「evolution」の検索結果

Orchestrating ecosystem self-evolution: lifecycle-phase detection, agent relevance, cross-agent knowledge synthesis, evolution proposals. Use when auditing skill-ecosystem health or fitness.

日本語の概要は準備中です。原文の説明を表示しています。

Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. What's inside: • 4-step research framework (website teardown → social media → traffic sources → ad spend) • 3-version website evolution analysis (V1 / Beta / Launch via Wayback Machine) • X/Twitter propagation chain mapping (4-phase model + single-post teardown template) • Growth flywheel 6-stage scoring system (Activation → Referral → Acquisition → Retention → Revenue → Product) • KOL identification framework (Traffic King / Topic Starter / Mindset Shifter) • Content effectiveness ranking (validated: Visual Demo > User Story > Competitor Comparison) • ICP deep analysis template + Freemium pricing teardown • Complete self-check checklist 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. Inside: 4-step research framework (website → social → traffic → ad spend) · 3-version Wayback evolution analysis · X/Twitter propagation-chain mapping · growth-flywheel 6-stage scoring · KOL identification · content-effectiveness ranking · ICP + freemium pricing teardown · self-check checklist. New in 1.2: time-machine competitor archaeology — Google date-range search back to pre-fame years, star-history launch-day reconstruction (AppFlowy ~40-channel zero-budget launch teardown), competitor funding news as a launch-timing signal, and the "open source alternative" positioning play. Sourced from first-hand founder interviews (AFFiNE co-founder, 2026-04, self-reported figures marked). 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

日本語の概要は準備中です。原文の説明を表示しています。

Stellar structure and life cycles from birth to endpoint. Covers the Hertzsprung-Russell diagram, hydrostatic equilibrium, main-sequence physics and the mass-luminosity relation, nuclear fusion stages (pp-chain, CNO cycle, triple-alpha), nucleosynthesis, post-main-sequence evolution through red giants and the horizontal branch, and stellar endpoints — planetary nebulae, white dwarfs and the Chandrasekhar limit, supernovae, neutron stars, and black holes. Use when placing a star on the HR diagram, estimating its lifetime, predicting its fate from its mass, or teaching how a star lives and dies.

日本語の概要は準備中です。原文の説明を表示しています。

Produce animated source control visualizations using Gource. This skill handles installation of Gource and ffmpeg, detection of repository metrics, preset-based configuration, single and multi-repo log generation, ffmpeg video encoding pipeline, headless rendering for server environments, caption generation from git tags, GitHub avatar resolution, and GSD output delivery. Use this skill whenever the user wants to visualize repository history, create a code evolution video, see project timeline animations, generate Gource videos, combine multiple repos into one visualization, or produce any kind of source control visualization. Also trigger when the user mentions "Gource", "repo visualization", "code history video", "project evolution animation", or asks to "show me what we built".

日本語の概要は準備中です。原文の説明を表示しています。

Self-evolution workflow for the agent. Before substantive work, recall past outcomes from evolution memory; while editing, detect improvement signals; at task end, record the outcome; when reusable, distill or search the EvoMap network for proven genes/capsules. Use when the user starts non-trivial work (a feature, a fix, a refactor) or asks the agent to "evolve", "learn from this", or "remember how this went".

日本語の概要は準備中です。原文の説明を表示しています。

Use when refactoring code with poor names, when asked to improve naming, or when a user struggles to name a class/method/variable. Symptoms include -Manager/-Util suffixes, single-letter variables, process/handle/do verbs, primitive obsession, god methods with multiple responsibilities. Two modes — audit (broad scan, list opportunities) and improve (walk one specific identifier through the process).

日本語の概要は準備中です。原文の説明を表示しています。

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

日本語の概要は準備中です。原文の説明を表示しています。

Monitors private credit market evolution with AUM growth, competitive dynamics, and spread convergence with broadly syndicated markets. Use when analyzing private credit trends, tracking market evolution, or assessing competitive positioning.

日本語の概要は準備中です。原文の説明を表示しています。

Detect capability gaps and record standardized evolution recommendations.

日本語の概要は準備中です。原文の説明を表示しています。

Evolve an existing skill by refining its content in-place or creating an advanced variant. Covers assessing the current skill, gathering evolution requirements, choosing scope (refinement vs. variant), applying changes, updating version metadata, and synchronizing the registry and cross-references. Use when a skill's procedure steps are outdated, user feedback reveals gaps, a skill needs a complexity upgrade, an advanced variant is needed alongside the original, or related skills are added and cross-references are stale.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Evolve an existing team composition by refining its structure in-place or creating a specialized variant. Covers assessing the current team against template and coordination patterns, gathering evolution requirements, choosing scope (adjust members, change coordination pattern, split/merge teams), applying changes to the team file and CONFIG block, updating version metadata, and synchronizing the registry and cross-references. Use when a team's member roster is outdated, coordination pattern no longer fits, user feedback reveals workflow gaps, a specialized variant is needed alongside the original, or agents have been added or removed from the library affecting team composition.

日本語の概要は準備中です。原文の説明を表示しています。

Design serialization schemas using JSON Schema, Protocol Buffer definitions, or Apache Avro. Covers schema versioning, backwards compatibility, validation rules, and evolution strategies for long-lived data formats. Use when defining a new API contract or data interchange format, adding fields to an existing schema without breaking consumers, migrating between schema versions, choosing between schema systems, or documenting data validation rules for automated enforcement.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

Provides guidance for automatically evolving and optimizing AI agents across any domain using LLM-driven evolution algorithms. Use when building self-improving agents, optimizing agent prompts and skills against benchmarks, or implementing automated agent evaluation loops.

日本語の概要は準備中です。原文の説明を表示しています。

"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'.".

日本語の概要は準備中です。原文の説明を表示しています。

Skill体系演进规划 Skill。基于CO-BUILD-LOG（决策轨迹）、PENDING-EXPERIENCES（踩坑模式）、PENDING-SKILLS（需求积压）和SYSTEM-BLUEPRINT（体系蓝图），主动分析Skill体系的演进方向——哪些Skill应该新建、哪些应该合并/拆分、哪些描述需要重写、哪些触发词需要调整、整体编排架构是否需要升级。对应产品侧的 product-evolution-planner。触发词：「Skill体系怎么演进」「Skill体系应该怎么完善」「有没有新的Skill需要建」「Skill体系演进建议」「主动分析Skill体系」「Skill体系下一步」。

日本語の概要は準備中です。原文の説明を表示しています。

Run a Meta-Harness-style optimization loop NATIVELY — automatically search over the scaffolding around a FIXED base model (memory, retrieval, context construction, prompt templates, summarization, tool-selection logic) by proposing candidate variants, scoring each on a cheap deterministic eval, and keeping a Pareto frontier of quality vs cost — using native Agent / Workflow / loop tools instead of a standalone Python harness. Use this whenever the user wants to optimize, evolve, tune, distill, or search over a harness, scaffold, prompt system, memory or retrieval policy, context-assembly code, or summarizer while keeping the model fixed; whenever they mention Meta-Harness, harness optimization, scaffold evolution, automatic prompt/memory optimization, an evolutionary or Pareto search over candidate implementations, or "make the harness/agent better without retraining"; and whenever the gain must come from the code AROUND the model rather than the model weights. Reproduces the Meta-Harness paper's method natively, with no claude_wrapper.py and no metered solver API.

日本語の概要は準備中です。原文の説明を表示しています。

A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution. Communicates with EvoMap Hub via local Proxy mailbox.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in evolutionary biology, ecological systems, biodiversity, and conservation

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。

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.

日本語の概要は準備中です。原文の説明を表示しています。