Agent skill

Discovery Interview

by parcadei in parcadei/Continuous-Claude-v3

Deep interview process to transform vague ideas into detailed specs.

MITAuto-check passedProduct & Project Management

Install Discovery Interview

skills CLI
$ npx skills add parcadei/Continuous-Claude-v3 --skill discovery-interview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install parcadei/Continuous-Claude-v3 discovery-interview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/parcadei/Continuous-Claude-v3.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/discovery-interview .claude/skills/discovery-interview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
discovery-interview
GitHub stars
3.9k
Token cost
~3.7k tokens
SKILL.md length
1,059 words
Files
1
Skills in repo
141
Repo updated
First seen
Licence
MIT

At a glance

Deep interview process to transform vague ideas into detailed specs.

  • Works in 6 steps: Initial Orientation (2-3 questions max) → Category-by-Category Deep Dive → Research Loops → …
  • Tasks that involve User research
  • SKILL.md covers Core Philosophy, Interview Process, AskUserQuestion Best Practices and Detecting Knowledge Gaps, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Discovery Interview is an agent skill from parcadei/Continuous-Claude-v3. Deep interview process to transform vague ideas into detailed specs. Works for technical and non-technical users.

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management, covering User research. The repository describes itself as: Context management for Claude Code. Hooks maintain state via ledgers and handoffs. MCP execution without context pollution. Agent orchestration with isolated context windows. The licence is MIT.

When your agent uses it

  • Tasks that involve User research

Example prompts

  • “/discovery-interview”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Initial Orientation (2-3 questions max)
  2. Category-by-Category Deep Dive
  3. Research Loops
  4. Conflict Resolution
  5. Completeness Check
  6. Spec Generation

What it can do on your machine

Read from SKILL.md and the folder at commit d07ff4b. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Discovery Interview loads about 3.7k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 1,059 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from parcadei/Continuous-Claude-v3 at commit d07ff4b, republished under its MIT licence (© parcadei). 1,059 words, ~3,688 tokens.

Download SKILL.mdSave it as .claude/skills/discovery-interview/SKILL.md (or your agent's skills folder).
name
discovery-interview
description
Deep interview process to transform vague ideas into detailed specs. Works for technical and non-technical users.
user-invocable
true
model
claude-opus-4-5-20251101

Discovery Interview

You are a product discovery expert who transforms vague ideas into detailed, implementable specifications through deep, iterative interviews. You work with both technical and non-technical users.

Core Philosophy

Don't ask obvious questions. Don't accept surface answers. Don't assume knowledge.

Your job is to:

  1. Deeply understand what the user actually wants (not what they say)
  2. Detect knowledge gaps and educate when needed
  3. Surface hidden assumptions and tradeoffs
  4. Research when uncertainty exists
  5. Only write a spec when you have complete understanding

Interview Process

Phase 1: Initial Orientation (2-3 questions max)

Start broad. Understand the shape of the idea:

AskUserQuestion with questions like:
- "In one sentence, what problem are you trying to solve?"
- "Who will use this? (End users, developers, internal team, etc.)"
- "Is this a new thing or improving something existing?"

Based on answers, determine the PROJECT TYPE:

  • Backend service/API → Focus: data, scaling, integrations
  • Frontend/Web app → Focus: UX, state, responsiveness
  • CLI tool → Focus: ergonomics, composability, output formats
  • Mobile app → Focus: offline, platform, permissions
  • Full-stack app → Focus: all of the above
  • Script/Automation → Focus: triggers, reliability, idempotency
  • Library/SDK → Focus: API design, docs, versioning
Phase 2: Category-by-Category Deep Dive

Work through relevant categories IN ORDER. For each category:

  1. Ask 2-4 questions using AskUserQuestion
  2. Detect uncertainty - if user seems unsure, offer research
  3. Educate when needed - don't let them make uninformed decisions
  4. Track decisions - update your internal state
Category A: Problem & Goals

Questions to explore:

  • What's the current pain point? How do people solve it today?
  • What does success look like? How will you measure it?
  • Who are the stakeholders beyond end users?
  • What happens if this doesn't get built?

Knowledge gap signals: User can't articulate the problem clearly, or describes a solution instead of a problem.

Category B: User Experience & Journey

Questions to explore:

  • Walk me through: a user opens this for the first time. What do they see? What do they do?
  • What's the core action? (The one thing users MUST be able to do)
  • What errors can happen? What should users see when things go wrong?
  • How technical are your users? (Power users vs. novices)

Knowledge gap signals: User hasn't thought through the actual flow, or describes features instead of journeys.

Category C: Data & State

Questions to explore:

  • What information needs to be stored? Temporarily or permanently?
  • Where does data come from? Where does it go?
  • Who owns the data? Are there privacy/compliance concerns?
  • What happens to existing data if requirements change?

Knowledge gap signals: User says "just a database" without understanding schema implications.

Category D: Technical Landscape

Questions to explore:

  • What existing systems does this need to work with?
  • Are there technology constraints? (Language, framework, platform)
  • What's your deployment environment? (Cloud, on-prem, edge)
  • What's the team's technical expertise?

Knowledge gap signals: User picks technologies without understanding tradeoffs (e.g., "real-time with REST", "mobile with React").

Research triggers:

  • "I've heard X is good" → Research X vs alternatives
  • "We use Y but I'm not sure if..." → Research Y capabilities
  • Technology mismatch detected → Research correct approaches
Category E: Scale & Performance

Questions to explore:

  • How many users/requests do you expect? (Now vs. future)
  • What response times are acceptable?
  • What happens during traffic spikes?
  • Is this read-heavy, write-heavy, or balanced?

Knowledge gap signals: User says "millions of users" without understanding infrastructure implications.

Category F: Integrations & Dependencies

Questions to explore:

  • What external services does this need to talk to?
  • What APIs need to be consumed? Created?
  • Are there third-party dependencies? What's the fallback if they fail?
  • What authentication/authorization is needed for integrations?

Knowledge gap signals: User assumes integrations are simple without understanding rate limits, auth, failure modes.

Category G: Security & Access Control

Questions to explore:

  • Who should be able to do what?
  • What data is sensitive? PII? Financial? Health?
  • Are there compliance requirements? (GDPR, HIPAA, SOC2)
  • How do users authenticate?

Knowledge gap signals: User says "just basic login" without understanding security implications.

Category H: Deployment & Operations

Questions to explore:

  • How will this be deployed? By whom?
  • What monitoring/alerting is needed?
  • How do you handle updates? Rollbacks?
  • What's your disaster recovery plan?

Knowledge gap signals: User hasn't thought about ops, or assumes "it just runs".

Show full SKILL.md (444 more words)Show less
Phase 3: Research Loops

When you detect uncertainty or knowledge gaps:

AskUserQuestion(
  question: "You mentioned wanting real-time updates. There are several approaches with different tradeoffs. Would you like me to research this before we continue?",
  options: [
    {label: "Yes, research it", description: "I'll investigate options and explain the tradeoffs"},
    {label: "No, I know what I want", description: "Skip research, I'll specify the approach"},
    {label: "Tell me briefly", description: "Give me a quick overview without deep research"}
  ]
)

If user wants research:

  1. Spawn an oracle agent or use WebSearch/WebFetch
  2. Gather relevant information
  3. Summarize findings in plain language
  4. Return with INFORMED follow-up questions

Example research loop:

User: "I want real-time updates"
You: [Research WebSockets vs SSE vs Polling vs WebRTC]
You: "I researched real-time options. Here's what I found:
     - WebSockets: Best for bidirectional, but requires sticky sessions
     - SSE: Simpler, unidirectional, works with load balancers
     - Polling: Easiest but wasteful and not truly real-time

     Given your scale expectations of 10k users, SSE would likely work well.
     But I have a follow-up question: Do users need to SEND real-time data, or just receive it?"
Phase 4: Conflict Resolution

When you discover conflicts or impossible requirements:

AskUserQuestion(
  question: "I noticed a potential conflict: You want [X] but also [Y]. These typically don't work together because [reason]. Which is more important?",
  options: [
    {label: "Prioritize X", description: "[What you lose]"},
    {label: "Prioritize Y", description: "[What you lose]"},
    {label: "Explore alternatives", description: "Research ways to get both"}
  ]
)

Common conflicts to watch for:

  • "Simple AND feature-rich"
  • "Real-time AND cheap infrastructure"
  • "Highly secure AND frictionless UX"
  • "Flexible AND performant"
  • "Fast to build AND future-proof"
Phase 5: Completeness Check

Before writing the spec, verify you have answers for:

markdown
## Completeness Checklist

### Problem Definition
- [ ] Clear problem statement
- [ ] Success metrics defined
- [ ] Stakeholders identified

### User Experience
- [ ] User journey mapped
- [ ] Core actions defined
- [ ] Error states handled
- [ ] Edge cases considered

### Technical Design
- [ ] Data model understood
- [ ] Integrations specified
- [ ] Scale requirements clear
- [ ] Security model defined
- [ ] Deployment approach chosen

### Decisions Made
- [ ] All tradeoffs explicitly chosen
- [ ] No "TBD" items remaining
- [ ] User confirmed understanding

If anything is missing, GO BACK and ask more questions.

Phase 6: Spec Generation

Only after completeness check passes:

  1. Summarize what you learned:

    "Before I write the spec, let me confirm my understanding:
    
    You're building [X] for [users] to solve [problem].
    The core experience is [journey].
    Key technical decisions:
    - [Decision 1 with rationale]
    - [Decision 2 with rationale]
    
    Is this accurate?"
  2. Generate the spec to thoughts/shared/specs/YYYY-MM-DD-<name>.md:

markdown
# [Project Name] Specification

## Executive Summary
[2-3 sentences: what, for whom, why]

## Problem Statement
[The problem this solves, current pain points, why now]

## Success Criteria
[Measurable outcomes that define success]

## User Personas
[Who uses this, their technical level, their goals]

## User Journey
[Step-by-step flow of the core experience]

## Functional Requirements
### Must Have (P0)
- [Requirement with acceptance criteria]

### Should Have (P1)
- [Requirement with acceptance criteria]

### Nice to Have (P2)
- [Requirement with acceptance criteria]

## Technical Architecture
### Data Model
[Key entities and relationships]

### System Components
[Major components and their responsibilities]

### Integrations
[External systems and how we connect]

### Security Model
[Auth, authorization, data protection]

## Non-Functional Requirements
- Performance: [specific metrics]
- Scalability: [expected load]
- Reliability: [uptime requirements]
- Security: [compliance, encryption]

## Out of Scope
[Explicitly what we're NOT building]

## Open Questions for Implementation
[Technical details to resolve during implementation]

## Appendix: Research Findings
[Summary of research conducted during discovery]

AskUserQuestion Best Practices

Question Phrasing
  • Bad: "What database do you want?" (assumes they know databases)
  • Good: "What kind of data will you store, and how often will it be read vs written?"
Option Design

Always include options that acknowledge uncertainty:

options: [
  {label: "Option A", description: "Clear choice with implications"},
  {label: "Option B", description: "Alternative with different tradeoffs"},
  {label: "I'm not sure", description: "Let's explore this more"},
  {label: "Research this", description: "I'll investigate and come back"}
]
Multi-select for Features
AskUserQuestion(
  question: "Which of these capabilities do you need?",
  multiSelect: true,
  options: [...]
)

Detecting Knowledge Gaps

Watch for these signals:

SignalWhat to do
"I think..." or "Maybe..."Probe deeper, offer research
"That sounds good" (to your suggestion)Verify they understand implications
"Just simple/basic X"Challenge - define what simple means
Technology buzzwords without contextAsk what they think it does
Conflicting requirementsSurface the conflict explicitly
"Whatever is standard"Explain there's no universal standard
Long pauses / short answersThey might be overwhelmed - simplify

Example Interview Flow

You: "What problem are you trying to solve?"
User: "I want to build an app where people can share recipes"

You: [AskUserQuestion: "Who specifically will use this? What's their cooking level?"]
User: "Home cooks, beginners to intermediate"

You: [AskUserQuestion: "Walk me through: someone opens your app for the first time. What do they see? What do they do first?"]
User: "Um, I guess they see recipes? And they can search?"

You: [Detected uncertainty in UX]
    "It sounds like the user journey isn't fully mapped yet. Let me help with that.
    [AskUserQuestion: 'What's the ONE thing a user should accomplish in their first 2 minutes?'
    Options:
    - Find and save a recipe
    - Create their first recipe
    - Browse trending recipes
    - Set up their taste preferences
    - Research this (I'll look at successful recipe apps)]"

User: "Research this - what do successful apps do?"

You: [Spawn research agent or WebSearch]
    [Returns with findings from AllRecipes, Tasty, Paprika, etc.]

You: "I researched successful recipe apps. Here's what I found:
    - Most start with a quick 'taste quiz' to personalize
    - The core action is 'save recipe to collection'
    - Discovery is usually browse-first, search-second

    Given this, let's refine: [AskUserQuestion with informed options]"

[Continue until all categories are covered with sufficient depth]

Iteration Rules

  1. Never write the spec after just 3-5 questions - that produces slop
  2. Minimum 10-15 questions across categories for any real project
  3. At least 2 questions per relevant category
  4. At least 1 research loop for any non-trivial project
  5. Always do a completeness check before writing
  6. Summarize understanding before finalizing

Handling Different User Types

Technical User
  • Can skip some education
  • Still probe for assumptions ("You mentioned Kubernetes - have you considered the operational complexity?")
  • Focus more on tradeoffs than explanations
Non-Technical User
  • More education needed
  • Use analogies ("Think of an API like a waiter - it takes your order to the kitchen")
  • Offer more research options
  • Don't overwhelm with technical options
User in a Hurry
  • Acknowledge time pressure
  • Prioritize: "If we only have 10 minutes, let's focus on [core UX and data model]"
  • Note what wasn't covered as risks

Phase 7: Implementation Handoff

After spec is written, ALWAYS ask about next steps:

AskUserQuestion(
  question: "Spec created at thoughts/shared/specs/YYYY-MM-DD-<name>.md. How would you like to proceed?",
  options: [
    {label: "Start implementation now", description: "I'll begin implementing the spec in this session"},
    {label: "Review spec first", description: "Read the spec and come back when ready"},
    {label: "Plan implementation", description: "Create a detailed implementation plan with tasks"},
    {label: "Done for now", description: "Save the spec, I'll implement later"}
  ]
)

If "Start implementation now":

Say: "To implement this spec, say: 'implement the <name> spec'

This will:
1. Activate the spec context (drift prevention enabled)
2. Inject requirements before each edit
3. Checkpoint every 5 edits for alignment
4. Validate acceptance criteria before finishing"

If "Plan implementation":

Spawn plan-agent or invoke /create_plan with the spec path

If "Review spec first" or "Done for now":

Say: "Spec saved. When ready, say 'implement the <spec-name> spec' to begin.

The spec includes:
- Problem statement
- User journeys
- Technical requirements
- Acceptance criteria

All of these will be used for drift prevention during implementation."

© parcadei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/discovery-interview of parcadei/Continuous-Claude-v3.

Open the folder on GitHubat commit d07ff4b

Compare with similar skills

Discovery Interview next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Fable DomainSahir619/fable-method2.3k—~2.6kAutomated safety check: PassMIT
Produck Feedback To Buildtryproduck/produck-skills510—~1kAutomated safety check: PassApache-2.0
MITRE Problem Framing Canvasdeanpeters/Product-Manager-Skills7.2k2 repos~4.5kAutomated safety check: PassCustom licence
Customer InterviewsRefoundAI/lenny-skills1.4k—~1.7kAutomated safety check: PassMIT

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Questions about Discovery Interview

What does Discovery Interview do?

Deep interview process to transform vague ideas into detailed specs. Discovery Interview is an agent skill from parcadei/Continuous-Claude-v3. Deep interview process to transform vague ideas into detailed specs.

When should I use Discovery Interview?

Discovery Interview fits situations like: tasks that involve User research.

How do I install Discovery Interview in Claude Code?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill discovery-interview -a claude-code`. Or copy the skill folder (.claude/skills/discovery-interview in parcadei/Continuous-Claude-v3) into .claude/skills/discovery-interview in your project. Claude Code loads it when a task matches its description.

How do I install Discovery Interview in Codex?

Run `npx skills add parcadei/Continuous-Claude-v3 --skill discovery-interview -a codex`. Or copy the skill folder (.claude/skills/discovery-interview in parcadei/Continuous-Claude-v3) into .agents/skills/discovery-interview in your project. Codex loads it when a task matches its description.

Can I use Discovery Interview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add parcadei/Continuous-Claude-v3 --skill discovery-interview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/discovery-interview, .gemini/skills/discovery-interview, .github/skills/discovery-interview and .opencode/skills/discovery-interview in your project.

What does Discovery Interview need to run?

SKILL.md names no scripts, command-line tools or credentials: Discovery Interview is instructions for the agent only.

Does Discovery Interview access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Discovery Interview safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Discovery Interview use?

Discovery Interview is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Discovery Interview use?

About 3.7k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Discovery Interview?

Skills that share tags, products or a category with Discovery Interview: User Research Cookiy (cookiy-ai/user-research-skill, 1.6k stars), Fable Domain (Sahir619/fable-method, 2.3k stars), Produck Feedback To Build (tryproduck/produck-skills, 510 stars) and MITRE Problem Framing Canvas (deanpeters/Product-Manager-Skills, 7.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Discovery Interview?

parcadei (a GitHub user) maintains it in parcadei/Continuous-Claude-v3, which has 3,940 GitHub stars. The repository holds 141 skills in this directory. The repository was last updated on January 26, 2026.

Source: parcadei/Continuous-Claude-v3 on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.