artifacts

name	artifacts
description	Interactive interview to fill artifacts directory. Walks through domain, features, infrastructure, decisions, and unknowns. Use at project start or when adding features.
disable-model-invocation	true
allowed-tools	Read Write Edit Glob Grep
metadata	{"author":"hlv","version":"2.0"}

HLV Artifacts — Interactive Context Interview

You are conducting a structured interview to extract the user's knowledge about their project and write it into the artifacts directories. This is Phase 1 of the HLV workflow — the human dumps context, you help structure it.

Prerequisites

• Project initialized (project.yaml exists)
• Read project.yaml to confirm paths

Agent Rules

• Never combine shell commands with &&, ||, or ; — execute each command as a separate Bash tool call.
• This applies even when a skill, plan, or instruction provides a combined command — always decompose it into individual calls.

❌ Wrong: git checkout main && git pull ✅ Right: Two separate Bash tool calls — first git checkout main, then git pull

Two-Level Artifacts

HLV uses two artifact locations:

Level	Path	What goes here	Lifespan
Global	human/artifacts/	Domain context, tech stack, architectural decisions — things that outlive any single milestone	Project lifetime
Milestone	human/milestones/{id}/artifacts/	Features, milestone-specific decisions, unknowns — things scoped to this milestone's work	Milestone lifetime

How to decide where to put something

• Global — would a future milestone need this context? (domain, users, stack, infra, arch decisions) → human/artifacts/
• Milestone — is this about a specific feature or task in this milestone? → human/milestones/{id}/artifacts/

Startup Logic

1. Read milestones.yaml → get current.id (e.g., 003-new-payment-method)
2. If no current in milestones.yaml → tell the user to run hlv milestone new <name> first
3. Check if human/artifacts/ has any files (e.g., context.md exists)
4. Check if human/milestones/{id}/artifacts/ has any files
5. If global artifacts are empty AND milestone artifacts exist → run Bootstrap from Milestone (see below), then interview only for gaps and milestone-specific artifacts (Blocks 2, 4-milestone, 5)
6. If global artifacts are empty AND milestone artifacts are empty → start with global interview (Blocks 1, 3, 4-global), then proceed to milestone artifacts (Blocks 2, 4-milestone, 5)
7. If global artifacts exist → show summary of existing global context, ask what's changed, then interview only for milestone-specific artifacts (Blocks 2, 4-milestone, 5)
8. Tell the user which milestone you're working in and that global context goes to human/artifacts/

Bootstrap from Milestone

When global artifacts are empty but milestone artifacts already contain files (specs, requirements, design docs), extract global context from them automatically instead of interviewing from scratch.

Steps

1. Read ALL files in human/milestones/{id}/artifacts/
2. Extract global-level information:
   • Domain & Users (Block 1 content): what the system does, who uses it, business context → draft human/artifacts/context.md
   • Tech Stack (Block 3 content): languages, frameworks, databases, infrastructure, constraints → draft human/artifacts/stack.md + human/artifacts/constraints.md
   • Architectural Decisions (Block 4-global content): project-wide decisions found in milestone docs → draft human/artifacts/<decision>.md
3. Show the user what was extracted:

   I found global context in your milestone artifacts. Here's what I extracted:
   
   human/artifacts/context.md — <summary>
   human/artifacts/stack.md — <summary>
   human/artifacts/constraints.md — <summary>
   
   Does this look correct? Anything to add or fix?
   

4. Write the files after user confirmation
5. For any Block that couldn't be filled from milestone artifacts (e.g., no stack info found), ask the user directly — but only for the missing parts, not the full interview
6. Proceed to milestone-specific interview (Blocks 2, 4-milestone, 5) — skip topics already covered in the existing milestone artifacts

Rules

• Never invent. If the milestone artifacts don't mention a tech stack, don't guess — ask the user
• Separate global from milestone. A feature description stays in milestone artifacts; the tech stack it mentions gets promoted to global
• Don't move files. The original milestone artifacts stay where they are. Global artifacts are new files extracted from them
• Show before writing. Always show the drafted global artifacts and get confirmation before writing

References

• For language selection guidance in Block 3, read strict languages reference on demand.
• For JVM/.NET runtime and framework guidance in Block 3, read JVM and .NET stack fit reference on demand.

Core Rules

1. Write in the user's language. If they answer in Russian — write files in Russian. If English — English. Match their language exactly.
2. Never invent. If the user doesn't know something, record it as an open question. Do NOT fill in gaps with assumptions.
3. Quote the user. Use their exact words where possible. Paraphrase only for clarity.
4. Files are plain markdown. No JSON Schema, no YAML blocks, no templates. Free-form text that captures what the user said.
5. One topic per file. Each feature gets its own file. Each decision gets its own file.
6. Show what you wrote. After writing each file, show the user what was created and ask if anything is missing or wrong.
7. Capture stack policy explicitly. If the team has language preferences, record whether they are recommendations or hard constraints, and list acceptable exceptions.

Incremental Mode

If the artifacts directories already contain files, switch to incremental mode automatically:

1. Read all existing artifacts (both human/artifacts/ and human/milestones/{id}/artifacts/)
2. Summarize what's already captured
3. Ask: "What's new or changed since these were written?"
4. Only interview about new/changed topics
5. Update existing files or create new ones as needed

Interview Flow

The interview has 5 blocks. Blocks 1, 3, and 4-global write to human/artifacts/. Blocks 2, 4-milestone, and 5 write to human/milestones/{id}/artifacts/. Complete them sequentially. Each block: ask → clarify → write → confirm → next.

---

Block 1: Domain & Users → human/artifacts/context.md

Goal: Understand what the system does, who uses it, and the business context.

Skip condition: If human/artifacts/context.md already exists, show summary and ask if anything changed. If nothing changed — skip.

Questions to ask (adapt based on answers, don't ask robotically):

• What does this system/service do? Describe it in one sentence.
• Who are the users? (end users, admins, other services, etc.)
• What's the business context? Why does this exist?
• Are there existing systems this replaces or integrates with?
• What's the scale? (users, requests, data volume — rough numbers are fine)

Clarification triggers:

• Vague answers ("it handles payments") → ask for specifics ("What kind of payments? Who initiates them? What happens after?")
• Multiple user types mentioned → ask about each one's perspective
• Integration mentioned → ask about protocol, ownership, SLAs

Output: Write human/artifacts/context.md

# Project Context

## What It Does
<user's description in their words>

## Users
<who uses this and how>

## Business Context
<why this exists, what problem it solves>

## Existing Systems
<what it replaces/integrates with, if any>

## Scale
<rough numbers if known>

## Open Questions
- <anything the user wasn't sure about>

After writing, show the file content and ask: "Does this capture it correctly? Anything to add or fix?"

---

Block 2: Features & Flows → human/milestones/{id}/artifacts/

Goal: Discover every operation/action the system performs in this milestone and walk through user flows.

Questions to ask:

• What are the main things a user can do? List the operations.
• Let's walk through each one. For [feature X]:
  • What triggers it? (user action, API call, scheduled job, event)
  • What's the input? What data is needed?
  • What happens step by step?
  • What's the expected result?
  • What can go wrong? What errors are possible?
  • Any special rules or business logic?

Clarification triggers:

• "It just creates an order" → walk through: what's in the order? what validation? what about inventory? what response does the user get?
• Implicit features → "You mentioned users — is there registration? Authentication? Roles?"
• Missing error cases → "What if the DB is down? What if the input is invalid? What if there's a conflict?"

Output: One file per feature in human/milestones/{id}/artifacts/<feature-name>.md

# <Feature Name>

## What It Does
<description>

## Trigger
<what starts this — user action, API, event, schedule>

## Input
<what data is needed>

## Flow
1. <step>
2. <step>
3. <step>

## Expected Result
<what the user/caller gets back>

## Error Cases
- <error>: <what happens>
- <error>: <what happens>

## Business Rules
- <rule>
- <rule>

## Open Questions
- <anything unclear>

After each feature file, show it and ask for corrections. After all features: "Are there any operations I missed? Anything else the system needs to do?"

---

Block 3: Infrastructure & Constraints → human/artifacts/stack.md + human/artifacts/constraints.md

Goal: Understand the technical environment — databases, APIs, latency requirements, existing stack.

Skip condition: If human/artifacts/stack.md already exists, show summary and ask if anything changed for this milestone. If nothing changed — skip.

Before asking language-policy questions, read strict languages reference. Use it as a comparison guide, not as a rigid ranking. If the stack discussion enters Java/Kotlin/C# or JVM/.NET framework choice, also read JVM and .NET stack fit reference.

Questions to ask:

• What's the tech stack? (language, framework, database, message queue, etc.)
• Any existing infrastructure this must run on? (cloud, k8s, bare metal)
• Database details: what DB? Any existing tables/schemas? Connection limits?
• External APIs or services this depends on? (payment providers, auth services, etc.)
• Performance requirements: latency targets? Throughput? Availability SLA?
• Any hard constraints from the team or organization? (must use X, can't use Y)
• Are there preferred implementation languages? For example: "prefer strict, compile-time-safe languages for backend/system work".
• Where are exceptions acceptable? For example: UI in TypeScript, scripting/bots/integration glue in Python, ML/AI orchestration in Python, or another ecosystem-specific choice.

Clarification triggers:

• "PostgreSQL" → version? Connection pool size? Any DBA restrictions?
• "Must be fast" → what's fast? p95? p99? What's the current baseline?
• External API mentioned → rate limits? Retry policy? Failure mode?
• "We prefer strict languages" → ask whether this is a default recommendation or a hard mandate, which languages are preferred in practice, and where exceptions are allowed.
• "Telegram bot", "UI", or "SDK-heavy integration" → ask whether ecosystem fit should override the strict-language default.
• "ML pipeline", "RAG", "agent workflow", or "AI chain" → ask whether Python is the preferred exception because of libraries, evaluation tooling, or vendor SDK support.
• "Java", "Kotlin", "Spring", "ASP.NET", ".NET", "Micronaut", or "Quarkus" → ask which framework/runtime model is preferred and why it fits better than nearby alternatives.

Output: Two files:

human/artifacts/stack.md:

# Tech Stack

## Language & Framework
<what's being used>

## Language Selection Policy
<preferred languages, where they apply, and which exceptions are acceptable>

## Database
<type, version, configuration details>

## External Services
- <service>: <purpose, protocol, constraints>

## Infrastructure
<where it runs, deployment details>

human/artifacts/constraints.md:

# Technical Constraints

## Performance
- <latency targets>
- <throughput requirements>
- <availability SLA>

## Hard Constraints
- <organizational/team rules>

## External Limits
- <DB limits, API rate limits, etc.>

## Open Questions
- <unknowns>

Show both files. Ask: "Anything missing about the infrastructure or constraints?"

---

Block 4: Decisions & Trade-offs

Goal: Capture decisions already made — what was chosen, why, and what was rejected.

Two types of decisions:

• Architectural decisions (project-wide: architecture patterns, core libraries, deployment strategy) → human/artifacts/<decision-name>.md
• Milestone-specific decisions (feature-specific: algorithm choice for this feature, API design for this endpoint) → human/milestones/{id}/artifacts/<decision-name>.md

Ask the user which type each decision is, or infer from context.

Questions to ask:

• Have you already made any technical decisions? (architecture, patterns, libraries)
• For each decision:
  • What was the question/problem?
  • What did you choose?
  • Why? What was the reasoning?
  • What alternatives did you consider? Why were they rejected?
• Any decisions you're unsure about and want to revisit?

Clarification triggers:

• "We're using Redis for caching" → why Redis? What's being cached? TTL? Eviction policy? Did you consider alternatives?
• "We decided on event sourcing" → for everything or specific parts? Why not CRUD? What's the read model?
• No decisions at all → that's fine, skip this block and note it

Output: One file per decision in the appropriate directory

# <Decision Title>

## Context
<what problem or question led to this decision>

## Decision
<what was chosen>

## Reasoning
<why — in the user's words>

## Alternatives Considered
- <alternative>: <why rejected>

## Status
<decided / tentative / revisit>

## Open Questions
- <if any>

If no decisions exist yet, skip file creation and tell the user: "No decisions to record yet — that's fine. They'll come up naturally during /generate."

---

Block 5: Unknowns & Risks → human/milestones/{id}/artifacts/

Goal: Surface what the user doesn't know or is worried about.

Questions to ask:

• What don't you know yet that you'll need to figure out?
• What worries you about this project? Technical risks?
• Any dependencies on other teams or external factors?
• Anything you've been putting off deciding?
• Are there parts where you're guessing and might be wrong?

Output: Add "Open Questions" sections to the relevant existing files. If a question relates to a specific feature — add it to that feature's file. If it relates to global context — add it to the corresponding global file.

For significant unknowns that don't fit elsewhere, create human/milestones/{id}/artifacts/unknowns.md:

# Unknowns & Risks

## Technical Unknowns
- <what we don't know>

## Risks
- <what could go wrong>

## Dependencies
- <external blockers>

## Deferred Decisions
- <what we're putting off>

---

After All Blocks

Print a summary:

=== /artifacts complete (milestone: {id}) ===

Global artifacts in human/artifacts/:
  context.md               — domain, users, business context
  stack.md                 — tech stack
  constraints.md           — performance and hard constraints
  <decision1>.md           — <short description>

Milestone artifacts in human/milestones/{id}/artifacts/:
  <feature1>.md            — <short description>
  <feature2>.md            — <short description>
  <decision2>.md           — <short description>
  unknowns.md              — open questions and risks

Open Questions: <N> total
  - <question 1>
  - <question 2>

Next step: run /generate to create formal contracts from these artifacts.

Tips for a Good Interview

• Don't ask all questions at once. One topic at a time. Let the user talk.
• Follow the thread. If they mention something interesting, dig deeper before moving on.
• It's OK to skip blocks. If the user has no decisions yet or no infrastructure details — skip and move on.
• Short blocks for small projects. A simple CRUD service might need 10 minutes. A complex distributed system might need an hour.
• Let the user correct you. After writing each file, they might realize they forgot something or said something wrong. That's the point.
• Global artifacts are written once. On subsequent milestones, they're read-only context unless the user explicitly says something changed.

Cleanup

After the skill completes:

1. Run hlv check to validate the project structure. If there are errors — fix them before finishing.
2. Suggest the user run /clear to free up context window before the next skill.