「downstream」の検索結果

GStack spec method for turning fuzzy intent into an executable spec with goals, non-goals, acceptance criteria, risks, and downstream role boundaries.

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Discipline for repairing a false claim once it has propagated: select what depends on it, produce a replacement, and accept with an expiring verdict. Trigger when: - A claim, citation, premise, or documented fact is found wrong and its downstream consequences must be traced and fixed. - A refutation, a failed re-run, a retracted external source, or a human correction lands against something already closed or already promoted. - Prompt contains: /remediation, remediation, retraction, false claim, propagated error, scrub, correction propagation, born-false, became-false, currency verdict, impeachment queue.

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Efficiently extract and convert the contents of any local file—such as PDF, DOCX, DOC, ODT, RTF, XLSX, XLS, or HTML—into clean, well-formatted markdown saved to disk. Use this skill whenever the user requests to parse, read, or extract information from a file on their computer, including phrases like “parse this PDF”, “convert this document”, “read this file”, “extract text from”, or when a local file path (not a URL) is provided. This skill offers advanced options like generating AI-powered summaries and answering questions based on the file's content. Prefer this tool over `scrape` when handling local files to deliver precise, structured outputs for downstream tasks.

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Set up notebook-connector configuration for Exasol AI workflows through the Secrets Python API. Covers secure config store (`scs`) creation, the `AILabConfig` and `StorageBackend` key sources, backend-specific config values such as `db_host_name`, `db_schema`, `storage_backend`, and `huggingface_token`, Python validation, and handoff to downstream notebook-connector skills.

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Translate a fuzzy analytical question into a rigorous investigation plan. Interrogates the ask, grounds the plan in the available data dictionary, applies analytical best practices, and produces a structured brief of query specifications for a downstream query-writing skill. Plans, does not write SQL. Use when: "why did X drop", "is there a relationship between A and B", "who are our highest-value customers", "what's driving the change in Y", "investigate this trend", "design an analysis for", "scope this analytical question". (narrative-common)

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Analyze a local codebase and infer the architecture it is actually using, including repo kind, best-fit dominant architecture, directly evidenced coexisting patterns, major subsystem exceptions, confidence, docs-vs-code drift, and repo-fit hints for downstream agents. Use when prompts ask what architecture a repo uses, whether it is really hexagonal or just layered, what subtle patterns shape a repo or slice, how a target slice fits a hybrid monorepo, whether documented architecture matches implementation, or when `/tw-tk` needs a repo-dialect preflight before making a minimal change.

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Load for Blazor BFF server work involving YARP proxy routes, cookie sessions, access-token forwarding/refresh, downstream API calls, BFF handlers, or browser-to-API auth failures; not for client component rendering or API JWT validation alone.

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Author a completed-spec fixture — a seeded intent graph plus a believable four-phase interview transcript — for a brunch feature, so it can feed any downstream tooling that consumes a completed spec or dogfood the UI. Use when you need a real completed specification in .brunch/brunch.db for testing or demo: pick a feature from memory/SPEC.md (or a free description), choose a stress profile (coverage|clean), and produce a registered scenario seed + an interview-questions doc that match exactly.

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Finds high-value SEO and GEO keywords using web search, AI analysis, and optionally paid tools like Ahrefs or Semrush. Produces a validated keywords.csv file with a fixed schema for downstream pipeline consumption.

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Use when a user provides a GitHub URL and the planner needs to converge that repo plus the user's goal into a concrete project blueprint for later execution. This skill handles GitHub-input project intake, Phase 1 intent convergence, and downstream project handoff structure. It does not generate teaching material packages.

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How to read submitted JetFormBuilder form data from a custom action, transform it (including %macro% replacement of admin-typed templates), call an external HTTP API, write the response back into the form context for downstream actions to use, and dispatch JFB events based on the API outcome. Use when building a custom action that integrates with a third-party service (LLM call, webhook, CRM, payment processor, geolocation lookup) and needs the action to behave as a node in a data-flow graph rather than a one-shot leaf. Triggers on mentions of "jet_fb_context", "update_request", "has_field", "get_value", "wp_remote_post" with form data, "macro replacement", or "API call from action".

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Build or update the shared product-marketing context: product, audience, positioning, competitors, jobs-to-be-done, objections, customer language, proof and goals. Use before downstream execution when context is missing.

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Apply before writing logic: choosing core types and data structures, sequencing scaffold-vs-feature work, asking what concurrent actors share. Get the data structures right so downstream code becomes obvious.

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Add or define specific fields in a collection's readSchema. Use when fields are missing from materialization field selection, pruned by complexity limit, need type refinement, need to be protected from future inference changes, or need to be required for downstream consumers. Use when user says "missing fields", "pruned fields", "field not showing up", "complexity limit", "define field in read schema", "restrict field type", or "protect field from inference".

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Create a bookkeeping context profile before downstream bookkeeping work. Captures entity type, tax jurisdiction, accounting method, chart of accounts baseline, connected tools, approval thresholds, record-retention policy, and review boundaries. Trigger on "set up my bookkeeping", "bookkeeping context", "chart of accounts", "cash or accrual", "configure my books", "what info do you need before bookkeeping".

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Scan downstream specs for contradictions with locked decisions and apply approved fixes with consistency checks

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Pre-scan decision types, collect downstream references, classify contradictions, and write propagation scan report

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Cascade approved evolution changes through downstream layers, assess impact, run consistency checks, and write evolution audit record

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Capture and classify new scope, write entry-point updates, and determine downstream cascade scope

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Introduce genuinely new scope into the pipeline by classifying change type and cascading updates through downstream layers

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Safely evolve shared contracts with consumer discovery, migration tests, versioning checks, and downstream spec cascade validation

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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.

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End-to-end Ribo-seq analysis from FASTQ through periodicity QC, P-site calibration, ORF detection, translation efficiency, and stalling. Use when orchestrating a full ribosome profiling pipeline and deciding harvest/dedup/alignment options and which downstream analyses the library can support.

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End-to-end multi-omics integration workflow. Orchestrates data harmonization, MOFA/mixOmics integration, factor interpretation, and downstream analysis across transcriptomics, proteomics, metabolomics, and other modalities. Use when integrating multiple omics datasets.

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。