「builder」の検索結果

Build business cases with cost-benefit analysis, ROI calculations, risk assessment, implementation timeline, and stakeholder analysis

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。

TRIGGER THIS when creating or updating employee handbooks, documenting HR policies, developing code of conduct, building benefits guides, or creating leave/PTO policies. Creates comprehensive, legally compliant, accessible employee handbooks covering company policies, culture, benefits, code of conduct, and all required disclosures.

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。

TRIGGER THIS when preparing interviews, planning hiring processes, creating interview questions, building evaluation frameworks, developing interviewer guides, or standardizing interview approaches. Builds complete interview kits with competency-based questions, scorecards, rubrics, interviewer training, and structured evaluation protocols.

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 professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.

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 the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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。

Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).

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 MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world 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。

SEO keyword clustering with topic organization, content hub architecture, internal linking strategies. Use for content strategy, keyword research, pillar page structures, or encountering cluster organization, hub architecture, internal linking errors.

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 a cumulative daily or retrospective sweep of research papers on a chosen topic, audit their claims, methods, integrity signals, and independent support, then identify overlooked but feasible project or business opportunities in a detailed source-grounded report. Use when asked to monitor papers every day, mine buried research, evaluate whether a paper is credible or reproducible, find unimplemented research ideas, or separate promising work from hype, weak evidence, and retracted or contradicted results.

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。

Inspect an unfamiliar repository, turn a focused Markdown behavior scenario into a deterministic test in the repository's native test stack, run it, and preserve traceability between intent and code. Use when asked to add scenario tests, compile acceptance criteria or Given/When/Then Markdown into executable tests, reproduce a user-visible regression, or convert a narrow workflow specification into stable web, API, CLI, desktop, or mobile interaction coverage. Do not use for broad exploratory journeys or agent-judged smoke tests.

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。

Provider-neutral deep-research orchestration for brainstorming, technology choices, comparisons, and state-of-the-art surveys. The Research Lead makes scope and decision calls while native subagents scout, search, verify, synthesize, audit, write, and commit every research artifact.

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 the user asks to run or continue an autonomous build: turn a goal into an approved plan, dispatch implementation and review agents, and finish with one pull request. The Lead is a provider-neutral orchestrator that makes decisions but never writes code, edits files, runs commands, or reviews its own 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。

Transform saved links, papers, articles, posts, videos, and reference collections into approachable AI teaching artifacts for later study. Use when a user wants to queue learning material, create a readable explanation from a source, teach a paper or post step by step, or run an interactive tutor that validates understanding over multiple sessions.

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。

Map the big picture around a topic or software repository by identifying its boundaries, layers, actors, components, relationships, flows, history, fault lines, and open questions, then explain how the pieces fit together and where to look next. Use when a user asks to understand a whole field, domain, technology, ecosystem, industry, codebase, architecture, unfamiliar repo, or phrases such as "give me the big picture", "map the landscape", "how does this all fit together?", "help me get oriented", or "what am I missing?"

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 an honest market-fit and viability audit of any project or idea and produce a decision doc, not code. Use when someone asks "is this viable as a business", "audit the market fit", "make the case and compare to competitors", "should I apply to YC or bootstrap", or wants to validate a project, product, or market before investing more time. Gathers verifiable traction data first, researches the competitive field, gets an independent cold read, writes a doc with pre-committed pass/middle/kill criteria and a concrete week-1 assignment, then hardens it with an adversarial review loop.

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, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture, shop, studio, or organization; derive a name from an existing project or concept; create a company and product naming system; compare candidates; or check company, domain, package, repository, marketplace, or trademark conflicts. Default to a fast no-browse creative pass; use current validation when the user requests availability or research, or is choosing a final name. Do not claim legal clearance, purchase domains, or present stale or unverified availability as fact.

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。

Indie maker playbook for bootstrapped apps inspired by Pieter Levels' MAKE: The Indie Maker Blueprint. Use when a user is starting or sizing an app idea, choosing what to build next, building a minimal product, preparing a launch, diagnosing stalled growth, deciding pricing or monetization, automating operations, maintaining a per-app MAKE.md tracker, or evaluating whether to sell a product.

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。

Inspect an unfamiliar repository, interpret a broad Markdown user journey at runtime, operate the real product through its supported web, API, CLI, desktop, or mobile surface, and produce an auditable pass, fail, or blocked judgment with screenshots, logs, recordings, and a step timeline when available. Use when asked to run smoke tests, validate an end-to-end user journey, dogfood a product, test a release candidate, or execute a non-deterministic workflow that may include authentication or human checkpoints. Do not use as a substitute for deterministic unit, integration, or scenario tests.

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。

Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current paradigms, a complete roadmap or curriculum, deliberate practice and feedback, or a top-1%-level mastery plan. Answer explanation questions completely before offering a curriculum; route explicit mastery goals to an adaptive practice and proof-of-capability system.

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。

Profile and debug Hermes sessions from their JSONL transcripts. Find a session and its subagents, build a queryable event table, summarize the work as a hierarchical table of contents, break down wall time, inference, tools, tokens, and estimated cost per agent, identify errors and improvement opportunities, and export a shareable Perfetto trace. Use when a user wants to inspect what a Hermes session did, debug agent or subagent activity, understand session cost or latency, create a session timeline, generate a trace, or learn how to improve the next agent run.

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 an agent is asked to draft, rewrite, edit, review, polish, copyedit, simplify, humanize, or create written prose, including creative writing, essays, posts, scripts, speeches, emails, documentation, product copy, and other style-sensitive text. Apply George Orwell's six rules and ASD-STE100 Simplified Technical English as a plain-English discipline while preserving the user's intended meaning, audience, tone, and explicit constraints.

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。

Analyze a software repository at the latest remote main commit and turn its implemented architecture into a citation-backed interactive isometric system map with a legend, selectable infrastructure buildings, dependency and payload flows, and plain-language learner explanations. For eligible public GitHub repositories, also contribute the verified map to tamdogood/CodeTerrain with a pull request. Use when a user provides a repository URL or asks to visualize, explore, learn, explain, or map a repo's architecture, infrastructure, runtime control flow, data flow, services, queues, stores, external systems, or deployment topology in an interactive UI.

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 or update a complete repository skill from a user's idea, including the workflow instructions, references, scripts or assets, agent metadata, skill-card artwork, cinematic banner artwork, README links, discovery metadata, and validation. Use when the user asks to create a new skill, add a skill to this collection, turn a workflow into a reusable skill, or make a skill's documentation and artwork consistent with the repository.

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。

Apply a disciplined engineering workflow to any code change. Use whenever implementing a feature, fixing a bug, or refactoring — before writing code, not after. Walks orient → baseline → smallest change → test → verify → self-review, and enforces language-agnostic hard gates (don't mass-reformat, keep the linter and type-checker clean, keep the build and tests green, make interface changes additive, protect security invariants).

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。