agentleFS
Sign inStart free
scottwaterscottwater/skills/skills/interview-me/SKILL.md

interview-me

Skill2 starsChanged 2 months agoMITView on GitHub

A relentless interview that asks every frontier question at once, round by round, until you and the user reach a shared understanding.

98 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use constraint-driven-development, idea-refine and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • MrCipherSmithSkill

    constraint-driven-development

    Use when a project needs a persistent, measurable quality bar — starting a project without documented standards, 'set up constraints', or when coverage/perf/a11y thresholds get re-debated per-PR. Produces CONSTRAINTS.md with enforceable numbers, ratchets, and a guard against silent weakening. NOT for throwaway prototypes or a one-off CI setup.

    Also in 16 projects that use this

  • LLl0k0laDSkill

    idea-refine

    Refine ideas through structured divergent and convergent thinking. Use "idea-refine" or "ideate" to trigger.

    Also in 42 projects that use this

  • LLl0k0laDSkill

    using-agent-skills

    Use when starting a session or when you need to discover which skill applies to the current task. This is the meta-skill that governs how all other skills are discovered and invoked.

    Also in 32 projects that use this

  • HK-hubSkill

    observability-and-instrumentation

    Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.

    Also in 32 projects that use this

  • orenluxySkill

    implementation-notes

    Maintain an IMPLEMENTATION_NOTES.md decision log during any multi-step implementation session. Use this in EVERY session that involves writing or modifying code across more than one file, or any session expected to run longer than a few tool calls. Records decisions, deviations from plan, and open questions so nothing is silently guessed.

    Also in 3 projects that use this

  • HK-hubSkill

    deprecation-and-migration

    Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when deciding whether to maintain or sunset existing code.

    Also in 29 projects that use this

  • +6 more from LLl0k0laD

The file

Create a free account to mount
Files
SKILL.md

Interview the user relentlessly until you reach a shared understanding. Map this as a design tree: every decision branches into the decisions that hang off it.

Work the tree in rounds. The frontier is every decision whose prerequisites are already settled — the questions you can ask now without guessing at answers you haven't heard yet. Ask the whole frontier in one round: number each question and give your recommended answer. Then wait for the user's answers before the next round.

Each round the user answers reshapes the tree — settled decisions push the frontier outward and unblock questions that depended on them. Recompute the frontier and ask the next round. A question whose answer depends on another question still open in this round belongs to a later round, not this one.

Go deep. Follow each branch until it bottoms out; a vague answer is a new question, not a settled decision. Stay on the topic the user brought — don't drift into adjacent work or start solving.

Finding facts is your job, never the user's. When a frontier question needs a fact from the environment (filesystem, tools, etc.), dispatch one fresh exploration sub-agent to find it — don't ask the user for anything you could look up yourself. That explorer owns the lookup; its returned evidence satisfies this fact-finding step. Don't block on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait for the sub-agent to report — ask the rest of the frontier now. The decisions are the user's — put each to them and wait.

The interview writes nothing. The design tree lives in the conversation, and the output is the shared understanding itself.

Done

The session is done when the frontier is empty: every branch of the design tree visited, nothing left silently assumed. Summarize the settled tree and ask the user to confirm it. Do not act on it until the user confirms you have reached a shared understanding.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free