「pipeline」の検索結果

Ambivo integration. Manage Organizations, Pipelines, Users, Filters, Activities, Notes and more. Use when the user wants to interact with Ambivo data.

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。

Affinity integration. Manage Organizations, Leads, Pipelines, Users, Roles, Filters. Use when the user wants to interact with Affinity data.

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。

Agiled integration. Manage Organizations, Leads, Pipelines, Users, Goals, Filters. Use when the user wants to interact with Agiled data.

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。

Accelo integration. Manage Organizations, Leads, Pipelines, Users, Goals, Filters. Use when the user wants to interact with Accelo data.

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。

Recover data from archived REST APIs using Wayback Machine. Use when: (1) Recovering data from defunct services like Vine, Twitter, or other platforms, (2) Need to find which API endpoints were archived vs which require authentication, (3) Building data recovery pipelines from web.archive.org. Covers CDX API queries to discover archived endpoints, understanding what gets archived (public) vs what doesn't (authenticated), and extracting embedded JSON from archived web pages as a fallback.

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。

CRITICAL: Never modify files in content-addressable storage systems where the filename IS the content hash (SHA256, IPFS CID, etc.). Use when: (1) Working with Blossom protocol, IPFS, or any CAS system, (2) Considering "optimizing" stored files (faststart, compression), (3) Implementing transcoding or processing pipelines for hash-identified content, (4) Building on top of ProofMode or any cryptographic verification system. Modifying files in place breaks hash verification and content integrity.

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。

AutoMCM-Pro industrial-grade math modeling agent (opencode binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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。

AutoMCM-Pro industrial-grade math modeling agent. Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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。

AutoMCM-Pro industrial-grade math modeling agent (DeepSeek Harness binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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。

AutoMCM-Pro industrial-grade math modeling agent (Codex CLI binding). Supports AP (AI-led) and Manual (human-spec-led) dual modes with mandatory GitOps checkpoints, forced self-verification of all solver code before LaTeX inclusion, and structured human cross-validation at each pipeline stage. Use for both CUMCM (Chinese) and MCM/ICM (English) competitions.

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 test suites and suggests optimal test execution order based on code change impact and project requirements. Use when optimizing CI/CD pipelines, deciding which tests to run before committing, or selecting critical tests under time constraints. Prioritizes tests by analyzing recently modified files, test dependencies, and coverage overlap. Outputs detailed rankings with priority scores and reasoning to help teams run the most impactful tests 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。

Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with RTL designs that need formal property generation, when adding assertions to existing RTL, or when users ask to infer properties, generate assertions, or create formal specifications from hardware designs.

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。

Generate configuration files for applications, services, and infrastructure. Use when: (1) Setting up new projects (package.json, requirements.txt, tsconfig.json), (2) Creating Docker or Kubernetes configurations, (3) Configuring CI/CD pipelines (GitHub Actions, GitLab CI, CircleCI), (4) Setting up web servers (Nginx, Apache), (5) Defining infrastructure as code (Terraform, CloudFormation), (6) Generating linter/formatter configs (ESLint, Prettier, Black). Provides templates and custom-generated configs for diverse tech stacks.

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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Full ICP-to-leads pipeline. Describe your ideal customer in plain English and get a ranked table of enriched decision-maker leads with emails and phone numbers.

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。

ML paper pipeline: experiment design to submission.

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。

Social Media Manager role skill. Orchestrates the full SMB social media workflow across three layers — Foundation (brand setup + calendar), Content Creation (captions, platform-specialist posts, visuals), and Distribution (scheduling via Blotato + performance review). Coordinates all 9 component skills as a sequential, human-reviewed pipeline. Run this skill instead of invoking component skills individually.

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 marketing pipeline for apps, dev tools, libraries, and SaaS products in 2026. Generates positioning, listing/ASO/package-SEO assets, press kit, launch plan, social calendar, email sequences, growth/referral strategy, paid playbook, retention, and community plan. Adapts automatically to app vs dev-tool vs SaaS. Trigger on: marketing, launch plan, app launch, go-to-market, ASO, press kit, product hunt launch, hacker news launch, dev tool marketing, growth strategy, launch checklist, marketing plan, distribution 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。

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

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。

Extracts executable task definitions from Markdown files and runs them as shell pipelines. Parses fenced code blocks with task metadata annotations and manages dependencies between tasks.

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。

Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.

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Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.

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 writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.

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。

Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.

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。