「upstream」の検索結果

"Analyze and mitigate the bullwhip effect where demand variability amplifies upstream in supply chains. Use this skill when the user needs to diagnose order variability amplification, quantify the bullwhip ratio, or implement dampening strategies — even if they say 'why are our orders so volatile', 'supply chain variability', or 'demand amplification problem'.".

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。

Procedure for controlled, human-gated outbound README badge provisioning PRs (Issue #1817). Preflights upstream repositories, ensures single-badge placement with deep links, and records campaign outcomes without automated retries or multi-badge noise.

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。

Broad, pre-curation evidence reconnaissance for a declared Gaia skill, intake batch, upstream repository, or proposed generic capability. Run before choosing a discovery strategy or mapping topology, to give the curator independent research leads and counterevidence. Context only: never scores, grades, ratifies, ingests, or mutates the registry. Later ev-discovery and ev-pipeline phases validate/promote any selected leads.

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。

Sync suite skill installation instructions with upstream sources and ensure Gaia-compliant styling.

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 bounded, read-only semantic advisory sidecar passes for candidate mapping, issue triage, upstream diffs, evidence verification triage, and meta audit hints. Enforces monthly spend limits with explicit fallback to worker-luna.

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。

Close out a review/meta branch that lands an evidence-ingest / curation batch: create the named nodes, ingest verified evidence, appraise Trust Magnitude, calibrate stars, wire suites, correct upstream naming, stage ONLY the intended artifacts (dropping CRLF churn and blocking leaks), validate, and open the PR. Use when a curation/ingest batch is verified and ready to land, or when the user says "close the review/meta branch", "land the ingest batch", "wrap up the intake PR", "/gaia-review-meta-close". Wraps the deterministic hygiene in scripts/review_meta_close.py and gates the judgment calls (calibration, Origin, suite promotion, naming, merge) behind explicit human checkpoints.

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。

Updates this skill pack from upstream, and owns the consent that decides whether it ever updates on its own. Checks whether the clone is behind, asks the user — update now, always keep me up to date, not now, or never ask again — and carries out the answer: applies the update through ./scripts/update.sh, records the consent flag the SessionStart hook already reads, snoozes on an escalating ladder, or turns the check off for good. Summarizes what changed between the two commits from the CHANGELOG. Use when the user asks to update or upgrade the pack, get the latest skills or the newest version, when the session banner reports that updates are available, when they ask whether their clone is current, when they want unattended updates switched on or off, or when they want to stop being asked. It never applies an update without an answer, and notify-only is the default.

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。

Leveraged-prototype dev loop (`pulp loop`) — focus marker plus normal watch/rebuild loop, with AOT analyzer guidance and deferred ar-swap/PR-monitor playbook. Use when porting an existing UI/bundle to Pulp, doing visual/behavioral parity work, or batching upstream framework gap fixes.

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。

Update Pulp's pinned Skia, Dawn, and optional V8 prebuilts as one milestone-matched render-toolchain release. Use for requests such as "update Skia/Dawn", "move to chrome/mNNN", "find the matching V8", "update the GPU toolchain", or "use the Skia/Dawn/V8 tuple that goes together". Distinguishes milestone-matched V8 releases from v8-builder's newer weekly LKGR releases, verifies exact upstream provenance and asset hashes, updates every Pulp mirror, and runs the provider-identity/ODR gates.

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 when a task needs the judgment of a Data Engineer — designing an idempotent pipeline operation so retries and backfills don't corrupt data, choosing event-time over processing-time partitioning for time-series aggregation, enforcing schema compatibility rules before an upstream change propagates downstream, adding automated data quality checks to a pipeline, or deciding between batch and streaming architecture for a specific freshness requirement.

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 when a task needs the judgment of a Compensation, Benefits, and Job Analysis Specialist — market-pricing a job against salary survey data, scoring a job through a point-factor or IPE job evaluation, running a pay-equity regression or cohort analysis, building or refreshing a salary structure and compa-ratio bands, classifying a job family's FLSA exemption status through the duties test, or modeling benefits plan cost (loss ratio, PEPM, self-funded vs. fully insured). Distinct from hr-people-manager (owns the comp/leveling judgment calls — whether to approve an exception, promote someone into a level), hr-specialist (owns individual-case leave/I-9/ADA/FLSA reclassification for one employee's drifted duties), and compensation-benefits-manager (owns pay philosophy, negotiation-range policy, and the department that runs this work). This role owns the mechanics: the survey match, the point score, the regression, the band math — the analysis those upstream policy and downstream judgment calls are made against.

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 when a task needs senior auto-service-technician judgment — diagnosing a check-engine-light or drivability complaint from DTCs and live data, deciding whether a code-named part actually needs replacing or the fault lies upstream, scoping a flat-rate repair order and labor time, choosing warranty vs. customer-pay job costing, or separating a genuinely needed repair from an upsell.

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。

Stages and commits the current changes onto a safe working branch, enforcing branch discipline and optionally pushing upstream. TRIGGER when: the user wants to commit, save, or check in the current changes. DO NOT TRIGGER when: opening a pull request → pr.

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。

Rebases a feature branch onto its latest base, resolving conflicts while preserving the intent of local changes, optionally force-pushing and watching CI afterward. TRIGGER when: the user wants to rebase a feature branch, update a branch against its base, or replay local work on top of the latest upstream. DO NOT TRIGGER when: merging a release branch into dev → merging-branches; only watching CI on an already-open PR → monitoring-pull-requests.

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。

Record a finding about the configured engine MCP server into .unikit/MCP-RECHECK-NOTES.md — the project's log of what has to be re-checked here. Use right after a call reported success while changing nothing, ate an argument, or validated a broken state, e.g. "write this down", "record this MCP finding", "the server lied — note it", "add a recheck note", "trap this". Takes three forms of input: the finding in one line, a path to a plan file (harvests the "## MCP Findings" table of that plan and nothing else), or nothing at all — then it takes findings already in the session first, and offers to scan the tables of plans touched since the last audit. The table only, never the plan body. Writes in the one allowed genre — a check to perform; never a lifted gate, never a named workaround, never a claim about what the server can or cannot do. Makes zero MCP calls and needs no editor. To replay, retire, or upstream existing notes use /unikit-mcp-audit 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。

Adversarial project planning workflow. Explores the problem space, drafts tickets in batches, then runs sequential adversarial loops — a mandatory UX loop ("should we build this?"), zero or more discretionary specialist loops (security, performance) for projects with architectural implications in those domains, and a mandatory implementer loop ("could we build this?") — to find gaps before implementation. Creates well-specified, batch-tagged tickets upstream only after every applicable loop signs off.

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 with DecisionsAI when a ticket or workflow needs internet research — social posts, articles, video subtitles, GitHub, RSS, or competitor scans. Routes through Agent Reach upstream tools; run agent-reach doctor first.

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。

Complete shadcn/ui component management for adding, searching, fixing, styling, and composing UI in any React project. Use this skill whenever the user mentions shadcn, shadcn/ui, adding UI components, building forms or dashboards with Radix, working with component registries (@shadcn, @magicui, @tailark), or any task involving npx shadcn CLI commands. Also trigger when the user asks to fix styling issues, build a settings page, dashboard, or modal flow in a project that uses shadcn/ui — even if they don't say "shadcn" explicitly. This skill manages the full component lifecycle: search, add, view, diff, smart-merge upstream updates, and enforce critical rules for forms, composition, styling, and icons.

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。

Research process for comparing frontier harnesses (Claude Code, Codex, OpenClaw, autoresearch, Prime Agent) with the original/upstream implementation and performing GAP analysis before feature implementation. Triggered by "research" ("리서치"), "gap", "frontier", "harness", "case study" ("사례 조사"), "pattern exploration" ("패턴 탐색"), "comparative analysis" ("비교 분석") keywords.

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 the Xero MCP server — troubleshoot its self-refreshing OAuth2 token file, maintain the local best-of-breed server build, and pick up other operational notes for working with Xero MCP tools. Use when Xero MCP tools fail with authentication errors, before starting any Xero workflow, when adding or updating upstream xero-mcp-server PRs, or for general guidance on Xero MCP usage.

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 local lessons to every use of the Orca CLI (orca-ide, orca-dev). Use alongside the upstream orca-cli skill whenever the Orca CLI is needed, including before creating a branch worktree or asking the user for feedback on a change before committing while running inside Orca.

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 local reliability, viewport, tab-safety, stale-ref, widget, and browser-boundary lessons to every agent-browser automation. Use alongside the upstream agent-browser skill whenever navigating, clicking, filling, testing, extracting, taking screenshots, or debugging any website with agent-browser.

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。

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.

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。