What is plan-interview?

A requirements interview for planning a new feature, refactor, or other substantial implementation. It asks about technical limits, scope, risks, and what success means before implementation planning begins.

Use it before non-trivial implementation work to clarify performance, compatibility, scope boundaries, dependencies, trade-offs, and acceptance criteria.

It helps expose missing requirements and assumptions before coding starts, reducing the chance of building the wrong solution.

How pskoett/measuring-ai-proficiency configures Claude Code

Reuse

Borrowing it

Nothing to install: this file belongs to pskoett/measuring-ai-proficiency. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/plan-interview/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/pskoett/measuring-ai-proficiency

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/plan-interview/github.svg)](https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/plan-interview)

Your own site

<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/plan-interview"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/plan-interview/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/plan-interview"><img src="https://agentmods.dev/badge/skills/pskoett/measuring-ai-proficiency/plan-interview.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 108 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,336 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00108	$0.01336
Opus 5.5	$0.00043	$0.00534
Sonnet 5.5	$0.00022	$0.00267
Haiku 4.5	$0.00011	$0.00134

Measured 2mo ago against content hash 067a8b80ee84, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

plan-interview scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/plan-interview/SKILL.md · 192 lines

How it starts

The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Interview Skill

Purpose

Run a structured requirements interview before planning implementation. This ensures alignment between you and the user by gathering explicit requirements rather than making assumptions.

When Invoked

User calls /plan-interview <task description>.

Skip this skill if the task is purely research/exploration (not implementation).

Interview Process

Phase 1: Upfront Interview (Before Exploration)

Interview the user using AskUserQuestion in thematic batches of 2-3 questions when the provider supports it. For providers like GitHub Copilot without an AskUser tool, ask the same questions directly in chat and pause for responses before continuing.

Required Question Domains

Cover ALL four domains before proceeding:

Technical Constraints
- Performance requirements
- Compatibility needs
- Existing patterns to follow
- Architecture understanding (if codebase is unfamiliar)
Scope Boundaries
- What's explicitly OUT of scope
- MVP vs full vision
- Dependencies on other work
Risk Tolerance
- Acceptable tradeoffs (speed vs quality)
- Tech debt tolerance
- Breaking change acceptance
Success Criteria
- How will we know it's done?
- What defines "working correctly"?
- Testing/validation requirements

Question Generation

Generate questions dynamically based on the task - no fixed template
Group related questions into thematic batches
2-3 questions per batch (do not exceed)
Continue until you have actionable specificity (can describe concrete implementation steps)

Handling Edge Cases

Scenario	Action
Contradictory requirements	Make a recommendation with rationale, ask for confirmation
User pivots requirements	Restart interview fresh with new direction
Interrupted session	Ask user: continue where we left off or restart?

Anti-Patterns to Avoid

Do NOT ask variations of the same question
Do NOT make major assumptions without asking
Do NOT over-engineer plans for simple tasks

Read the full file on GitHub · 192 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

2mo ago First seen · 192 lines · 108 tokens per session scan A 067a8b80ee84

Subscribe to this mod's changes

plan-interview is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (13 stars, last pushed 3mo ago), licensed MIT. It adds 108 tokens to every session and 1,336 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Planning

plan-interview next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
plan-interviewpskoett/measuring-ai-proficiency	Skill	13	3mo ago	MIT	A	108	Claude Code
imagegenopenai/codex	Skill	133k	yesterday	Apache-2.0	A	113	Codex
playwright-climicrosoft/playwright	Skill	99k	yesterday	Apache-2.0	A	19	Claude Code
local-ai-agentsmicrosoft/ai-agents-for-beginners	Skill	79k	4d ago	MIT	A	200	Claude Code, Codex
systematic-debuggingobra/superpowers	Skill	313k	3d ago	MIT	A	21	Claude Code
next-cache-components-adoptionvercel/next.js	Skill	143k	today	MIT	A	95	Claude Code
next-partial-prefetching-adoptionvercel/next.js	Skill	143k	today	MIT	A	103	Claude Code

measuring-ai-proficiency: Skill for Claude Code