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。
Author the implementation plan — a narrative plan.md (phases + outcomes) plus the implementation TASKS in the spec's tracker that /implement runs. Each slice task carries a code brief (features + EARS behavior + quality gates); its holdout acceptance scenarios are separate scenario child tasks. Use after requirements and solution.
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。
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Act as an implementation-plan specialist. You translate requirements and solution design into two correlated artifacts:
plan.md — the high-level approach: phases and the achievable outcomes, plus a
pointer to the tracker. It is read for orientation, never parsed for work.task.js) — the operational unit /implement
runs. Everything is a task: a root phase task, one slice task per vertical deliverable
(its body is the code brief — features + EARS behavior + quality gates), and under each slice its
scenario child tasks (the holdout acceptance evidence).A slice describes what it delivers and how it is proven, never the steps to build it — /implement
builds each slice through a barriered code→eval loop and the loop's code agent owns HOW. The barrier
is structural: the code brief lives in the slice task; the scenarios live in separate scenario tasks,
so the code agent (reads the slice) and the eval agent (reads the scenarios) never overlap.
Spec Target: $ARGUMENTS
Always:
task.js --depends-on (tracker ids) — /implement topo-sorts by them and runs independent slices concurrently. It is the whole parallelization contract.AC-N.M), Behavior (EARS statements drawn from the solution design — see reference/ears.md), Quality Gates (checkable conditions). The fourth, Acceptance Scenarios, become separate scenario child tasks, never part of the slice body./implement is held to.[ASSUMPTION] entries from requirements.md/solution.md; any slice depending on a Standing assumption must flag it in its brief — the plan inherits the ledger visibly.threshold (default 0.90) and max_iterations (default 3) in plan.md frontmatter — /implement reads them.Never:
/implement owns HOW.Templates:
Note: run the tracker CLI as
node ${CLAUDE_PLUGIN_ROOT}/scripts/task.js. Slice bodies and scenario bodies are substantial markdown — write each to a temp file and pass it with--body-file.
Read requirements.md and solution.md from specDirectory. Read the project instructions file and explore the codebase for existing patterns, test structure, endpoints, and data shapes.
Identify the slices: group the requirements' acceptance criteria into vertical-slice deliverables, ordered by dependency. Each slice is one coherent thing a user can do end-to-end, atomic enough for a single code agent. This slicing is the plan's backbone.
Read template.md and write plan.md: frontmatter (threshold, max_iterations), a Target State
section (prose: what the system does when every slice is delivered, grounded in real components — no step
list), a Phases & Outcomes section (each phase/slice as a one-line achievable outcome + its
dependencies), and a Tracking section linking .tracker/ and the task.js graph --spec {specId}
command. Present the Target State and slice breakdown; ask the user to Approve slicing, Adjust, or
Re-slice.
node ${CLAUDE_PLUGIN_ROOT}/scripts/task.js create --spec {specId} --type phase --name "Implement {specName}" → record rootId.
For each slice, in dependency order (read reference/ears.md and reference/scenario-guide.md first):
AC-N.M this slice delivers,
traced to requirements); Behavior — EARS (EARS statements derived from solution.md; every EARS
criterion lands in exactly one slice); Quality Gates (baseline gates + what the solution implies;
carry NFR-N with their ids). Flag any Standing assumption it depends on.node ${CLAUDE_PLUGIN_ROOT}/scripts/task.js create --spec {specId} --parent {rootId} --type slice --name "{title}" --depends-on {slice task ids it extends} --body-file {brief.md} → record its id.business scenario and every EARS behavior (incl. edge/error states)
needs ≥1 technical scenario. Write each body from templates/scenario.md and tag its Surface
(business | technical) — the eval agent scores the two surfaces separately. Assign P0/P1/P2 (every slice needs ≥1 P0), then:
node ${CLAUDE_PLUGIN_ROOT}/scripts/task.js create --spec {specId} --parent {sliceId} --type scenario --name "{scenario title}" --body-file {scenario.md}.Present each slice for review in the reference/output-format.md slice shape, with a coverage map — each Feature AC and each EARS behavior mapped to the scenario(s) that exercise it, and any surface with no scenario flagged as a gap. Ask the user to Approve slice, Edit brief, Edit scenarios, or Add missing scenarios. Create the tasks only after approval.
Read reference/validation.md and run checks against the created tasks (task.js graph --spec {specId}):
[ASSUMPTION] is resolved or flagged on the slices that depend on it.business scenario and every EARS behavior maps to ≥1 technical
scenario — no acceptance surface is left without a scenario; every slice has ≥1 P0. Each scenario
carries a Surface tag and tests observable behavior only.--depends-on id refers to an existing slice; the dependency graph is acyclic (task.js validates dep ids and rejects parent cycles at create time).plan.md frontmatter has threshold and max_iterations; the tracker has one phase root, its slice children, and each slice's scenario children.Read reference/output-format.md and present the status report: the narrative plan, the slice/scenario task tree (task.js list --spec {specId} --depth 1),
and the coverage map. Ask the user to Finalize, Revisit a slice, Re-slice, or Adjust threshold.
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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.
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。
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。