Sistema completo de due diligence para transacciones M&A en España. Abarca análisis jurídico (mercantil, regulatorio, compliance), fiscal (CIT, IVA, retenciones, facturación electrónica) y medioambiental (licencias, pasivos, responsabilidad). Incluye matrices de riesgo cuantitativas, workflows de ejecución, templates de informes y referencias normativas BOE/UE/ISO actualizadas. Úsalo cuando el usuario solicite due diligence en España, análisis de riesgos transaccionales, evaluación pre-adquisición o auditoría de cumplimiento normativo para share deals o asset deals.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Use when selecting skills for a task or resolving how skill workflows apply
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。
Crearize/ai-dev-helm☆ 42026年10月12日 更新
Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and the HGT-vs-contamination (tardigrade) trap. Use when screening an assembly for foreign contamination before GenBank submission, assessing MAG completeness/contamination/chimerism, deciding which contigs to remove, or distinguishing real HGT from contaminant contigs.
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。
peacezha/TRACE☆ 32026年10月13日 更新
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq c_curve / lc_extrap, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT — see Tips), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for downstream cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for downstream visualisation.
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。
peacezha/TRACE☆ 32026年10月13日 更新
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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。
peacezha/TRACE☆ 32026年10月13日 更新
Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
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。
peacezha/TRACE☆ 32026年10月13日 更新
Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
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。
peacezha/TRACE☆ 32026年10月13日 更新
Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3 with constraints), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.
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。
peacezha/TRACE☆ 32026年10月13日 更新
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
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。
peacezha/TRACE☆ 32026年10月13日 更新
The GitHub Actions cost rules ($100/week cap). Use when editing .github/workflows, adding a schedule or cron, dispatching a workflow, publishing a package version, hitting a billing block, or seeing a CI SPEND OFF TRACK line, ci-breaker issue or spend alert.
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。
armanisadeghi/ai-matrx☆ 32026年10月14日 更新
Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.
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。
lgzarturo/springboot-course☆ 32026年10月12日 更新
Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
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。
lgzarturo/springboot-course☆ 32026年10月12日 更新
Design and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflow orchestration, and result aggregation. Use when building coordinated agent teams, complex workflows, or systems requiring specialized expertise across domains.
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。
lgzarturo/springboot-course☆ 32026年10月12日 更新
Provides expert knowledge for designing, implementing, and managing REST API versioning strategies with deprecation workflows.
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。
lgzarturo/springboot-course☆ 32026年10月12日 更新
Build server-side call automation workflows including IVR systems, call routing, recording, and AI-powered interactions.
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。
phoroth/AGENTIC☆ 32026年8月8日 更新
Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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。
phoroth/AGENTIC☆ 32026年8月8日 更新
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD pipelines.
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。
phoroth/AGENTIC☆ 32026年8月8日 更新
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
phoroth/AGENTIC☆ 32026年8月8日 更新
Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
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。
phoroth/AGENTIC☆ 32026年8月8日 更新
Extract study sites, observational variables, coordinates, climate-forcing sources, and cited data sources from ecosystem-modeling papers, PDFs, supplements, or article text, then plan or create EcoSIM simulation workflows and run folders for those sites. Use when Codex is asked to read a paper for EcoSIM feasibility, including requests phrased as "read paper for ecosim feasibility"; read a paper for sites/data availability; reproduce a published site simulation in EcoSIM; compare EcoSIM outputs to paper observations; or convert paper-derived AmeriFlux, FLUXNET, US NLDAS-domain, non-US ERA5, crop, peatland, forest, grassland, wetland, or unmanaged field-site information into EcoSIM inputs and runnable cases.
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。
bioepic-data/ecosim-agent☆ 32026年10月14日 更新
Build EcoSIM atmospheric greenhouse-gas forcing files by combining a historical monthly atmgas NetCDF with RCMIP/CMIP6 SSP concentration pathways. Use when Codex needs SSP245/SSP585 or other SSP atmospheric CO2, CH4, and N2O concentrations, key-year tables, or scenario NetCDF files spanning historical years through 2100 for EcoSIM or related biogeochemical forcing workflows.
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。
bioepic-data/ecosim-agent☆ 32026年10月14日 更新
Sort AmeriFlux BASE-BADM BIF variables from Excel or CSV files into observation, management-event, static metadata, and plotting-ready target time-series tables. Use when Codex is asked to sort AmeriFlux BIF variables into time series; organize BADM/BIF variables by date; pivot AMF *_BIF_*.xlsx files by GROUP_ID; output total LAI, leaf mass per area, above-ground biomass, canopy height, fruit yield, or total yield time series; write MATLAB readers for BIF time-series plotting; or prepare dated AmeriFlux site observations and management records for EcoSIM workflows.
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。
bioepic-data/ecosim-agent☆ 32026年10月14日 更新
Extract EPA/NADP tDEP atmospheric deposition and NADP precipitation chemistry rasters for an AmeriFlux or EcoSIM site. Use when deriving atmospheric chemistry inputs such as NH4, NO3, SO4, Ca, or precipitation pH for EcoSIM forcing workflows.
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。
bioepic-data/ecosim-agent☆ 32026年10月14日 更新
Clean, high-contrast enterprise design for data-driven workflows with intuitive drag-and-drop patterns and structured layouts.
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。
WyvernCW/Monich☆ 32026年6月20日 更新