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Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD).

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。

Conviction-trigger surveillance via Quartr keyword alerts — parse every active thesis's Conviction Triggers for named observables, propose keyword alerts scoped to the Vault Active watchlist (or market-wide for macro observables), create them ONLY on explicit user confirmation, and reconcile the alert set against the book as theses open/close. Use when user says "monitor", "set up alerts", "keyword alerts", "watch for X", or "reconcile alerts".

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。

Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `/numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

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。

Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `$numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

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。

Reconcile SDD Composy task Markdown and the JSON projection: read-only inspect and plan, then apply only with an explicit authority and the CONFIRM-SDD-SYNC token. Use when task contracts and operational state diverged — 'sincronizar as tarefas', 'o JSON das tasks está desatualizado', 'markdown and json disagree'. Do NOT use to list or advance tasks (sdd-tasks), for a status overview (sdd-status), or to simulate approval.

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。

Gera imagem por IA via Ideogram, Leonardo ou Flux usando os scripts do plugin, ou entrega o prompt otimizado quando não há chave. Use quando o usuário disser "gerar imagem", "criar ilustração", "imagem por IA", "Ideogram", "Leonardo", "Flux", "preciso de uma foto que não temos", ou quando creative-concept marcar um ativo como "gerar". Skill central deste plugin. Toda chamada gasta crédito e exige confirmação prévia.

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。

Aumenta resolução e qualidade de imagem via Freepik/Magnific, ou orienta o processo manual quando não há chave. Use quando o usuário disser "upscale", "melhorar a resolução", "a imagem está pixelada", "Magnific", "Freepik", "aumentar a imagem", "imagem pequena demais". Gasta crédito — exige confirmação.

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。

Estima, confirma e acompanha o gasto com geradores pagos. Use quando o usuário disser "quanto vai custar", "orçamento", "quanto gastei", "controlar custo", "está caro", antes de qualquer lote de geração, ou ao fechar campanha. Com APIs no centro, custo deixa de ser detalhe e vira restrição de projeto.

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。

Arquitetura e diagnóstico AWS — VPC, EC2, ECS, EKS, ALB, NLB, RDS, ElastiCache, S3, CloudFront, Route53, IAM, WAF, GuardDuty. Use quando o usuário disser "AWS", "VPC", "ALB", "RDS", "revisar a arquitetura", "Well-Architected", "subnet", "security group", "IAM", ou citar qualquer serviço AWS. Leitura livre; qualquer mudança passa pelo portão de confirmação.

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。

Implement GNOME's 4-tier destructive-action pattern: undo toasts instead of confirmation dialogs, AdwAlertDialog for irreversible actions. Use when adding delete, remove, clear, or other destructive actions.

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 the migrate-prep cascade — extract --prep → direct --prep → prototype --prep → assets prep — with confirmation gates between phases. Builds the data structure migrate consumes.

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。

Derive and implement first-principles audio-coupled haptic and kinetic UI feedback. Use when designing, reviewing, or coding haptics/audio for mobile apps, native iOS/Android, React Native/Expo, SwiftUI, games, detent pickers, toggles, sliders, page curls, scroll gestures, wallet/signature flows, Solana transactions, backend/realtime state changes, confirmations, settlements, and semantic UI events where feedback should follow signal math instead of ad hoc vibration calls.

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 the right tool for a process step. Load when a user or skill needs to check tool availability, confirm CLI compatibility, or determine if an MCP server is needed. Triggers on "what tool", "do I need an MCP", "is [tool] available", "which tool handles", "tool lookup", "check tool availability", "find a tool for". Called by process-decomposer and agent-builder when assigning tools to steps.

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。

Gracefully retire a skill that is redundant, superseded, or no longer earning its place in the context window. Load when improve-skills finds a skill scoring 0-5/14 AND research confirms the domain is now handled natively by current models, when two skills have overlapping triggers and one subsumes the other, when the user asks to remove a skill, retire a skill, delete a skill, or clean up redundant skills, or when validate-skills flags a skill as a duplicate trigger risk. Handles removal cleanly: updates all callers, removes from AGENTS.md, updates README, and archives rather than deletes so the skill can be recovered if needed.

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 about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always

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。

Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD).

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。

Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.

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。

Interactive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or choosing an annotation viewer.

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。

Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.

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。

Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.

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。

Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.

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。

Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.

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。

Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.

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。

Implements missing-data sensitivity analyses for confirmatory clinical trials including MMRM under MAR (with Kenward-Roger correction), reference-based multiple imputation (J2R, CR, CIR, LMCF per Carpenter-Roger 2013), Permutt delta-adjustment / tipping-point analysis, pattern-mixture identifying restrictions (CCMV, NCMV, ACMV), and the Cro vs Bartlett variance debate. Use when handling missing primary or secondary endpoint data in regulatory submissions following NRC 2010 and ICH E9(R1).

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。