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概要と使いどころ
生成 A 股医药行业日报：从新浪财经抓取 20 只核心医药股实时行情，分析 7 大细分赛道排名、涨跌榜 TOP 3、资金流向，然后通过飞书发送富文本日报。 覆盖恒瑞医药、百济神州、智飞生物、药明康德、迈瑞医疗、片仔癀、云南白药等 20 只核心标的。 当用户需要 医药日报、医药行业日报、pharma daily report、 新浪财经医药数据、A 股医药板块分析、飞书推送医药数据、 制药行业快报 时触发。也适用于"帮我看一下今天医药板块"、 "给飞书发一份今天的医药数据"等口语化请求。
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Use when asked 做一份外贸报价单, FOB 和 CIF 怎么报价, 出口报价核算, 报价怎么算利润, 退税怎么算进报价, quotation sheet for an export order, or price an export quotation from China. Produces a cost build-up from the factory price through export tax rebate, domestic logistics, port charges, freight and insurance to each Incoterms 2020 term, a quotation sheet in English with a Chinese summary, quantity tiers, validity and exchange-rate assumptions, payment terms and the risk each term puts on the seller, with rates marked to confirm.
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Infrastructure-as-code specialist for multi-cloud provisioning using Terraform across any provider (AWS, GCP, Azure, Oracle Cloud). Use for terraform plan/apply, state management, compute, databases, storage, networking, IAM, OIDC, cost optimization, policy-as-code, ISO/IEC 42001 AI controls, ISO 22301 continuity, and ISO/IEC/IEEE 42010 architecture documentation.
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Canonical short-form-ad audio mix in one FFmpeg pass. VO loudnorm + 3.0× per-clip + 2.0× mix, music 0.13 base + apad+afade, sidechain compress 20:1 @ 0.01, climax line +20%, optional video-to-music duration sync. Replaces the reactive multi-round tuning that cost v03 6+ passes.
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Assemble a creator picture-in-picture product-listicle ad from a config — the creator stays FULL-FRAME the whole beat (voice plus lips generated together per beat, no separate VO, no cut to a full-frame product shot), and on each product beat three persistent overlays ride on top for the WHOLE beat — a title pill top-center, the DEMO in a rounded PiP window top-right (the brand's real UGC clip MUTED, or for a no-UGC brand the product's own autocropped UI still / screen-recording sized to fill the window), and a bottom product card (rounded thumbnail plus 'N · CATEGORY' small-caps plus product NAME in a serif face). Hook plus CTA beats are the creator full-frame with the title pill only. Assembly builds ONE full-1080x1920 transparent overlay PNG per beat, overlays it on the creator clip (cover-scaled to 1080x1920) for the whole beat keeping the native audio, concats all beats, then burns captions LAST as timed PIL PNG overlays (this ffmpeg has no libass) timed deterministically from the known per-beat script. This is the FREE deterministic assembly stage (overlay-PNG build plus cover-scale composite plus concat plus PIL-PNG caption burn); the creator anchor and the N native talking clips come from create-image-fal (Seedream v5 Pro) and create-video-fal (Seedance 2.0). Use for the creator-pip-listicle format.
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Assemble a narrated-UGC "stitch reply" ad from a config — a single spoken VO carries a verbatim testimonial while ~30 per-cut i2v clips (one creator across ~5 wardrobes in ~3 worlds, plus product B-roll) are each trimmed to their EDL window built from the VO's Whisper word boundaries and hard-concatenated via filter_complex concat (never the demuxer, which drops audio on a duration mismatch), the VO mixed over an optional sidechain-ducked instrumental bed (−20dB, 20 to 1) so the VO stays on top, karaoke-pop captions burned on every word throughout (VEED Whisper preset, re-spelled against the locked script), a landing-page scroll rendered as FFmpeg zoompan over a Playwright PNG (not i2v), and closed on the brand's real end-card PNG — never AI-rendered text. This is the FREE deterministic assembly stage (trim-to-EDL + filter_complex concat + VO and music mix + karaoke captions + landing-page zoompan + end-card append), run with the shared stitch-videos-ffmpeg montage.py helper that installs with this package; the VO, creator, start-frames, and clips come from create-vo-elevenlabs / create-image-gpt-image-fal / create-image-fal / create-video-fal. Use for the narrated-ugc-wardrobe-stitch format.
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Assemble an animated character-explainer video ad (any art style — Pixar-style 3D, claymation, anime, paper-cut, felt) (~77s, 9:16) built on an "N types of X" listicle spine — a recurring human protagonist plus a locked cast of N persona characters, one per list item. Given the per-scene i2v clips + a per-scene target-duration table + a narration track, it trims each clip to its scene window, re-encodes every segment to identical 1080x1920/30fps/libx264/yuv420p (decrease+pad, never crop) so the concat demuxer never drops frames, concats, and muxes audio — in RESTYLE mode the source ad's VO+music mix is reused verbatim, in ORIGINAL mode fresh per-scene VO (loudnorm I=-14) is mixed under an optional music bed (loudnorm I=-26). A static-still fallback loops a scene's keyframe when its clip is missing/failed, so the master always assembles; libass captions are burned last. FREE deterministic assembly (Python + ffmpeg, no bash, no paid calls); the recipe supplies the clips, keyframes, VO or source audio, and caption table and gates the paid cast-anchor/keyframe/Kling-i2v/VO/music calls to their own capabilities. Use for the 3d-character-explainer listicle format.
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Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.
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Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
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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.
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Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
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Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
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Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), ancestry-conditional calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.
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Queries PharmGKB / CPIC / DPWG for drug-gene interactions; calls CYP2D6/CYP2C9/CYP2C19/DPYD/TPMT/NUDT15/UGT1A1/SLCO1B1 star alleles and phenotype with PharmCAT, Cyrius (CYP2D6 structural variants), Aldy, Stargazer; applies Caudle 2020 activity-score translation. Use when implementing pharmacogenomic-guided prescribing, applying CPIC vs DPWG guidance, screening HLA risk alleles for ICI / antiepileptics / abacavir, or interpreting compound TPMT+NUDT15 thiopurine risk.
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Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.
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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).
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Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
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Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
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Computes sample size and power for clinical trials including continuous, binary, and time-to-event endpoints; superiority, non-inferiority, and equivalence designs; FDA 2016 non-inferiority margin selection with M1/M2 framework; Schoenfeld 1981 and Lakatos 1988 for survival; Schuirmann TOST and 80-125% bioequivalence; minimum clinically important difference (MCID) vs δ distinction. Use when justifying trial size in protocol or SAP per CONSORT 2025 item 16a.
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Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
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Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
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Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.
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Transmit and capture RF waveforms using Wireless Testbench with NI USRP radios (X410, X310, N310, N320, N321, N300, X300, E320). Use when generating test signals, transmitting over the air, capturing IQ data, performing loopback tests, configuring multi-antenna setups, or troubleshooting dropped samples and gain settings. Covers basebandTransceiver, basebandTransmitter, basebandReceiver, continuous and once transmit modes, foreground and background capture, and UseRadioBuffer options. Also use when the user mentions transmit waveform, capture signal, IQ data, loopback, RF gain, sample rate, or antenna configuration.
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