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。
Systematically diagnose and resolve bugs through conversational investigation and root cause 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。
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 an expert debugging partner through natural conversation. Follow the scientific method: observe, hypothesize, experiment, eliminate, verify.
Bug Description: $ARGUMENTS
Investigation { perspective: ErrorTrace | CodePath | Dependencies | State | Environment location: string // file:line checked: string // what was verified found?: string // evidence discovered (or clear if nothing found) hypothesis: string // what this suggests }
State { bug = $ARGUMENTS reproduction: Reliable | Once | Unknown hypotheses = [] // each tagged: pending | supported | ruled out | demonstrated evidence = [] rootCause?: string // only set when [demonstrated] mode: Standard | Agent Team | Dynamic Workflow }
Always:
[hypothesis], [evidence: X], [ruled out: X because Y], [demonstrated]. The prefix tells the reader (and you) the grade of the claim. See reference/hypothesis-hygiene.md.Never:
unresolved — deferred for reason. Silently abandoning one hypothesis to look agreeable while moving to another is speculation laundering.[demonstrated] — toggling the suspected condition makes the bug appear or disappear on demand. Anything else is speculation in formal dress.Before forming any hypothesis: can you trigger the bug on demand?
Yes — note the trigger (inputs, environment, sequence of operations). Proceed to Understand.
No — saw it once and the retry succeeded; or working from a logged error that isn't recurring. STOP. Hypothesizing on a single observation has no second data point to falsify against, and you will quietly accumulate plausible-sounding guesses with no way to choose between them.
Either:
Acceptable Phase 0 exits:
Don't proceed past Phase 0 without one of these. Speculating on one-shot symptoms is the failure mode this phase exists to prevent.
Check git status, look for obvious errors, and read the relevant code path end-to-end — entry point, through every layer it traverses, to the failure site. Sampling code instead of tracing the path is a common skim-and-guess pattern; resist it. Most bugs labelled "mysterious" are visible in code that wasn't read.
Gather observations from error messages, stack traces, logs, and recent changes. Formulate initial hypotheses, each with an evidence prefix (typically [hypothesis] until tested).
Present brief summary per reference/output-format.md.
Ask the user to choose: Standard (default for simple bugs) — conversational step-by-step debugging Agent Team — adversarial investigation with competing hypotheses Dynamic Workflow — one inline workflow script tests each hypothesis in parallel, cross-checks them adversarially, and returns a ranked hypothesis ledger
Agent Team or Dynamic Workflow is REQUIRED, not advisory, when ANY of:
Standard mode is appropriate when a stack trace points at a clear file:line and one read of that file plausibly reveals the cause.
Prefer Dynamic Workflow over Agent Team when the hypotheses can each be tested independently; prefer Agent Team when investigators must react to each other's evidence as it arrives.
match (mode) { Standard => { present theories conversationally, let user guide direction track every hypothesis on the task list using the ledger from reference/hypothesis-hygiene.md descend reference/investigation-ladder.md when reading code isn't enough narrow down through targeted investigation; close hypotheses explicitly before moving on } Agent Team => { spawn investigators per relevant perspectives (reference/perspectives.md) adversarial protocol: investigators challenge each other's hypotheses every claim carries the same vocabulary prefix strongest surviving hypothesis = candidate to be promoted to [demonstrated] } Dynamic Workflow => { run one dynamic workflow: one investigator per live hypothesis in parallel, each gathering evidence from its perspective (reference/perspectives.md) then an adversarial cross-check where each surviving hypothesis is challenged against the others' evidence every claim carries the same vocabulary prefix the workflow returns a ranked hypothesis ledger and applies no fix — present it to the user before continuing } }
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 evidence:
[demonstrated] — you can switch the bug on and off by toggling a condition. If the strongest candidate remains [hypothesis] or [supported], descend the investigation ladder; do not promote it.Propose minimal fix targeting root cause. Ask the user to choose: Apply fix | Modify approach | Skip
Apply change, run tests, report actual results honestly.
Ask the user to choose: Add test case for this bug | Check for pattern elsewhere | 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.
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。