVIDIA TAO Visual ChangeNet (VCN) Classify experiments by invoking the data-services container (`tao_toolkit.data_services` from `versions.yaml`) directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, auditing PASS/NO_PASS boundary cases, or running DEFT gap analysis on an AOI ChangeNet model.
Create comprehensive technical roadmaps aligned with business goals. Plan technology investments, architecture evolution, and infrastructure improvements over quarters and years.
Motto: Style counts. Context matters. Story trumps numbers.
Requirements Discovery Specification, applicable to exploratory scenarios, helps users identify high-ROI functional directions when they are confused through role-playing. Automatically triggered, purely conversational inspiration.
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.