「end-to-end」の検索結果

Set up and end-to-end test the peoplereadme CLI (init, validate, schema changes). Use when verifying changes to src/peoplereadme/, schemas/persona.schema.json, or persona package scaffolding.

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。

How to test the peoplereadme CLI end-to-end, including the fidelity harness (eval/calibrate) with a synthetic persona fixture and live LLM calls.

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 this skill to drive a research question (RQ) end-to-end: validate the RQ README, generate a fill batch-plan, start the Docker batch in the background, monitor progress, run aggregation, and propose findings updates. Trigger when the user says "RQ-N voranbringen", "run-rq", "fill RQ-N", "run RQ-N", "Forschungsfrage N starten", or names a specific RQ-N directory in research/.

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 the speckit preparation phases end-to-end — specify, plan, critique, tasks — making all decisions autonomously. Stops before implementation.

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 the full speckit workflow end-to-end — specify, plan, critique, tasks, implement, review — making all decisions autonomously. Stops before extract; run that manually.

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。

Plan, write, and maintain automated tests in any language, with worked examples in Python and JavaScript/TypeScript. Use this skill whenever the user wants to write or plan tests, apply TDD (red-green-refactor), structure unit tests with arrange-act-assert and given-when-then naming, choose between fakes, stubs, and mocks, set up dependency injection for testability, build integration tests with testcontainers, database seeding, or transaction rollback, write Playwright or Cypress end-to-end tests, use property-based testing with Hypothesis or fast-check, create pytest fixtures or factory_boy factories, review coverage or mutation testing, or triage flaky tests in CI. Also trigger for "test pyramid", "test trophy", "test isolation", "mocking", "snapshot tests", "golden files", "parametrized tests", "make the tests pass".

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。

Orchestrate an end-to-end data analysis or machine learning project from decision framing through reproducible handoff. Use when a request spans multiple lifecycle stages or an ambiguous modeling request must become a decision-ready result; use narrower audit or experiment-design skills for isolated 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。

Use for multi-step / ambiguous / end-to-end work — refactor a whole module, drive a vague ticket to a verified result, plan+build+verify a migration; coordinates the reasoning chain across skills.

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。

Autonomous multi-step investigation — deep research carried end-to-end without per-step approval; explicit request only, never for normal feature work.

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 reviewing authorization end-to-end — route → gate → policy → query scope → response filter — before changes to permissions, tenants, ownership, or admin flows.

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 adding, running, or debugging proctmux end-to-end integration tests, especially PTY/TUI behavior, terminal snapshots, unified/client modes, timing flakes, VT100 emulator limits, or files under tests/e2e and internal/testharness/e2e.

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Crie suites de testes end-to-end confiáveis, rápidos e mantíveis que ofereçam segurança para fazer deploy de código rapidamente e capturem regressões antes que os usuários as encontrem.

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Designs an end-to-end process for finding, extracting, enriching, qualifying, and exporting leads. Use when the user wants a repeatable lead workflow plan rather than a single source plan.

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 the end-to-end Git workflow through PR merge in the current checkout. Use only when explicitly invoked.

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。

Add tree-sitter language support to codegraph end-to-end — wire the grammar + extractor, write tests, then benchmark extraction quality and retrieval value on 3 popular real-world repos. Use when the user runs /add-lang <language> or asks to add/support a new language (e.g. Lua, Elixir, Zig, OCaml) in codegraph.

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Use when managing Alibaba Cloud Simple Application Server (SWAS OpenAPI 2020-06-01) resources end-to-end, including querying instances, starting/stopping/rebooting, executing commands (cloud assistant), managing disks/snapshots/images, firewall rules/templates, key pairs, tags, monitoring, and lightweight database operations.

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Distributed machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).

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。

Create an atomic PR closing a GitHub issue end-to-end via a 6-step pipeline. Use to resolve, fix, or implement issue #N. Don't use for analysis without fixing (/issue-analysis), reviewing a PR (/issue-pr-review), or bulk backlog work (/auto-pilot).

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。

Review a PR end-to-end with CI checks, fix cycles, and optional auto-merge. Use for PR review, cleanup, or readiness checks. Don't use for creating PRs, raw issue analysis, or non-PR code review.

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End-to-end orchestrator: from a business idea, app idea, or existing app to running Azure deployment with cost estimates and pre-deploy approval. Analyzes your app, auto-detects the right Azure services, scaffolds infrastructure code, and deploys — tailored to your app, not a template.

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。

Optimize end-to-end application performance with profiling, observability, and backend/frontend tuning. Use when coordinating performance optimization across the stack.

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。

Automated end-to-end UI testing and verification on an Android Emulator using ADB.

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 coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.

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Use when user asks to "build a Solana dapp", "write an Anchor program", "create a token", "debug Solana errors", "set up wallet connection", "test my Solana program", "deploy to devnet", or "explain Solana concepts" (rent, accounts, PDAs, CPIs, etc.). End-to-end Solana development playbook covering wallet connection, Anchor/Pinocchio programs, Codama client generation, LiteSVM/Mollusk/Surfpool testing, and security checklists. Integrates with the Solana MCP server for live documentation search. Prefers framework-kit (@solana/client + @solana/react-hooks) for UI, wallet-standard-first connection (incl. ConnectorKit), @solana/kit for client/RPC code, and @solana/web3-compat for legacy boundaries.

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。