Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
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。
Deep-research analysis of a codebase, a topic, or both. Clarifies the question, agrees the research angles with you, then runs a dynamic workflow that gathers cited claims per angle, merges duplicates, has independent skeptics try to refute every claim, and writes one cited report from the claims that survived. Use for "how does X work", "map the Y flow", "research Z", or any mechanism-level question that must be evidence-backed.
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。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
Act as the analysis orchestrator. You turn a request into a precise research question and a set of research angles, get the user to confirm them, then author and run one dynamic workflow (the Workflow tool with an inline script you write) that does the research, verification, and synthesis. You do not research or write findings yourself — nothing reaches the report unless a verifier opened its source.
Analysis Target: $ARGUMENTS
Always:
file:line or URL and a verbatim excerpt.docs/research/ is pre-authorized.Never:
If $ARGUMENTS is underspecified — no subsystem named, no scope, or ambiguous between several things
(e.g. "how does auth work" in a repo with session, API-key, and OAuth paths) — ask 2–3 clarifying
questions and keep asking until the user confirms the scope. Settle whether the answer comes from the
codebase, the web, or both. Weave the answers into one refined question.
Decompose the question into 2–6 angles that together cover it and can be researched concurrently by agents with no shared context. Split along the axis the question actually has — subsystem, data-flow stage, layer, external source, or sub-question. Angles must not overlap; do not pad a question that genuinely has two. Give each a 2–4 word label and a concrete focus.
Present the refined question and the angles. Ask the user to Launch, Adjust angles, or Refine the question.
These instructions are the opt-in: call the Workflow tool directly with an inline script. Do not load the workflow-authoring reference or assemble the script through shell commands first — runs that detour through either stall before launching. Read reference/workflow.md and the four agent references. Author one workflow script that implements
the contract exactly, with each agent's prompt taken from its reference and its placeholders filled.
Launch it with the Workflow tool, passing the question and the angles as args.
Choose model and effort per stage from what the stage does. Stages that fetch and quote sources, or
group claims by meaning, can run on a smaller, faster model at lower effort. Tracing a mechanism through
code, refuting a claim, and synthesizing the report need the strongest model. A verifier never runs on a
weaker model than the researcher whose claim it judges. When unsure, omit model so the stage inherits
the session model.
The run executes in the background. Do not research in parallel or fabricate progress.
When the workflow returns (shape in reference/workflow.md):
error or no confirmed claims, say so plainly and distinguish an infrastructure
failure (verifiers errored — retry) from a negative result (claims refuted on the merits).まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Context enrichment for agentic AI application development using LangChain, Vercel AI SDK, and assistant-ui. Use when building AI agents, chat interfaces, tool-calling pipelines, RAG systems, or multi-step AI workflows.
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。
REST and GraphQL API design patterns, OpenAPI/Swagger specifications, versioning strategies, and authentication patterns. Use when designing APIs, reviewing API contracts, evaluating API technologies, or implementing API endpoints.
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。
System architecture patterns including monolith, microservices, event-driven, and serverless, with C4 modeling, scalability strategies, and technology selection criteria. Use when designing system architectures, evaluating patterns, or planning scalability.
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。
Consolidate a completed specification into a single archived record and remove its working directory. Use as the final stage after implementation — distills requirements + solution into .start/archive/[NNN]-[name].md and deletes the .start/specs/[NNN]-[name]/ directory.
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。
Required first step before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design 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。
Unified code review skill for correctness, design, readability, security, performance, testability, accessibility, and error-handling conventions. Use when reviewing changes, enforcing quality standards, or identifying technical debt.
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。