interview-plan

interview-plan is a skill for Claude Code from ayberkcansever/ai-skills. It costs 144 tokens per session (6,961 once invoked), scanned A, original, MIT.

An interview process that asks one question at a time and produces a detailed implementation plan after examining the codebase.

In plain words
What is it for?
It is for planning changes whose business rules, existing users, stored data, or rollout details need careful decisions.
Why use it?
It exposes unclear requirements, compatibility risks, and technical edge cases before coding begins.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter. Also seen: mentions AGENTS.md.

Good fit It is for planning changes whose business rules, existing users, stored data, or rollout details need careful decisions.

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Install with agentmods
npx agentmods add skills/ayberkcansever/ai-skills/interview-plan
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ayberkcansever/ai-skills --skill interview-plan
Clone the repo
git clone --depth 1 https://github.com/ayberkcansever/ai-skills

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.

agentmods badge for interview-plan

README.md
[![agentmods](https://agentmods.dev/badge/skills/ayberkcansever/ai-skills/interview-plan/github.svg)](https://agentmods.dev/skills/ayberkcansever/ai-skills/interview-plan)
Your own site
<a href="https://agentmods.dev/skills/ayberkcansever/ai-skills/interview-plan"><img src="https://agentmods.dev/badge/skills/ayberkcansever/ai-skills/interview-plan/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.

agentmods 80×15 button for interview-plan

Your own site · 80×15
<a href="https://agentmods.dev/skills/ayberkcansever/ai-skills/interview-plan"><img src="https://agentmods.dev/badge/skills/ayberkcansever/ai-skills/interview-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 144 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,961 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00144 $0.06961
Opus 5 $0.00072 $0.03481
Sonnet 5 $0.00029 $0.01392
Haiku 4.5 $0.00014 $0.00696

Measured 4d ago against content hash 4a7e80fa1a14, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

interview-plan 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 4d 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.

sdlc/cursor/interview-plan/SKILL.md · 584 lines

How it starts

The opening of the file, as written. The whole thing — 584 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Plan Interview

Produce one ambiguity-free implementation plan by interviewing the user one question at a time. The crucial goal is a plan with no ambiguity across three lenses:

  • Business requirements — what problem, measurable success, who, why not simpler.
  • Backward compatibility — every consumer, stored record, in-flight message, and deployed client affected, enumerated from the actual code.
  • Technical — behaviour, edge cases, data model, contracts, testing, rollout.

The model discovers and interrogates — it reads the codebase first, confronts the user with findings, and pushes back with alternatives. It does not prepare a plan from the user's words alone. A plan built only from what the user said is the failure mode this skill exists to prevent.

The single output is one plan markdown file written to disk — authored by the write-plan skill from the spec this interview assembles, then verified by this skill's Audit Pass. It must be complete enough that a fresh reader can implement it without a follow-up question.

The goal is not "minimum questions to start coding". It is "no ambiguity left that would bite us at code-review or in production".

Rules

  1. One question per turn. Never bundle multiple questions, sub-questions, or "while we're at it" asides. If you catch yourself writing "and also" — delete it and save it for the next turn. (Batch-confirmations of clearly out-of-scope checklist items are not bundled questions; see "Using the Checklist Efficiently".)
  2. Discover before asking. Run the Discovery Phase first. Any fact that the codebase, configs, docs, or git history can answer — read it, don't ask. Questions of intent ("should we retry on this error?", "is partial success acceptable?", "do we need an audit log?") must always be asked; the code tells you what is, not what should be. Every question you do ask should be grounded in something you read, not in the user's framing.
  3. Walk the decision tree depth-first. Resolve a decision's dependencies before moving to the next sibling. Don't jump branches until the current one is settled.
  4. Always recommend an answer. For every question, propose your recommended answer with a one-sentence reason. The user can accept, override, or refine — this prevents stall. When the user overrides a recommendation, record the decision as D<n>. <decision> (overrides recommendation: <user's reason>). Pushback with no reason → ask once for the reason before recording; never silently flip. Code-grounded findings remain facts regardless of the user's framing.
  5. Tag the type of decision. Mark each question as [technical], [product], [compat], or [scope] so the user knows whether they're being asked an engineering tradeoff, a business call, a backward-compat call, or a boundary call.
  6. Write the decision log to disk as you go. Append each agreed answer, explicit non-goal, and open risk to docs/specs/<TICKET-ID>/spec.md at the moment it is decided (create the file from the Spec File Template below on the first decision). Every behavioral decision carries a Check: line — a runnable command or named test that proves it (or manual QA: <step>). Cannot write one = decision too vague; sharpen it in the same turn before recording. Long interviews degrade chat recall; the file cannot forget. The end-of-interview spec assembly (Output Step 1) organizes this file — it does not reconstruct decisions from memory. Also track the consumer list from Discovery in the same file so the backward-compat lens can be closed item by item. On every append, check the new decision against the existing decisions and non-goals — if it contradicts one, surface both immediately and ask which wins; do not record two conflicting decisions.
  7. Push back with code-grounded findings, not just questions. When you spot a simpler design, a non-obvious risk, a broken consumer, or a more idiomatic approach for this codebase — surface it on your own turn. Ground it in something you actually read. Use this format:

Read the full file on GitHub · 584 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.

  1. 4d ago Changed · +21 lines 4a7e80fa1a14
  2. 9d ago First seen · 563 lines · 144 tokens per session scan A 593a9a4373bd

Subscribe to this mod's changes

interview-plan is a skill published in the GitHub repository ayberkcansever/ai-skills (2 stars, last pushed 5d ago), licensed MIT. It adds 144 tokens to every session and 6,961 once invoked, about $0.0007 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.