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.
npx skills add ayberkcansever/ai-skills --skill interview-plangit clone --depth 1 https://github.com/ayberkcansever/ai-skillsWrote 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/ayberkcansever/ai-skills/interview-plan)<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.
<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>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 | $0.00144 | $0.06961 |
| Opus 5 | $0.00072 | $0.03481 |
| Sonnet 5 | $0.00029 | $0.01392 |
| Haiku 4.5 | $0.00014 | $0.00696 |
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.
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
- 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".)
- 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.
- 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.
- 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. - 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. - 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.mdat the moment it is decided (create the file from the Spec File Template below on the first decision). Every behavioral decision carries aCheck:line — a runnable command or named test that proves it (ormanual 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. - 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:
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.
- 4d ago Changed · +21 lines 4a7e80fa1a14
- 9d ago First seen · 563 lines · 144 tokens per session scan A 593a9a4373bd
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.
Other skills, from other repositories
toolport
Use when the user asks for any external action or data: email, payments, deployments, databases, repos, issues, files, web search, messaging, or any connected service. Toolport is the front door to every MCP server on this machine; search it before concluding a capability is unavailable.
maestro
Direct Maestro command hub for Codex slash menu: compose or choose a Frontier model panel, settings, terse, and update.
terse
Token-efficient terse narration mode. Cuts agent explanation tokens while preserving requested artifacts. Levels: lite, full, ultra. Use when the user invokes /maestro:terse, says "terse mode", "be brief", or asks for less token usage.
frontier
Maestro Frontier local multi-CLI fusion engine — switch mode, or run a prompt through the panel.
maestro-settings
View and change Maestro toggles (terse, frontier, context-bar, discipline, verify) via the settings CLI.
maestro-terse
Toggle Maestro terse narration level (lite, full, ultra, off) via the settings CLI.