Agent skill

Spec Interview

by jellydn in jellydn/my-ai-tools

Clarify requirements through targeted questions — uncovers unknown unknowns in specs

MITAuto-check passedAgent Workflows

Install Spec Interview

skills CLI
$ npx skills add jellydn/my-ai-tools --skill spec-interview -a claude-code

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

GitHub CLI
$ gh skill install jellydn/my-ai-tools spec-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/jellydn/my-ai-tools.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/spec-interview .claude/skills/spec-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
spec-interview
GitHub stars
123
Token cost
~2.1k tokens
SKILL.md length
736 words
Files
1
Skills in repo
33
Repo updated
First seen
Licence
MIT

At a glance

Clarify requirements through targeted questions — uncovers unknown unknowns in specs

  • Works in 6 steps: Review Initial Spec → Categorize Gaps → Prioritize Questions → …
  • Tasks that involve Requirements gathering
  • SKILL.md covers When to Use, What It Does, How to Execute and Question Patterns, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Spec Interview is an agent skill from jellydn/my-ai-tools. Clarify requirements through targeted questions — uncovers unknown unknowns in specs

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: cline, claude, opencode, amp, codex, gemini, cursor, pi

It sits in Agent Workflows, covering Requirements gathering. The repository describes itself as: Comprehensive configuration management for AI coding tools - Replicate my complete setup for Claude Code, OpenCode, Amp, Li, Codex and Claude Code Switch with custom… The licence is MIT.

When your agent uses it

  • Tasks that involve Requirements gathering

Example prompts

  • “/spec-interview”

Requirements

  • Compatibility (from SKILL.md): cline, claude, opencode, amp, codex, gemini, cursor, pi

Workflow steps

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

  1. Review Initial Spec
  2. Categorize Gaps
  3. Prioritize Questions
  4. Conduct Interview (One Question at a Time)
  5. Process Answers
  6. Summarize

What it can do on your machine

Read from SKILL.md and the folder at commit 7a06584. 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.

    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.

  • Compatibility

    cline, claude, opencode, amp, codex, gemini, cursor, pi

    From compatibility in the SKILL.md frontmatter.

Context cost

Spec Interview loads about 2.1k tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 736 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~25
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

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 jellydn/my-ai-tools at commit 7a06584, republished under its MIT licence (© jellydn). 736 words, ~2,100 tokens.

Download SKILL.mdSave it as .claude/skills/spec-interview/SKILL.md (or your agent's skills folder).
name
spec-interview
description
Clarify requirements through targeted questions — uncovers unknown unknowns in specs
compatibility
cline, claude, opencode, amp, codex, gemini, cursor, pi
license
MIT
hint
Use when feature requirements are vague or incomplete
user-invocable
true

Spec Interview

When to Use

Use this skill when:

  • Feature requirements are vague or incomplete
  • You have a general idea but lack specifics
  • Stakeholders said "you know what I mean"
  • The spec has obvious gaps
  • Making assumptions that could be wrong

What It Does

The agent interviews you to uncover unknown unknowns in your feature specification, focusing on questions that would change architectural decisions.

How to Execute

Step 1: Review Initial Spec

Analyze what the user provided:

  • Explicit requirements (what they said)
  • Implicit requirements (what they assumed)
  • Missing details
  • Ambiguous areas
Step 2: Categorize Gaps

Identify question categories:

Scope & Boundaries:

  • What's in scope vs out of scope?
  • Edge cases to handle?
  • MVP vs future iterations?

User Experience:

  • What happens when...?
  • Error states and recovery?
  • Loading and async states?

Technical Decisions:

  • Performance requirements?
  • Data consistency needs?
  • Integration points?
  • Security considerations?

Architecture Impact:

  • Does this change existing patterns?
  • New abstractions needed?
  • Migration strategy for existing data?
Step 3: Prioritize Questions

Sort by impact on implementation:

  1. Architecture-changing: Would change core approach
  2. High-impact: Significant implementation difference
  3. Medium-impact: Affects specific modules
  4. Low-impact: Nice to clarify but not blocking
Step 4: Conduct Interview (One Question at a Time)

Use the ask_user_question tool for each question. Ask one question at a time — present it, wait for the answer, then proceed to the next. This keeps the interview focused and lets the user's answer to one question influence follow-ups.

Guidelines for using ask_user_question:

  • Set header to a short category label (max 16 chars), e.g. "Architecture", "Scope", "UX", "Edge Cases"
  • Write a clear question string with context about why you're asking
  • Provide 2-4 concrete options with concise label (1-5 words) and descriptive description explaining trade-offs
  • After the user answers, acknowledge the choice and explain how it impacts the implementation before asking the next question
  • Architecture-changing questions first, then high-impact, then medium-impact

Example — single question call:

ask_user_question(questions: [{
  header: "Architecture",
  question: "Where should the export process run? Large exports could time out or block the web process.",
  options: [
    {
      label: "Synchronous HTTP",
      description: "Simple, returns CSV directly in response — but risky for large datasets that could timeout"
    },
    {
      label: "Background job + email",
      description: "More robust: process asynchronously, email link when done — requires job queue and storage"
    },
    {
      label: "Streaming download",
      description: "Immediate start, handles large data, no queuing needed — but more complex to implement"
    }
  ]
}])

When to use open-ended instead of multiple choice:

  • Exploring unknown design space where you can't enumerate options
  • After multiple-choice answers that surface unexpected direction
  • For rank/priority questions ("Which features are most important?")
  • Set options with 2-4 broader paths, or use ask_user_question's single-select custom answer ("Type something") for truly open exploration
Step 5: Process Answers

After each answer:

  • Acknowledge the choice and its implications
  • Update your mental model
  • Decide if follow-up questions are needed (you can dig deeper before moving on)
  • Identify decisions that need documentation
Step 6: Summarize

After all questions are answered:

  • Summarize the key architectural decisions made
  • Flag decisions the user deferred ("decide for me" or "skip")
  • Outline next steps with confidence level

Question Patterns

These show how to translate each pattern into an ask_user_question call.

Architecture-Changing
// One question at a time
ask_user_question(questions: [{
  header: "Architecture",
  question: "How should we handle authentication for this feature? This determines whether we modify the current auth flow or build a new one.",
  options: [
    {
      label: "Extend existing auth",
      description: "Adds to current flow — simpler but may create coupling"
    },
    {
      label: "New auth abstraction",
      description: "Clean separation — more upfront work, more flexible long-term"
    },
    {
      label: "External auth service",
      description: "Offload entirely — fastest to build, adds third-party dependency"
    }
  ]
}])
Scope Clarification
ask_user_question(questions: [{
  header: "Scope",
  question: "Should this feature support multiple organizations from the start?",
  options: [
    {
      label: "Yes, initial release",
      description: "Adds 2-3 more integration points and multi-tenant data isolation now"
    },
    {
      label: "Later if needed",
      description: "Simpler initial build, but may require data migration later"
    },
    {
      label: "Not needed at all",
      description: "Single-tenant only — keeps everything simple"
    }
  ]
}])
Show full SKILL.md (295 more words)Show less
Edge Case Discovery
ask_user_question(questions: [{
  header: "Edge Cases",
  question: "What should happen when the external API is down during export? The codebase has no retry logic for this service yet.",
  options: [
    {
      label: "Graceful degradation",
      description: "Show partial results with a warning banner — best UX when service is degraded"
    },
    {
      label: "Hard error to user",
      description: "Show clear error message asking them to retry — simplest implementation"
    },
    {
      label: "Auto-retry with queue",
      description: "Queue the request and retry — most robust but requires background job infrastructure"
    }
  ]
}])

Interview Flow: Best Practices

  1. One question per call: Use ask_user_question with a single-question array each time. This keeps the interaction focused and lets answers inform the next question.
  2. Provide context: Always explain why you're asking and how the answer affects implementation.
  3. Show trade-offs: Each option's description should explain the trade-off, not just restate the label.
  4. Lead with impact: Architecture-changing questions first, then high-impact, then medium-impact.
  5. Allow deferral: The "Chat about this" option and custom answer ("Type something") let users skip or elaborate. Honour "skip" gracefully.
  6. Limit questions by impact, not quota: Stop when all unresolved questions are either architecture-changing, acceptance-test-changing, security/data-risk-changing, or explicitly deferred. A complete spec may need zero follow-up questions.
  7. Acknowledge each answer: Before asking the next question, briefly restate what was decided and its implications.

Answer Processing

After each answer via ask_user_question:

  1. Acknowledge: "Thanks — going with [option] means we'll [impact]. That makes sense because [reason]."
  2. Update plan: Adjust your mental model of the implementation.
  3. Decide follow-up: Did the answer reveal a new unknown? Ask a follow-up question now, or note it for later.
  4. Move on: Ask the next prioritized question or summarise if done.

After all questions:

  • Present a summary of decisions made
  • Flag any decisions deferred
  • Suggest next steps

Integration with Other Skills

  • Before Interview: Run blind-spot-pass to inform questions
  • After Interview: Document decisions in ADR or implementation notes
  • Follow-up: Run quiz-me after implementation to verify understanding

Success Criteria

A good spec interview:

  • Uncovers at least 2-3 assumptions user didn't state
  • Changes the implementation approach in at least one significant way
  • Uses ask_user_question for focused, one-at-a-time questioning
  • Takes 5-15 minutes to complete
  • Provides clear direction for next steps
  • Avoids unnecessary questions (don't ask if you can infer)

© jellydn, 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 skills/spec-interview of jellydn/my-ai-tools.

Open the folder on GitHubat commit 7a06584

Compare with similar skills

Spec 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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SkillStarsUsed inTokensAuto-checkLicenceRepo updated
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Interview Meaddyosmani/agent-skills103k6 repos~3.8kAutomated safety check: PassMIT
Grillingpietheinstrengholt/rssmonster56432 repos~510Automated safety check: PassMIT
Agentic Workflow Designerdotnet/Open-XML-SDK4.6k2 repos~3.5kAutomated safety check: PassMIT
Ask User QuestionMemTensor/MemOS12k—~1kAutomated safety check: PassApache-2.0

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Categories

Questions about Spec Interview

What does Spec Interview do?

Clarify requirements through targeted questions — uncovers unknown unknowns in specs. Spec Interview is an agent skill from jellydn/my-ai-tools.

When should I use Spec Interview?

Spec Interview fits situations like: tasks that involve Requirements gathering.

How do I install Spec Interview in Claude Code?

Run `npx skills add jellydn/my-ai-tools --skill spec-interview -a claude-code`. Or copy the skill folder (skills/spec-interview in jellydn/my-ai-tools) into .claude/skills/spec-interview in your project. Claude Code loads it when a task matches its description.

How do I install Spec Interview in Codex?

Run `npx skills add jellydn/my-ai-tools --skill spec-interview -a codex`. Or copy the skill folder (skills/spec-interview in jellydn/my-ai-tools) into .agents/skills/spec-interview in your project. Codex loads it when a task matches its description.

Can I use Spec 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 jellydn/my-ai-tools --skill spec-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/spec-interview, .gemini/skills/spec-interview, .github/skills/spec-interview and .opencode/skills/spec-interview in your project.

What does Spec Interview need to run?

SKILL.md names no scripts, command-line tools or credentials: Spec Interview is instructions for the agent only. Compatibility (from SKILL.md): cline, claude, opencode, amp, codex, gemini, cursor, pi.

Does Spec 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 Spec 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 Spec Interview use?

Spec Interview is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Spec Interview use?

About 2.1k tokens (SKILL.md is roughly 8.4k 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 Spec Interview?

Skills that share tags, products or a category with Spec Interview: Using Superpowers (farm-fe/farm, 5.6k stars), Interview Me (addyosmani/agent-skills, 103k stars), Grilling (pietheinstrengholt/rssmonster, 564 stars) and Agentic Workflow Designer (dotnet/Open-XML-SDK, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Spec Interview?

jellydn (a GitHub user) maintains it in jellydn/my-ai-tools, which has 123 GitHub stars. The repository holds 33 skills in this directory. The repository was last updated on October 8, 2026.

Source: jellydn/my-ai-tools on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.