“skill evolution”的搜索结果

[Deprecated] skill lifecycle merged. Use `cm-skill-evolution` instead.

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[Deprecated] skill lifecycle merged. Use `cm-skill-evolution` instead.

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Manages Apache Avro schema evolution with compatibility checking via Confluent Schema Registry API. Validates forward, backward, and full compatibility across schema versions automatically.

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Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.

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Evolve an existing agent definition by refining its persona in-place or creating an advanced variant. Covers assessing the current agent against best practices, gathering evolution requirements, choosing scope (refinement vs. variant), applying changes to skills, tools, capabilities, and limitations, updating version metadata, and synchronizing the registry and cross-references. Use when an agent's skills list is outdated, user feedback reveals capability gaps, tool requirements have changed, an advanced variant is needed alongside the original, or the agent's scope needs sharpening after real-world use.

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Audits and optimizes the five global Claude layers (User Preferences, Styles, Global Memory, Skills, MCP Connectors) using the Global Layer Scorecard (six dimensions, anchored 1-5 rubrics). Detects eight cross-layer failure modes and produces evolutionary recommendations (Promotion, Demotion, Codification, Skill Extraction). Use when user says "audit my global setup," "optimize my preferences," "review my Claude configuration," "check my cross-project setup," "are my preferences working," "clean up my global memory," or "what should be in my preferences vs my project." Also use when a user has 3+ Projects and wants to improve their shared foundation. Do NOT use for single-Project audits, Project Memory optimization, or full-stack audits (use rootnode-project-audit, rootnode-memory-optimization, or rootnode-full-stack-audit respectively, if available). Run on Opus 5 or Sonnet 5 at `high` effort (both defaults); depth reduces on legacy models.

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"Apply Kuhn's paradigm theory to analyze scientific progress through the cycle of normal science, anomalies, crisis, and revolution. Use this skill when the user needs to understand why a field resists change, trace paradigm shifts in a discipline, analyze incommensurability between competing frameworks, or when they ask 'why do scientists ignore contradictory evidence', 'how do scientific revolutions happen', or 'why can't proponents of different paradigms agree'.".

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主动式产品演进规划 Skill。基于「AI时代产品问题全景框架」等设计原则，主动分析当前产品现状，识别原则缺口，生成有理据的改进建议。与 role-产品经理 的区别：后者执行具体设计任务；本 Skill 是「对照原则做战略诊断，主动思考产品应该怎么演进」。触发词：「基于产品原则看看有什么可以改进的」「帮我看看产品还缺什么」「给我一些迭代建议」「从第一性原理看这个产品」「主动分析产品」「这个产品符合马斯洛新瓶颈吗」「产品演进建议」。

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Solves complex, open-ended problems using an evolutionary search mechanism inspired by genetic algorithms. Generates a diverse population of candidate answers, scores them against a rubric, then iteratively applies selection, crossover, and mutation to breed progressively better solutions. Use this skill whenever the user wants to explore a solution space deeply, asks for the "best" answer to a subjective or multi-dimensional problem, wants to evaluate competing approaches and distill the strongest, or uses any of these triggers: "evolutionary search", "genetic algorithm", "evolve an answer", "breed solutions", "population-based search", or phrases like "give me multiple approaches and refine the best one". Also trigger when the problem is complex, open-ended, or has no obvious single correct answer and the user wants a high-quality, well-explored result, not just a quick response.

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Wardley Mapping for strategic positioning — map your value chain from customer need to underlying components, plot where each component sits on the evolution curve (novel to commodity), and spot the strategic plays that movement creates. Use this skill when someone wants to understand where their industry is heading, whether they're building things they should be buying, where competitors are vulnerable, what's about to become commoditised, or why their competitive advantage is eroding. Triggers on questions about build-vs-buy, component evolution, value chain analysis, commoditisation risk, or strategic positioning relative to market movement. Three modes — full diagnostic (build a map from scratch), review (update positions and reassess), and alert triggers (set tripwires for when components move). Based on practice by Eterdis (eterdis.com).

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Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

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• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Process DNA, RNA, and protein sequences with Biopython, convert biological files, and retrieve NCBI records. Supports batch workflows for sequence comparison, searches for similar sequences, protein structure analysis, and evolutionary trees.

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• Convert GenBank files to FASTA
• Calculate sequence length and GC content in batches
• Retrieve sequences and publication records from NCBI

Analyze existing evolutionary trees and other hierarchical trees with ETE 4. Edit branches, add annotations, compare branching structures, explore large trees interactively, and render figures for publication using the appropriate optional visualization components.

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• Prune selected species and change a tree's root
• Compare branching structures between two trees
• Add NCBI or GTDB taxonomy information

Helps startup leaders make strategic data decisions: assess permission to train AI on customer data, choose data architecture, evaluate customer data as a business asset, and sequence hiring around the decisions the business needs to make.

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• Assessing customer data for AI training
• Choosing data architecture and what to build or buy
• Evaluating data assets before fundraising or M&A

Support startup AI strategy by comparing API use, fine-tuning—additional training of an existing model—and in-house development. Assess three-year costs, regulatory risk, self-hosting breakeven points, and hiring priorities to inform leadership decisions.

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• Compare API use, fine-tuning, and in-house development
• Assess regulatory risks for an AI use case
• Calculate when self-hosting becomes cost-effective

Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.

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• Reviewing retention metrics and reasons for churn
• Allocating support investment across customer tiers
• Estimating customer success staffing needs

Research a product, company, concept, or person by tracing its history and comparing it with current peers. Combine both perspectives to explain strengths, weaknesses, and possible futures, then deliver a structured PDF report with sources.

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• Investigating a product’s history and current competitors
• Understanding the origins and evolution of a technical concept
• Examining a company’s strategic decisions and market position

Set up, configure, update, and troubleshoot persistent memory for OpenClaw agents. The plugin stores conversations locally and supports automatic recall, task summaries, reusable skills derived from successful tasks, and a visual memory manager.

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• Setting up persistent memory for OpenClaw
• Updating or troubleshooting the memory plugin
• Choosing local or external memory search models

Compare code structure across two commits, tags, or directories to identify security-relevant changes. Produces a report on new attack paths, privilege boundaries, complexity, and how widely a change could affect other functions.

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• Compare code structure before a release
• Investigate new attack paths after code changes
• Track untrusted data flows across a refactor

Design a modular monolith, a single application with clear business boundaries. Plan module structure, external service integration, failure handling, and future separation, then produce an HTML architecture document with SVG diagrams.

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• Design business boundaries for a new backend
• Plan integration that limits dependence on an external vendor
• Define retries and failure handling for external calls

NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA BioNeMo NIMs: search for MSA alignments with MSA-Search (ColabFold), then predict the structure with OpenFold3 using the retrieved alignments. Use this skill whenever the user wants to predict a protein structure with maximum accuracy using MSA context, run the full AlphaFold3-style pipeline, generate MSA-informed structure predictions, or improve structure prediction accuracy by providing evolutionary information. Triggers on: MSA structure prediction pipeline, structure prediction pipeline, MSA-informed prediction, OpenFold3, ColabFold MSA, AlphaFold3 pipeline, protein structure, homology search, a3m alignment, UniRef30, NIM microservice. This pipeline chains MSA-Search and OpenFold3.

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Fetch Evolutionary Conservation scores (phyloP, phastCons) and Transcription Factor Binding Sites (TFBS) from the UCSC Genome Browser. Use when analyzing whether genomic variants or regions are evolutionarily conserved, functionally important, or bounded by TF regulators across major projects (ENCODE, JASPAR, ReMap).

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Baidu FaMou algorithm skills for efficient algorithm self-evolution. Provides experiment management and visualization capabilities to help optimize complex algorithms. Use when user needs algorithm optimization or experiment management.

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Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.

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