27 件 ・ 関連度順
概要と使いどころ
Conducts pre-DPIA threshold screening to determine whether a full Data Protection Impact Assessment is required under GDPR Article 35. Applies the EDPB WP248rev.01 nine-criteria test, national supervisory authority blacklists, and organisational risk appetite to produce a documented screening decision. Keywords: threshold screening, DPIA trigger, pre-DPIA, WP248, Article 35(1), blacklist, screening decision.
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。
GDPR Data Protection Impact Assessment (DPIA) guidance under Article 35 GDPR, EDPB Guidelines WP 248 rev.01, EDPB Opinion 28/2024 (AI), and national SA blacklists/whitelists. Triggers: "DPIA", "DSFA", "Datenschutz-Folgenabschätzung", "impact assessment", "Art. 35", "do I need a DPIA", descriptions of new high-risk processing (profiling, AI, biometrics, large-scale monitoring, special category data), Art. 36 prior consultation questions, national blacklist/whitelist queries.
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。
Run or plan ATAC-seq QC, alignment, TSS enrichment, fragment-size, blacklist, peak-calling, consensus peak, and differential accessibility 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。
Darwin Skill 2.0 (达尔文.skill 2.0): autonomous skill optimizer, v2.0 integrates Microsoft Research SkillLens (arXiv 2605.23899) 9-dim rubric + SkillOpt (arXiv 2605.23904) validation-gated design + human-in-the-loop checkpoints. Evaluates SKILL.md files using a 9-dimension rubric (structure + effectiveness + meta-skill blacklists), runs hill-climbing with git version control, spawns independent judge agents for blind evaluation, validates improvements through test prompts with auto-break on diminishing returns, and generates visual result cards. Use when user mentions "优化skill", "skill评分", "自动优化", "auto optimize", "skill质量检查", "达尔文", "darwin", "帮我改改skill", "skill怎么样", "提升skill质量", "skill review", "skill打分".
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。
Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC score", "make text human-like", "去ai化", "改成人话", "去机器味", "降低AI率", "过AIGC检测", "长文本改写", "小说改写"
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。
Reverse-engineer a company's sales and marketing tech stack from public signals. Detects CRMs, cold email tools, people databases, ad pixels, email delivery services, and outbound sending domains via DNS records, website source inspection, Apify technology profiling, blacklist checks, and public spam complaint searches. Works on single companies or batches. Outputs a structured markdown report per company.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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 set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.
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 whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
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。
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
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。
Design critique with 7 rated passes: Information Architecture, Interaction State Coverage, User Journey and Emotional Arc, AI Slop Risk (10-item blacklist), Design System Alignment, Responsive and Accessibility, Unresolved Decisions. Each rated 0-10 with gap-fill loop. Parallel cross-model design voices.
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。
Live-site visual QA audit with fix loop. 10-category checklist (~80 items) including AI Slop Detection (10 blacklisted patterns). Atomic commits per fix. Before/after screenshots. A-F grading. Parallel cross-model code-level design audit. Modes: Full, Quick, Deep, Diff-aware, Regression.
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。
Triage playbook for when cold email deliverability breaks. Decision-tree guidance for "I landed in spam", "bounce rate spiked", "domain blacklisted", "inbox blocked in warmup", "reply rate dropped". Tells you what to check first, what to fix, and how long the fix takes. Pair with /email-deliverability-audit for diagnosis.
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。
Extract exactly 8 context-sensitive keywords from Chinese, English, or mixed text and turn them into a distributed weighted Graph View with no center goal node. Use when Codex needs keyword extraction, blacklist filtering, co-occurrence edges, node definitions/notes, weighted graph JSON, or an online Graph View tool for text analysis.
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。
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
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。
Scan the current market landscape and produce a ground-truth ecosystem snapshot. Chains /last30days for live research, validates with user, and writes brand/landscape.md with a Claims Blacklist that hard-gates all content generation. Use when: "landscape", "ecosystem", "market snapshot", "ground truth", "what's happening", "refresh landscape", "market trends", or before any content campaign to verify claims.
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。
Email marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup — plus the contact budget: a cross-channel frequency cap across every sender (marketing, sales sequences, customer success, in-app). Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate, BIMI, brand logo in inbox, VMC, Verified Mark Certificate, Common Mark Certificate, transactional email, password reset emails going to spam, frequency cap, email fatigue, customers get too many emails, too many messages, sales and marketing both emailing the same person, communication limits, send frequency safeguard, global frequency capping, quiet period, message precedence.
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。
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
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。
Email marketing operations and automation for B2B SaaS. Use this skill for newsletter strategy, email automation sequences, lifecycle campaigns, nurture workflows, onboarding sequences, deliverability optimization, email copywriting, lead scoring, marketing automation platform setup, and cold-email sending infrastructure — secondary sending domains, SPF/DKIM/DMARC, and domain warmup. Also triggers on: email, newsletter, drip campaign, lifecycle, nurture, onboarding sequence, deliverability, email automation, lead scoring, marketing automation, our emails are going to spam, SPF, DKIM, DMARC, sender reputation, sending domain, cold email domains, domain warmup, mailbox setup, blacklist, bounce rate.
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 set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.
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。
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
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 whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
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。
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
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 set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.
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。