Agent skill

Plan Interview

by secondsky in secondsky/claude-skills

Adaptive interview-driven spec generation. An agent skill from secondsky/claude-skills.

MITAuto-check passedAgent Workflows

Install Plan Interview

skills CLI
$ npx skills add secondsky/claude-skills --skill plan-interview -a claude-code

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

GitHub CLI
$ gh skill install secondsky/claude-skills plan-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/secondsky/claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/plan-interview/skills/plan-interview .claude/skills/plan-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
plan-interview
GitHub stars
227
Token cost
~882 tokens
SKILL.md length
330 words
Files
7 (incl. references)
Skills in repo
169
Repo updated
First seen
Licence
MIT

At a glance

Adaptive interview-driven spec generation. An agent skill from secondsky/claude-skills.

  • Works in 6 steps: Foundations & Scope - Stakeholders,… → Technical Deep-Dive - Architecture, data… → User Experience - Personas, flows,… → …
  • Converting rough plans into comprehensive specifications
  • SKILL.md covers When to Use, Available Components, Interview Phases and Interview Philosophy, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Plan Interview is an agent skill from secondsky/claude-skills. Adaptive interview-driven spec generation. Use when converting rough plans into comprehensive specifications, needing structured requirements gathering, or transforming ideas into implementation-ready documentation.

Its SKILL.md is about 880 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `references/example-spec.md`, `references/interview-techniques.md` and `references/phase-1-clarifications.md`).

It sits in Agent Workflows, covering Requirements gathering. The repository describes itself as: Production-ready skills for Claude Code CLI - Cloudflare, React, Tailwind v4, and AI integrations. The licence is MIT.

When your agent uses it

  • Converting rough plans into comprehensive specifications
  • Needing structured requirements gathering
  • Transforming ideas into implementation-ready documentation

Example prompts

  • “/plan-interview”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

  1. Foundations & Scope - Stakeholders, success criteria, constraints, MVP scope
  2. Technical Deep-Dive - Architecture, data models, scalability, security
  3. User Experience - Personas, flows, cognitive load, error recovery
  4. Risks & Tradeoffs - Risk categorization, blast radius, contingency plans
  5. Operationalization - Testing, deployment, monitoring
  6. Wrap-Up (optional) - Only for complex plans with remaining gaps

What it can do on your machine

Read from SKILL.md and the folder at commit 8837836. 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 bash).

    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

Plan Interview loads about 882 tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 330 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~882
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

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 secondsky/claude-skills at commit 8837836, republished under its MIT licence (© secondsky). 330 words, ~882 tokens.

Download SKILL.mdSave it as .claude/skills/plan-interview/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
plan-interview
description
Adaptive interview-driven spec generation. Use when converting rough plans into comprehensive specifications, needing structured requirements gathering, or transforming ideas into implementation-ready documentation.
license
MIT

Plan Interview Skill

Transform rough plans into comprehensive, implementation-ready specifications through adaptive, structured interviews.

When to Use

  • Converting a plan or idea into a detailed specification
  • Gathering requirements through structured questioning
  • Transforming rough documentation into implementation-ready specs
  • Ensuring all edge cases, risks, and stakeholders are considered before implementation

Available Components

Command: /plan-interview:interview [plan-file]

Adaptive interview that calibrates depth based on plan complexity:

ComplexitySignalsQuestions
SimpleSingle feature, clear scope10-15
ModerateMulti-component, some integrations18-23
ComplexCross-system, many stakeholders22-28

Usage:

bash
/plan-interview:interview docs/feature-plan.md
# Output: docs/feature-plan-spec.md
Agent: spec-reviewer

Autonomous quality analysis of specifications across 4 dimensions:

  • Completeness (25 pts) - All sections populated?
  • Consistency (25 pts) - No contradictions?
  • Clarity (25 pts) - No ambiguous language?
  • Edge Cases (25 pts) - Error handling defined?

Triggers when you say "review my spec" or "check specification quality".

Interview Phases

  1. Foundations & Scope - Stakeholders, success criteria, constraints, MVP scope
  2. Technical Deep-Dive - Architecture, data models, scalability, security
  3. User Experience - Personas, flows, cognitive load, error recovery
  4. Risks & Tradeoffs - Risk categorization, blast radius, contingency plans
  5. Operationalization - Testing, deployment, monitoring
  6. Wrap-Up (optional) - Only for complex plans with remaining gaps

Interview Philosophy

Core Principle: Depth over breadth. Better to deeply understand critical aspects than superficially cover everything.

Key Techniques:

  • Non-obvious questions - Skip what the plan already answers
  • Edge probing - What happens in unusual cases?
  • Assumption surfacing - Make implicit beliefs explicit
  • Contradiction detection - Flag when answers don't align
  • Adaptive depth - Probe deeper on complex areas, move faster on clear ones

Spec Output Structure

Generated specs include:

  • Overview (problem, solution, success criteria, stakeholders)
  • Functional and non-functional requirements
  • Technical design (architecture, data models, APIs, security)
  • User experience (personas, flows, states, edge cases)
  • Risks and mitigations (risk register, tradeoffs, contingency plans)
  • Implementation notes (key decisions, dependencies, migration)
  • Operationalization (testing, deployment, monitoring)
  • Open questions and out-of-scope items
  • Phasing (MVP vs future)

References

Load these for deeper guidance during interviews:

  • references/phase-1-clarifications.md - Foundations questions and pitfalls
  • references/phase-2-technical.md - Architecture discussion patterns
  • references/phase-3-ux.md - Persona development, UX patterns
  • references/phase-4-risks.md - Risk assessment frameworks
  • references/interview-techniques.md - Cross-cutting interview skills
  • references/example-spec.md - Annotated high-quality spec example

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

Files

SKILL.md and 6 other files (references) in plugins/plan-interview/skills/plan-interview of secondsky/claude-skills.

  • SKILL.md
  • references/example-spec.md
  • references/interview-techniques.md
  • references/phase-1-clarifications.md
  • references/phase-2-technical.md
  • references/phase-3-ux.md
  • references/phase-4-risks.md

Open the folder on GitHubat commit 8837836

Compare with similar skills

Plan 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.

Plan Interview compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Plan Interview this skillsecondsky/claude-skills227—~882Automated safety check: PassMIT
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
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 Plan Interview

What does Plan Interview do?

Adaptive interview-driven spec generation. An agent skill from secondsky/claude-skills. Plan Interview is an agent skill from secondsky/claude-skills. Adaptive interview-driven spec generation.

When should I use Plan Interview?

Plan Interview fits situations like: converting rough plans into comprehensive specifications; needing structured requirements gathering; transforming ideas into implementation-ready documentation.

How do I install Plan Interview in Claude Code?

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

How do I install Plan Interview in Codex?

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

Can I use Plan 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 secondsky/claude-skills --skill plan-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/plan-interview, .gemini/skills/plan-interview, .github/skills/plan-interview and .opencode/skills/plan-interview in your project.

What does Plan Interview need to run?

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

Does Plan 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 Plan 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 Plan Interview use?

Plan 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 Plan Interview use?

About 882 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.8k tokens, read only when the agent opens those files.

What are the alternatives to Plan Interview?

Skills that share tags, products or a category with Plan 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 Plan Interview?

secondsky (a GitHub user) maintains it in secondsky/claude-skills, which has 227 GitHub stars. The repository holds 169 skills in this directory. The repository was last updated on September 28, 2026.

Source: secondsky/claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.