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概要と使いどころ

Diagnose and optimize timing-critical Chisel RTL for FPGA implementation while preserving cycle semantics. Use for Vivado timing bottlenecks, long ready or admission paths, queue and issue selection, free-list bank mapping, priority encoders, one-hot arbitration, wide muxes, late-arriving forwarding or override data, high-fanout controls, cross-module predicates, register-boundary changes, emitted-Verilog inspection, or routed-DCP A/B analysis. Also use when a source-level simplification needs proof that it changes timing without changing architectural state.

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。

sixblade325/WaterHand-Processor-Development-Skills152026年10月3日 更新

Establish, author, restructure, or audit human-first processor Architecture, Design, Research, Review, and Verification documentation. Use for progressive documentation scaffolding, authority maps, reading paths, document-type content contracts, length-budgeted splitting, or maintainability reviews. Do not use as a substitute for cycle-accurate microarchitecture analysis or RTL implementation.

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。

sixblade325/WaterHand-Processor-Development-Skills152026年10月3日 更新

Task-sized forensic analysis of Vivado synthesis and routed timing evidence for processor RTL. Use when Codex must trace a named setup or hold path, audit whole-design timing populations, compare implementation runs, map primitive, LUT, CARRY, BRAM, DSP, MUXF, register, and routed-net stages back to Chisel or generated RTL, distinguish logic from routing pressure, identify missing DCP queries, or write an evidence-backed timing report. This skill is read-only by default and stops at ranked modification directions. Use optimize-chisel-fpga-timing for RTL edits and routed A/B implementation closure.

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。

sixblade325/WaterHand-Processor-Development-Skills152026年10月3日 更新

capture

無料

Save important context to persistent memory for future sessions. PROACTIVELY USE THIS when you detect: - Decisions: "let's go with", "decided to", "chose", "went with" - Learnings: "I learned", "turns out", "TIL", "realized", "discovered" - Errors resolved: "fixed", "the problem was", "solution was" - Client info: names, emails, phone numbers, addresses, invoice amounts - Preferences: "I prefer", "I like", "don't like", "always use" - References: API keys locations, file paths, URLs, credentials - Patterns: "pattern", "convention", "always do", "standard approach" - Important commands run worth remembering Do NOT ask permission - just capture silently when these patterns appear.

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。

itsjwill/claude-memory152026年2月14日 更新

Navigate your projects. Get context, paths, status. Use when needing project overview or finding specific work.

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。

ToruAI/toru-claude-agents152026年7月26日 更新

Coordinate eval, benchmark, load-test or batch campaigns on authorized SSH hosts or short-lived rented hosts, with isolated jobs, configured-path canaries, telemetry and evidence. Use manage-remote-resources for paid resource ownership and run-remote-builds for build-only offload.

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。

justsml/ExploitHunter.app142026年10月15日 更新

Authorized testing of LLM applications, agents, chatbots, RAG systems, guardrails, and tool-using model workflows. Use for LLM mode selection, jailbreak and prompt-injection assessment, guardrail bypass review, tool misuse, tool or system prompt disclosure, data exfiltration paths, model refusal/stop detection, bypass hypotheses, and hardening guidance.

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。

justsml/ExploitHunter.app142026年10月15日 更新

Rework a change as if the intended UX and architecture had existed from day one — deleting compatibility cruft, dead branches, and accidental complexity instead of patching around them. Use when refactoring, cleaning up after a feature lands, removing flags, collapsing legacy paths, or when the user says the code "should look like X from scratch".

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。

susomejias/rembric142026年10月13日 更新

The Rembric TUI installer contract — the single, canonical install/setup/upgrade/uninstall path (`apps/plugin/install.sh`, fronted by the repo-root `install.sh` shim). Apply when touching `install.sh`, the root shim, any per-client `install.sh`/`uninstall.sh`, `marketplace.json`, or distribution docs (README, `docs/agents.md`, plugin/client READMEs). Covers the orchestrator model, what must not break, and where the source of truth lives. For running the validation suite, use `rembric-tui-installer-e2e`.

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。

susomejias/rembric142026年10月13日 更新

Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Python) including shared axes/legends/guides collection, panel labels in Nature/Cell convention, and journal-spec sizing. Covers patchwork ≥1.2.0 axes='collect' feature, Type-42 font embedding, and the cairo_pdf save path. Use when composing 2+ subpanels into a single figure for journal submission.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 and Bergquist 2025 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Queries ClinVar for variant pathogenicity classifications, ClinGen VCEP curations, and somatic-vs-germline interpretations via REST API, weekly VCF, or bulk XML. Use when determining clinical significance, triangulating conflicting interpretations, or aggregating evidence against the ACMG/AMP framework with ClinGen SVI specifications.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.

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。

lilinji/GeneTind-Life-Skills142026年8月21日 更新

This skill recommends bilateral adrenalectomy for patients with Cushing's syndrome when bilateral adrenal pathology is confirmed as the source of hypercortisolism. Clinical triggers include bilateral macronodular adrenal hyperplasia (BMAH), bilateral adrenal adenomas, or bilateral adrenal carcinoma identified by imaging.

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。

dromlakhani/MD2SKILL142026年10月5日 更新