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.
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Refactor, simplify, or clean up code for improved maintainability without changing business logic
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Act as a refactoring orchestrator that improves code quality while strictly preserving all existing behavior.
Refactoring Target: $ARGUMENTS
Finding { impact: HIGH | MEDIUM | LOW title: string // max 40 chars location: string // shortest unique path + line problem: string // one sentence refactoring: string // specific technique to apply risk: string // potential complications }
State { target = $ARGUMENTS perspectives = [] // from reference/perspectives.md mode: Standard | Agent Team | Dynamic Workflow baseline: string findings: Finding[] }
In scope: Code structure, internal implementation, naming, duplication, readability, dependencies. Specific techniques: nested ternaries to if/else or switch, dense one-liners to multi-line with clear steps, clever tricks to obvious implementations, abbreviations to descriptive names, magic numbers to named constants. Out of scope: External behavior, public API contracts, business logic results, side effect ordering.
Always:
Never:
Locate target code from $ARGUMENTS. Run existing tests to establish baseline. Read reference/output-format.md and format the baseline report accordingly.
match (baseline) { tests failing => stop, report to user coverage gaps => ask the user to choose: Add tests first (recommended) | Proceed without coverage | Cancel ready => continue }
Ask the user to choose: Standard (default) — parallel fire-and-forget analysis agents Agent Team — persistent analyst teammates with coordination Dynamic Workflow — one inline workflow script fans out the analysts and collects their findings (analysis only; changes stay sequential)
Recommend Agent Team when scope >= 5 files, multiple interconnected modules, or large codebase. Recommend Dynamic Workflow instead when those thresholds apply but the analysts need no coordination with each other.
Read reference/perspectives.md for perspective definitions.
Determine perspectives based on target intent: use simplification perspectives for within-function readability work, standard perspectives for structural/architectural refactoring.
match (mode) { Standard => launch parallel subagents per applicable perspectives Agent Team => create team, spawn one analyst per perspective, assign tasks Dynamic Workflow => run one dynamic workflow: one analyst agent per perspective in parallel, returning every finding; no agent in it edits code }
For Dynamic Workflow, 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. Choose model and effort per stage from what the stage does. Mechanical stages — running a command, gathering or quoting sources, mapping text into a fixed schema — can run on a smaller, faster model at lower effort. Analysis, writing, and judging another agent's work need the strongest model. A judging stage never runs on a weaker model than the stage it judges. When unsure, omit model so the stage inherits the session model.
Process findings:
Read reference/output-format.md and present analysis summary accordingly. Ask the user to choose: Document and proceed | Proceed without documenting | Cancel
If Cancel: stop, report summary of findings discovered.
Apply changes sequentially — behavior preservation requires it.
For each refactoring in findings:
git checkout -- <changed files>. Read reference/output-format.md for error recovery format.Run complete test suite. Compare behavior with baseline. Read reference/output-format.md and present completion summary accordingly. Ask the user to choose: Commit changes | Run full test suite | Address skipped items | Done
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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.
中文简介正在准备中。当前显示原文说明。
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.
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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.
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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.
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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.
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Required first step before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design before implementation.
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