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Analyze, summarize, and extract insights from DeLive transcription sessions. Use when: user mentions DeLive, transcription, meeting transcripts, live captions, audio transcription, AI correction, corrected transcript, or transcript analysis; user wants to search, retrieve, summarize, correct, or process recorded transcripts; user asks about meeting notes, action items, discussion summaries, or transcript quality from DeLive. Requires DeLive app running locally with its MCP server or REST API.

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Fix password/secret authentication failures caused by trailing newlines when creating Google Cloud secrets (or similar) with bash here-strings. Use when: (1) Password authentication fails with correct password, (2) Secret created with `<<< "value"` syntax, (3) Error like "password authentication failed" or "invalid token" despite correct value. Bash here-strings (`<<<`) add a trailing newline that corrupts secrets.

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Automatically infer loop invariants for code verification and correctness proofs. Use when analyzing loops to identify properties that hold throughout execution, generating assertions for verification, proving loop correctness, or documenting loop behavior. Supports Python, Java, C/C++, and language-agnostic analysis. Generates invariants as code assertions (assert statements). Triggers when users ask to infer invariants, find loop properties, generate loop assertions, prove loop correctness, or verify loop behavior.

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Think and work like an expert Corrosion Engineer. Use when a task calls for Corrosion Engineer judgment. Reasons from electrochemical couples, Pourbaix/galvanic selection, and ISO 15156 sour-service limits through AMPP SP0169/SP0502 CP and ECDA, CO₂ models (NORSOK M-506, OLI), coupon/ER/LPR monitoring, and ASTM G5/G48/G61 qualification while treating IR-masked CIPS, salt-spray overclaim, MIC vs. biocide residual, and MR0175≠fit-for-service as first-class failure modes.

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Establish weekly customer-discovery cadence and Opportunity Solution Tree practice using Teresa Torres's Continuous Discovery Habits framework during PRD v1.0 Market Adoption. Triggers on requests to set up discovery cadence, build opportunity solution tree, run weekly customer interviews, or when user asks "Torres", "continuous discovery", "opportunity solution tree", "outcomes vs outputs", "weekly interviews", "assumption mapping". Outputs CFD-* discovery entries and updates to ADO-STAGE-* and PER-* with new evidence.

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World-class application logging - structured logs, correlation IDs, log aggregation, and the battle scars from debugging production without proper logsUse when "log, logging, logger, debug, trace, audit, structured log, correlation id, request id, log level, winston, pino, bunyan, log4j, logging, observability, debugging, monitoring, tracing, structured-logs, correlation, aggregation" mentioned.

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Produces structured summaries of insurance claims correspondence including coverage letters, reservation of rights (ROR), denial letters, and claim file documents. Extracts policy details, coverage positions, claims chronology, settlement posture, and liability exposure. Use when summarizing claim files, coverage disputes, ROR letters, denial letters, or multi-document insurance correspondence threads.

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Audits Solidity liquidation mechanisms for denial of service vulnerabilities including unbounded loops over positions causing out-of-gas reverts, data structure corruption preventing liquidation, front-running to block liquidation via nonce changes or self-liquidation, pending withdrawals forcing reverts, malicious ERC721/ERC20 callback reverts, collateral in external vaults not seized, insufficient insurance fund blocking liquidation, fixed bonus exceeding available collateral, incorrect decimal handling, conflicting nonReentrant modifiers, zero value transfer reverts, token deny list issues, and single borrower edge 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。

Use to analyze correction trends, surface recurring patterns, and graduate repeat corrections to guardrails or anti-patterns.

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。

Monitors cross-asset correlation patterns with regime-dependent analysis and diversification effectiveness assessment. Use when analyzing correlations, assessing diversification, or evaluating cross-asset relationships.

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Pivots on indicators of compromise across multiple data sources by correlating a seed set of IOCs against logs to find co-occurring indicators, hosts, and timeframes that expand the investigation. Activates for requests to pivot on IOCs, correlate indicators across data sources, or expand an investigation from a seed indicator set.

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Reviews audio DSP code for numerical correctness. Use when the user asks to review, audit, or check DSP code for correctness issues — filters, feedback loops, fixed-point arithmetic, accumulation loops, or any numeric computation in audio code. Trigger on "check this filter for NaN", "why does my reverb blow up?", "is this numerically stable?", "why do I hear DC in my output?". Pairs with audio-dsp-review (realtime safety); this skill covers numeric correctness. Flag issues proactively.

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。

Public relations, corporate communications, and analyst relations for B2B SaaS. Use this skill for earned media and press strategy, announcements and press releases, executive and crisis communications, media relationships and briefings, and the Gartner/Forrester/IDC analyst program including Magic Quadrant and Wave evaluation submissions. Also triggers on: PR, public relations, press release, earned media, media relations, journalist, embargo, announcement, crisis comms, corporate communications, analyst relations, AR, Gartner, Forrester, IDC, Magic Quadrant, Forrester Wave, analyst briefing, inquiry, newsjacking, rapid response, should we comment on this news story, expert commentary, reactive comment, source request, Qwoted, is this newsworthy.

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Use when a task needs the judgment of a Correspondence Clerk — selecting and customizing a template response to a standardized written inquiry, triaging a correspondence queue against SLA deadlines, deciding when a form letter suffices versus needs escalation, or auditing a response-template library for staleness.

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。

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。

Otimiza inferência de LLM com NVIDIA TensorRT para máxima vazão e latência mínima. Use para implantação em produção em GPUs NVIDIA (A100/H100), quando você precisa de inferência 10-100x mais rápida que PyTorch, ou para servir modelos com quantização (FP8/INT4), batching em voo e escalabilidade multi-GPU.

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Detect NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log analysis.

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Correlate events across multiple log files from different sources, systems, or time periods. Build unified timelines, identify causal relationships between events, and highlight anomalies that span multiple data sources. Supports timezone normalization and gap detection. Use when analyzing logs from multiple systems (frontend, backend, database, infrastructure), investigating distributed system failures, or building event timelines across services. Triggers: "correlate these logs", "build timeline from multiple logs", "cross-system analysis", "find related events across logs", "multi-source log analysis", "distributed trace analysis"

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Get external links to your repos through other people's curated surfaces: awesome-lists, tool catalogs and vendor showcases. Measures each list's merge queue before submitting, so nothing goes into a dead queue. Triggers: /borrowed-audience, /ba, borrowed audience, get external links, submit to awesome lists.

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。

Public relations, corporate communications, and analyst relations for B2B SaaS. Use this skill for earned media and press strategy, announcements and press releases, executive and crisis communications, media relationships and briefings, and the Gartner/Forrester/IDC analyst program including Magic Quadrant and Wave evaluation submissions. Also triggers on: PR, public relations, press release, earned media, media relations, journalist, embargo, announcement, crisis comms, corporate communications, analyst relations, AR, Gartner, Forrester, IDC, Magic Quadrant, Forrester Wave, analyst briefing, inquiry.

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。

Verifies bug fixes are correct and complete by tracing callers, checking imports/exports, validating path resolution, and confirming no regressions. Does the code work? Use when reviewing code changes for functional correctness.

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 NTLM relay attacks through Windows Security Event correlation by analyzing Event 4624 LogonType 3 for IP-to-hostname mismatches, identifying Responder/LLMNR poisoning artifacts, auditing SMB and LDAP signing enforcement across the domain, and detecting NTLM downgrade attacks from NTLMv2 to NTLMv1 using event log 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。

Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.

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。

Optimizes LLM inference with NVIDIA TensorRT for maximum throughput and lowest latency. Use for production deployment on NVIDIA GPUs (A100/H100), when you need 10-100x faster inference than PyTorch, or for serving models with quantization (FP8/INT4), in-flight batching, and multi-GPU scaling.

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。