「downstream」の検索結果

Summarizes data lineage graphs on Google Cloud to help users debug data quality issues and understand data provenance for BigQuery and Cloud Storage. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

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Analyzes the downstream impact (blast radius) using data lineage on Google Cloud when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.

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。

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

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。

Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.

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。

Applies standard AI review policy guidelines for downstream (test/QA) phase reviews.

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。

Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq c_curve / lc_extrap, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT — see Tips), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for downstream cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for downstream visualisation.

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Record upgrade instructions alongside a Prisma 8 breaking-change PR, so downstream consumers (users of `@internal/*` and authors of Prisma 8 extensions) can apply the matching code translation automatically via the published upgrade skills. Use when you have refactored framework code and the test suite went red in `examples/` or `packages/3-extensions/`, when you fixed those red tests by updating example or extension code, when you are told to "record upgrade instructions for this PR", or when you made a breaking change to Prisma 8 that downstream consumers will need help migrating across.

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Refines broad, vague, or aspirational biomedical research objectives into clear, bounded, measurable, executable, and downstream-ready study objective statements. Always use this skill when a user has a general aim such as “explore a mechanism,” “study prognosis,” “investigate biomarkers,” or “look at treatment response,” but the objective is still too broad, non-operational, or too ambiguous to support protocol framing, design selection, analysis planning, or hypothesis design. Never assume that polished wording alone means the objective is actionable. Focus first on objective type, missing operational elements, scope discipline, and downstream-ready formulation.

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Clarifies a vague clinical or biomedical research idea into a structured, bounded, searchable, researchable, and testable question. Always use this skill whenever a user has an early-stage clinical or research thought, an over-broad topic, an ill-defined evidence question, or an unclear problem statement that must be translated into a question framing suitable for literature retrieval, evidence synthesis, gap analysis, study design, or downstream protocol planning. Never jump straight to answering the substantive medical question unless the user explicitly asks for that. Focus first on question framing, boundary setting, and downstream-ready formulation.

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Trace relationships between semantic models and downstream reports across Fabric workspaces. Automatically invoke when the user asks to "find downstream reports", "show report lineage", "impact analysis", "what depends on this dataset", "cross-workspace lineage", "which reports are connected", "get model dependencies", or mentions model-to-report dependency tracing.

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Walk the user through four directional axes (tone register, aesthetic philosophy, audience relationship, sensory ambition) and produce a structured aesthetic brief that downstream content, copy, design, and art-direction skills consume as required input. The aesthetic depth layer, distinct from `creative-brief` (operational kickoff: scope, audience, deliverables, constraints). Use when a project needs aesthetic coherence across many small decisions and the user has only a vague feeling, or when multiple downstream aesthetic-producing skills need a shared brief. Triggers on creative direction, aesthetic direction, set the aesthetic, define the visual direction, what's the vibe, what's the tone, the four axes. Does NOT fire for a general kickoff brief (use `creative-brief`), tactical single-piece work, already-documented direction, purely functional output, or locked production-stage work.

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Verify ASP.NET Web services build the right IDownstreamApi route and payload without standing up HTTP

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On-Behalf-Of (OBO) Flow for web APIs to call downstream APIs while preserving user identity in MSAL.NET

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Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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Use when reviewing a .NET pull request for breaking changes that may affect other microservice repositories. Detects cross-repo API, DTO, endpoint, EF entity, and NuGet-package breaks, and checks whether a compatible downstream PR already exists. Keywords: cross-repo impact, breaking change, microservice compatibility, downstream break, compatible PR, blast radius review, api compatibility, contract break, dto shape change, endpoint break.

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Evaluates downstream tests for coverage and edge cases.

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Cascade merge changes through a chain of dependent branches. Use when updating stacked/chained PRs, propagating changes from a parent branch to all downstream branches, or when the user mentions "cascade merge", "propagate changes", "update dependent branches", or "merge to downstream branches".

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。

Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.

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。

A presentation system for agent-made PPTs — born for the talk, not just the template. It turns raw material into an AST (audience-state-transfer) outline with per-page visual-enhancement decisions (image / SVG diagram / video), hands a production brief to a downstream renderer (HTML-PPT skills or native PPTX), then runs a capped 3-round presentation checkup (演讲体检) on the rendered deck. It never renders slides itself. Use before generating PPT/HTML slides from raw material, and after rendering when the user says things like "给这份 deck 做演讲体检" or "PPT 渲染质检". If all you want is one beautiful template page with no outline and no checkup, a rendering skill alone is enough.

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Push the run's accepted artifact to a downstream collaboration surface (cli, other code agents, cloud, desktop) and stamp the artifact manifest with the export target.

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Condense any input — an idea, brief, PRD, transcript, or mixed notes — into a short spec: one kernel file plus supporting files that downstream skills build from. Also updates and validates existing specs. Use when the user says "create a spec", "distill this into a spec", "validate this spec", or "update the spec"

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for reverse engineering, malware, DFIR, firmware, pwnable, and native exploit challenges. Use when the user asks to reverse a binary, unpack a sample, inspect a memory dump or PCAP, recover malware behavior, debug a crash, or build or verify an exploit chain under sandbox assumptions. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for custom binary or text protocol recovery, handshake reconstruction, framing, sequence control, checksums, stateful replay, and accepted-session reproduction. Use when the user asks to decode an unknown protocol, recover custom framing, build a replay harness, satisfy sequence or checksum rules, replay a captured session, or prove the smallest message order that reaches an accepted branch. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.

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。