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.
curl -O https://raw.githubusercontent.com/pskoett/measuring-ai-proficiency/main/.claude/skills/plan-interview/SKILL.mdgit clone --depth 1 https://github.com/pskoett/measuring-ai-proficiencyWrote 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.
[](https://agentmods.dev/skills/pskoett/measuring-ai-proficiency/plan-interview)<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="Measured on agentmods" height="20"></a>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 | $0.00054 | $0.00668 |
| Sonnet 5 | $0.00022 | $0.00267 |
| Haiku 4.5 | $0.00011 | $0.00134 |
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 6d 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.
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
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.
- 6d ago First seen · 192 lines · 108 tokens per session scan A 067a8b80ee84
plan-interview is a skill published in the GitHub repository pskoett/measuring-ai-proficiency (11 stars, last pushed 1mo ago), licensed MIT. It adds 108 tokens to every session and 1,336 once invoked, about $0.0005 per session on Opus 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…