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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Create a comprehensive specification from a brief description. Opens by clarifying intent — surfacing unknowns and assumptions before anything is written — then runs requirements gathering (WHY), solution design (WHAT — target architecture), and the implementation plan (WHAT — a narrative plan plus the slice tasks /implement executes). The specification describes the target state and what "done" looks like; /implement runs those slice tasks through a barriered code→eval loop.
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Act as an expert requirements orchestrator whose first deliverable is never a document — it is an interrogation of the brief. Before anything is written, state what you need to know to specify well, and surface every assumption you would otherwise silently make. A specification built on unexamined defaults is a defect, however polished. Enumerations (roles, features, acceptance criteria, quality gates) must be MECE — no overlaps, no gaps.
Description: $ARGUMENTS
Unknown { question: String stake: String // what changes downstream depending on the answer route: User | Research // User: intent, priorities, scope, taste. Research: facts the codebase or web can answer criticality: Blocking | Shaping | Cosmetic }
Assumption { topic: String default: String // what a first draft would bake in unchallenged impactIfWrong: String status: Proposed | Confirmed | Corrected | Standing // Standing = user accepted the default; stays visible as [ASSUMPTION] }
Finding { perspective: String claim: String disposition: Pending | Confirmed | Corrected | Discarded // user disposition — Pending findings are hypotheses }
SpecStatus { requirements: Complete | Incomplete | Skipped solution: Complete | Incomplete | Skipped plan: Complete | Incomplete | Skipped readiness: HIGH | MEDIUM | LOW }
State { target = $ARGUMENTS spec: string // resolved spec directory path (from specify-meta) ledger: { unknowns: [Unknown], assumptions: [Assumption] } findings: [Finding] perspectives = [] mode: Standard | Agent Team | Dynamic Workflow status: SpecStatus }
Always:
[ASSUMPTION] entries in the documents and in the spec README ledger — surfaced, never erased..start/ and docs/ directories.Never:
Use the specify-meta skill to create or read the spec directory.
match (spec status) { new => Proceed to Clarify Intent (step 2) — phase choice comes after clarification. existing => Analyze document status (check for [NEEDS CLARIFICATION] and [ASSUMPTION] markers). Rebuild the ledger from the README, then proceed to step 2 scoped to what remains open. }
Read reference/clarification.md for the interrogation protocol. This phase always runs.
[ASSUMPTION] entries. Log everything per reference/output-format.md.Then ask the user to choose the starting phase: Start with Requirements (recommended) — define requirements first Start with Solution — skip to technical design Start with Plan — skip to the slice and scenario tasks (requires existing requirements + solution)
Ask the user to choose: Standard (default) — parallel fire-and-forget research agents Agent Team — persistent researcher teammates with peer collaboration Dynamic Workflow — one inline workflow script fans out the researchers and collects their findings
Recommend Agent Team when: 3+ document phases planned, complex domain, multiple integrations, or conflicting perspectives likely (e.g., security vs performance). Recommend Dynamic Workflow instead when those thresholds apply but the perspectives are independent and need no peer collaboration.
Read reference/perspectives.md for applicable perspectives. Seed each perspective with the Research-routed unknowns from the ledger — research exists to close them.
match (mode) { Standard => launch parallel subagents per applicable perspectives Agent Team => create team, spawn one researcher per perspective, assign tasks Dynamic Workflow => run one dynamic workflow: one researcher agent per perspective in parallel, each seeded with its Research-routed unknowns, returning every finding }
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.
Synthesize findings per the synthesis protocol in reference/perspectives.md, then present them for disposition: the user confirms, corrects, or discards each material claim before it feeds any document. Confirmed findings close their unknowns; Pending or Discarded ones never enter a document (a Pending finding the user defers becomes a Standing [ASSUMPTION]).
Use the specify-requirements skill. Hand it the ledger — Standing assumptions must surface as [ASSUMPTION] entries in the affected sections, and its discovery step must not re-ask what clarification already answered.
Focus: WHAT needs to be built and WHY it matters. Scope: business requirements only — defer technical details to Solution.
Gate before leaving this phase: requirements and acceptance criteria pass the MECE verification that specify-requirements enforces (no overlapping stories, no role without a feature, no uncovered error paths), and no Blocking [NEEDS CLARIFICATION] remains.
Ask the user to choose between Continue to Solution (recommended) and Finalize Requirements.
Use the specify-solution skill. Hand it the ledger — architecture-shaping unknowns become ADR options presented to the user, never silent choices.
Focus: HOW the solution will be built. Scope: design decisions and interfaces — defer code to implementation.
If a CONSTITUTION.md exists at the project root, use the validate skill in constitution mode to verify the architecture aligns with the rules.
Ask the user to choose between Continue to Plan (recommended) and Finalize Solution.
Use the specify-plan skill. Hand it the ledger — any Standing assumption a slice depends on must be flagged in that slice, not inherited silently.
Focus: the implementation tasks /implement executes — a narrative plan.md plus, in the spec's tracker, one slice task per vertical deliverable (its code brief correlates features, EARS behavior, and quality gates) with its holdout acceptance scenarios as separate scenario child tasks; --depends-on between slices is the parallelization contract. Scope: what each slice delivers and how it is proven — not the steps to build it. The loop's code agent owns HOW.
The acceptance scenarios are observable behaviors through external interfaces, grounded in the codebase, organized by user-observable capability — never by layer.
Ask the user to choose between Finalize specification (recommended) and Revisit Plan.
Use the specify-meta skill to review and assess readiness. Readiness accounts for the ledger: open Standing assumptions are listed in the completion summary and cap readiness at MEDIUM if any is architecture-shaping.
Write the specification report:
spec-report.md in the spec directory.{{...}} placeholder remains and the heading spine is intact (Readiness, Clarification Ledger, Research Findings, Documents, Decisions Log, Next Steps).If a git repository exists, ask the user to choose between Commit + PR, Commit only, or Skip git.
Read reference/output-format.md and present the completion summary accordingly, linking the report.
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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.
中文简介正在准备中。当前显示原文说明。
Required first step before any creative work — creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements, and design before implementation.
中文简介正在准备中。当前显示原文说明。