245 件 ・ 関連度順
概要と使いどころ
Continuous self-improvement system for Hermes Agent. Analyzes your past sessions to find recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them. Set it to run overnight and wake up to a better agent. Use /dojo to start.
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。
Orchestrate a phased WinForms-to-Avalonia migration: behavior contracts, UI-independent application logic, platform adapters, MVVM shell, parity/cutover, evolution, and hardening. Use when the request explicitly involves moving a WinForms or .NET Framework desktop UI to Avalonia, or continuing that migration. Do not trigger for unrelated migrations, rewrites, ViewModel refactors, settings work, or command-line work without WinForms-to-Avalonia context. Load this orchestrator before one matching phase skill.
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。
用户在选择投资标的或商业模式时，纠结于"便宜买差企业"还是"合理价买好企业"。 语言信号:"这个便宜能不能捡"/"估值低是不是就值得买"/"贵的好公司值不值得买"/"便宜货有什么问题" 不适用于:已有明确投资方法论且不需要在两种风格间做选择的场景。
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。
Forward Deployed Engineer delivery contract for AWS engagements, build-first artifacts, grounded cost estimates, Well-Architected review, evolution roadmap
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。
Casting personas: rapid generation from diverse inputs, registry-based persistence and lifecycle, data-driven evolution, inter-agent sync. Not for UI walkthroughs (Echo) or user research (Field).
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。
Brownfield improvements — the full WDS pipeline in miniature for existing products
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 interpreting biological research findings, evaluating life science evidence, analyzing molecular or cellular mechanisms, comparing competing biological hypotheses, designing or critiquing experiments in biology, genetics, genomics, cell biology, immunology, neuroscience, ecology, or any life science domain. Triggers on questions about gene function, pathways, phenotypes, GWAS hits, single-cell data, animal models, clinical translation, evolutionary arguments, or any biology/life science reasoning task.
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 one iteration of proteus memory-summary evolution. Called by meta_harness.py.
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-Evolving Development Loop - Dynamic skill generation with learning and evolution
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。
Kotlin mapping for the AppSettings proto-backed DataStore: UI-state and native-preferences conversion. Use when adding or reading an app-settings field. Removing a field: use protobuf-schema-evolution first.
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。
Reserved-field rules and field-number lookup for evolving AppSettings, plus the Rust/Kotlin diagnostics wire contract. Use before assigning or removing a proto field. DataStore mapping: protobuf-datastore.
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。
Decompose and explain healthcare cost growth into contributing factors including utilization changes, unit price shifts, case mix evolution, and population changes. Use when analyzing PMPM cost trends, explaining cost variance to leadership, preparing actuarial summaries, or identifying cost reduction opportunities.
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。
Add a new single-objective optimization algorithm to the sci-comp library. Use when the user asks to implement a new optimizer (e.g. gradient descent, BFGS, simulated annealing, differential evolution).
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。
Industrial production of iron and steel from ore to finished product — blast furnace, Bessemer converter, open-hearth, basic oxygen, electric arc, continuous casting, rolling, and secondary metallurgy. Covers the chemistry of decarburization, dephosphorization, and desulfurization, the metallurgy of plain carbon steels, the evolution from puddling and rolling to BOF and EAF, and the process-structure-property chain that makes mild steel the default structural material of the industrial era.
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。
Distributed storage systems design and operation for cloud platforms. Covers the GFS/HDFS block-and-master pattern, object storage (Swift/S3) with consistent hashing and eventual consistency, block storage semantics, replication vs erasure coding, the CAP theorem in practice, read-repair and anti-entropy, snapshot chains, and the GFS/BigTable/Spanner evolution. Use when designing a storage subsystem, choosing between object/block/file, or reviewing a replication and consistency strategy.
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。
Artificial Life atlas covering open-ended evolution, chemical reaction networks, morphogenesis, self-replication, and programmable chemistry. Use when picking a simulation framework, wiring ALife models into the skill graph, or onboarding contributors to the ALife cluster.
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。
Comprehensive Artificial Life skill combining ALIFE2025 proceedings, classic texts (Axelrod, Epstein-Axtell), ALIEN simulation, Lenia, NCA, swarm intelligence, and evolutionary computation. 337 pages extracted, 80+ papers, 153 figures.
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。
ALIFE 2025 Proceedings - 337 pages, 80+ papers on artificial life, evolution, emergence, cellular automata, neural networks, agent-based models. Full Mathpix extraction with equations and figures.
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。
Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.
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。
Weekly review of IWE work culture (element 14 — System Evolution). Runs during Week Close.
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。
AI-powered diary generation for agents - creates rich, reflective journal entries (400-600 words) with Quote Hall of Fame, Curiosity Backlog, Decision Archaeology, and Relationship Evolution. Generates personal, emotional entries from the agent's perspective. Works best with Claude models (Haiku, Sonnet, Opus).
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。
SQLite database expertise for migrations, multi-tenant patterns, transactional safety, FTS5 virtual tables, and schema evolution. Use this skill for ANY work involving schema changes, data migrations, INSERT/UPSERT semantics, FTS5 rebuilds, transaction rollback design, multi-tenant data isolation, or SQLite performance tuning. Triggers include: writing migration SQL, modifying `_import_table`-style importers, designing UNIQUE/PK constraints, adding indexes for JOINs, debugging "rows missing" / "wrong count" / "stuck for hours" symptoms in SQLite-backed systems.
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 the user asks about evolution progress, current scores, best version, how many iterations ran, or whether the loop is stagnating.
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。
[Deprecated] skill lifecycle merged. Use `cm-skill-evolution` instead.
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。