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概要と使いどころ
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI.
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。
Install and wire a Telegram MCP server (chigwell/telegram-mcp, MTProto user account, not a bot) so Claude Code, Claude Desktop, Codex or any MCP client can read your chats and send replies. Use when connecting an agent to a personal Telegram account, applying the kit patches (shared SSE daemon, search_dialogs, multi-account), generating a session string, or debugging file tools that silently refuse, orphaned uv processes, database is locked, AuthKeyDuplicated or flood-wait.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
MUST USE for building FastAPI/REST API applications with Aerospike database (aerospike-py Rust/PyO3 async client). Covers AsyncClient lifespan management, FastAPI Depends injection, aerospike_py exception-to-HTTP-status mapping (RecordNotFound→404, RecordExistsError→409, BackpressureError→503, AerospikeTimeoutError→504), POLICY_EXISTS_CREATE_ONLY/UPDATE_ONLY for CRUD semantics, NamedTuple attribute access (record.bins not tuple unpacking), client.ping() readiness probe, batch_read returning dict, batch_write with in_doubt retry signal, and global AerospikeError handler. Triggers on: FastAPI + Aerospike, REST API + aerospike-py, CRUD API with Aerospike, client.ping() health probe, batch_read/batch_write endpoint, web server/HTTP service backed by Aerospike NoSQL, uvicorn + Aerospike.
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 this harness with Darwin Mode — frozen model, evolving harness (real, sandboxed, safety-gated).
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Extracts and assigns COSMIC v3.4 mutational signatures (84 SBS / 11 DBS / 18 ID / 24 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
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。
Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.
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。
Create or modify Rash Studio projects, SVG animation templates, timeline keyframes, interactive scenes, and website embeds. Use for Rash authoring requests, not general Flash or SWF conversion.
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。
Verify Rash Studio scenes, ActionScript subset behavior, resource links, portable embeds, exports, and desktop or mobile interaction. Use for Rash acceptance checks, compatibility review, security review, or release validation.
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。
Enforces Pydantic v2 strict models, asynchronous dependency generators, and uv environment setup.
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。
Compõe a peça final no Figma sobre os ativos gerados — texto, marca, layout e grid. Camada OPCIONAL: use quando houver Figma conectado e o time precisar do arquivo editável. Dispara em "montar no Figma", "compor a peça", "colocar o texto na imagem", "aplicar a marca", "arquivo editável". Sem Figma, entrega especificação de composição.
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。
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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。
Extracts and assigns COSMIC v3.4 mutational signatures (84 SBS / 11 DBS / 18 ID / 24 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
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。
Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.
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。
使用Maya Python将选中的多边形或细分模型转换为NURBS曲面，并按照指定的U和V方向数量提取曲线。
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。
Core principles and golden rules for writing production-quality Ansible automation, covering FQCN requirements, module selection guidance, and execution patterns using uv run.
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。
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in uv, ruff, pydantic, and FastAPI.
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。
You are a Python project architecture expert specializing in scaffolding production-ready Python applications. Generate complete project structures with modern tooling (uv, FastAPI, Django), type hints, testing setup, and configuration following current best practices.
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。
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI. Use PROACTIVELY for Python development, optimization, or advanced Python patterns.
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。
Generate and edit images with Gemini API using pure Python stdlib. Zero dependencies - works on locked-down environments where pip/uv aren't available.
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。
Render mathematically-faithful Turkish marbling (Ebru) and paper-marbling with closed-form coordinate-deformation maps — a drop is a radial outward push, a comb/tine is a decaying shear, all composed as homeomorphisms over colored region boundaries. No fluid solver, runs 60fps on a 2D canvas. Use when building a marbling/Ebru toy or generative-art renderer, implementing drop/comb/wave/swirl deformations, scripting traditional patterns (Battal, Gel-Git, Şal, Bülbül Yuvası), keeping combed edges crisp via adaptive resampling, modeling ox-gall shouldering for crisp ring-in-ring drops, or exporting high-DPI marbled prints. NOT for real fluid / Navier-Stokes simulation, metaball / alpha-blended "smoke" blob effects, raster image-processing filters, or non-marbling generative art.
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。
Systematic framework for resurrecting and modernizing legacy codebases through archaeology, resurrection, and rejuvenation phases. Activate on "legacy code", "inherited codebase", "no documentation", "technical debt", "resurrect", "modernize". NOT for greenfield projects or well-documented active codebases.
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。
MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.
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。