262건 · 관련도순
Expert-level evolutionary biology covering natural selection, genetic drift, molecular evolution, phylogenetics, speciation, macroevolution, and the modern synthesis.
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Track and optimize agent specialization during methodology development. Use when agent specialization emerges (generic agents show >5x performance gap), multi-experiment comparison needed, or methodology transferability analysis required. Captures agent set evolution (Aₙ tracking), meta-agent evolution (Mₙ tracking), specialization decisions (when/why to create specialized agents), and reusability assessment (universal vs domain-specific vs task-specific). Enables systematic cross-experiment learning and optimized M₀ evolution. 2-3 hours overhead per experiment.
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Build evolutionary trees from related nucleotide or protein sequences using MAFFT with IQ-TREE or FastTree. Review branch support, a measure of evidence for each grouping, and produce tree summaries and optional visualizations.
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Think and work like an expert Evolutionary Biologist. Use when a task calls for Evolutionary Biologist judgment. Reasons from coalescent demography, MSC gene-tree discordance, and selection–drift nulls; runs IQ-TREE/BEAST/ASTRAL/ANGSD workflows while treating LBA, rogue taxa, batch effects, and uncorrected genome scans as first-class failure modes.
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Evolution CMS JP Editionの開発タスクを実行するためのワークフロー、コマンド(/work, /start-session等)、およびコーディング規約ガイド。開発作業を開始する際に使用します。
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Evolution CMS JP Edition のリリース作業を対話形式でガイドするスキル。バージョン更新・タグ作成・GitHub Actions によるパッケージビルドまでを順を追って進める。「リリース」「release」と依頼されたときに使用する。
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Add or change stable public state, contracts, routes, migrations, adapters, rescue behavior, or compatibility-sensitive surfaces through the evolution ledger.
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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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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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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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Develop solution ideas through repeated rounds of research, cross-domain borrowing, recombination, and evaluation. Receive ranked candidates with feasibility and novelty scores, supporting and opposing arguments, and a record of where each idea came from and how it changed.
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Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.
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Guides iterative improvement of AI agent prompts, skills, and memory using A-Evolve. It explains how to evaluate agents against benchmark tasks, apply changes, validate results, and track or roll back revisions with Git.
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Compares Trailmark code graphs at two source code snapshots (git commits, tags, or directories) to surface security-relevant structural changes. Detects new attack paths, complexity shifts, blast radius growth, taint propagation changes, and privilege boundary modifications that text diffs miss. Use when comparing code between commits or tags, analyzing structural evolution, detecting attack surface growth, reviewing what changed between audit snapshots, or finding security-relevant changes that text diffs miss.
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Guides design and implementation of evolutionary modular-monolith platforms with DDD (strategic + tactical), flat-by-aggregate organization, an Anti-Corruption Layer for vendor independence, a transactional outbox for events, smart resilience (backoff with jitter, circuit breakers, idempotency), and a polished architecture HTML document with elegant SVG diagrams. Use when designing a platform or backend, defining bounded contexts, organizing modules and folders, choosing monolith vs microservices, decoupling from an external service (ERP, storage, AI), making calls resilient, adding real-time push, picking a 2026 TypeScript stack (Nx, NestJS, React), or producing an architecture document or diagram. Also triggers on 'modular monolith', 'bounded contexts', 'flat-by-aggregate', 'ports and adapters', 'architecture diagram'. Do NOT use for simple CRUD, NestJS-only deep implementation (use nestjs-modular-monolith), or pure domain-model review (use tactical-ddd).
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Specialist in designing and implementing scalable modular monolith architectures using NestJS with DDD, Clean Architecture, and CQRS patterns. Use when building modular monolith backends, designing bounded contexts, creating domain modules, implementing event-driven module communication, or when user mentions "modular monolith", "bounded contexts", "module boundaries", "DDD", "CQRS", "clean architecture NestJS", or "monolith to microservices". Do NOT use for simple CRUD APIs, frontend work, general NestJS questions without architectural context, or stack-agnostic evolutionary modular monolith design (use evolutionary-modular-architecture).
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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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Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
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Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.
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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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Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
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After solving a non-trivial problem, detect generalizable learnings and propose skill updates. Always active — applies to every interaction.
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Argentine Supreme Court citation network analysis using JurisRank — a peer-reviewed PageRank algorithm with temporal decay for measuring jurisprudential authority. Ranks precedents by citation influence, traces doctrinal evolution, and detects constitutional drift. Published methodology: JCLLT (DOI: 10.47852/bonviewJCLLT62027951). Activate with: which cases to cite, rank precedents, case authority, doctrinal evolution, Argentine Supreme Court, CSJN jurisprudence, citation network, leading case, legal research Argentina.
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Use when running, reviewing or changing coco's self-evolution cycle: the 30-day loop that observes how skills are actually used, proposes evidence-backed edits to them as one branch and one pull request, and lands nothing without the owner's merge. Also use when asked why a skill changed, what the loop would propose next, or how to reject a proposal so it is not raised again.
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