Agent skill

Ulw Interview

by rlaope in rlaope/oh-my-hermes

[omh] Vague, underspecified request: one-question-at-a-time clarification.

MITAuto-check: warningsAgent Workflows

Install Ulw Interview

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add rlaope/oh-my-hermes --skill ulw-interview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install rlaope/oh-my-hermes ulw-interview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/rlaope/oh-my-hermes.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ulw-interview .claude/skills/ulw-interview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ulw-interview
GitHub stars
3.2k
Token cost
~2.6k tokens
SKILL.md length
1,470 words
Files
2 (incl. references)
Skills in repo
143
Repo updated
First seen
Licence
MIT

At a glance

[omh] Vague, underspecified request: one-question-at-a-time clarification.

  • Works in 3 steps: All three dimensions resolved. Emit the… → The user asks to stop. "Just plan it",… → Budget reached at Round 6. After the…
  • The user says: deep-interview
  • SKILL.md covers Why This Exists, Do Not Use When, Examples and Completion Checklist, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ulw Interview is an agent skill from rlaope/oh-my-hermes. [omh] Vague, underspecified request: one-question-at-a-time clarification. Use when the user says: deep-interview, interview me, clarify, feature shaping, ambiguous product request, one question, 要件を詰めて, 曖昧な要求.

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/ambiguity-taxonomy.md`).

It sits in Agent Workflows, covering Requirements gathering. The repository describes itself as: All in one plugin for Hermes Agent ⚚ the coding intelligence, a long-term memory system and model optimized workflow packages. The licence is MIT.

When your agent uses it

  • The user says: deep-interview
  • Feature shaping
  • Ambiguous product request

Example prompts

  • “/ulw-interview”

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. All three dimensions resolved. Emit the clarified brief and continue to planning.
  2. The user asks to stop. "Just plan it", "그냥 해줘", or any explicit request to proceed ends
  3. Budget reached at Round 6. After the Round 6 answer, do not ask another

What it can do on your machine

Read from SKILL.md and the folder at commit f772a94. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ulw Interview loads about 2.6k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 1,470 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: warnings

The automated check found patterns that need a careful read before installing.

  • WarningTells the agent its actions are pre-authorized / not to stop for confirmationSKILL.md:24
    from the repository or local artifacts without asking the user.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rlaope/oh-my-hermes at commit f772a94, republished under its MIT licence (© rlaope). 1,470 words, ~2,646 tokens.

Download SKILL.mdSave it as .claude/skills/ulw-interview/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
ulw-interview
description
[omh] Vague, underspecified request: one-question-at-a-time clarification. Use when the user says: deep-interview, interview me, clarify, feature shaping, ambiguous product request, one question, 要件を詰めて, 曖昧な要求.

Deep Interview

This is a Hermes-native deep-interview workflow skill.

Why This Exists

deep-interview exists to stop Hermes from guessing through ambiguous product, workflow, or implementation intent; it converts uncertainty into a clarified brief before planning or handoff.

Do Not Use When

  • The request already has concrete scope, acceptance criteria, and verification commands.
  • The missing information is discoverable from the repository or local artifacts without asking the user.
  • The user asked for immediate read-only analysis and the ambiguity does not change the answer.
  • The ambiguity is specifically repository terminology or project-language alignment; use context and its direct-lookup/frontier boundary.
  • The open question is answerable by a small reversible experiment rather than another interview round; use decision-prototype.

Examples

Good example:

  • Prompt: $deep-interview before planning Discord and Slack routing, ask what each channel owns and what evidence counts.
  • Expected behavior: Ask one decision-changing question at a time, then produce goals, non-goals, and acceptance criteria.
  • Why: The request explicitly rejects assumptions and needs product boundaries before implementation.

Bad example:

  • Prompt: $deep-interview fix this failing test; the traceback and expected behavior are attached.
  • Expected behavior: Proceed to diagnosis or implementation instead of interviewing.
  • Why: The required facts are already available, so more questions would slow the workflow.

Completion Checklist

  • The clarified brief names goals, non-goals, constraints, and one next planning or handoff path.
  • Remaining ambiguity is listed only when it changes the plan, risk, or stop condition.
  • No implementation handoff is prepared until the blocking decision is resolved.

Recovery Notes

  • If an answer surfaces new ambiguity, file it under one of the three clarity dimensions and keep asking only while the round budget allows; once round 6 is reached, record the rest as assumptions and plan.
  • If repo evidence can answer the question, inspect it before asking the user.

Workflow Lane

  • Current lane: Intent -> plan (oh-my-hermes, meta-router, deep-interview, context, plan, ralplan, adversarial-consensus, codebase-onboarding, +8 more) - clarify, plan, ship, or loop goals.
  • If intent belongs to another lane, hand back to oh-my-hermes or name the adjacent workflow.
  • Shared product, routing, compatibility, and evidence rules: omh-routing/references/skill-common-rail.md.

Interview Round Protocol

This interview is bounded: at most 6 rounds, one question per round.

Before each question, find the most recent round header you emitted in this thread and add 1. If there is no header, you are at Round 1. If you have already asked questions here but cannot recover the number (for example after context compaction), do not restart at Round 1 — run the mid-interview check now and continue from Round 4.

Every question is preceded by this header on its own line, then a blank line, then the question:

Round {n}/6 · Clarity: {percent}% ({resolved}/3) · Targeting: {dimension}
  • Clarity is scored against exactly three fixed dimensions: outcome (what is true when this is done), constraints and non-goals (what bounds the work), and success criteria (how anyone would verify it). {resolved} counts how many you could restate in one sentence without a qualifier. The denominator is always 3; {percent} is 0, 33, 67, or 100.
  • {dimension} names the unresolved dimension this question targets — the one that most changes the plan, not the easiest one.
  • A new concern raised in an answer files under one of the three dimensions. It never extends the denominator and never extends the round budget. Once the budget is spent, record it as an assumption instead of asking about it.

Voice — the header is instrumentation; the question is a conversation.

  • Never fold counters, ratios, or dimension names into the question sentence.
  • Ask the way a senior colleague would ask out loud: one sentence, no preamble, no restating what the user just said, no numbered sub-questions. If it reads like a form field, rewrite it.
  • Outside the header line, the user never hears the words round, budget, dimension, or resolved.
  • Mirror the user's language in the header labels and the question. Korean header: 라운드 {n}/6 · 명확도: {percent}% ({resolved}/3) · 확인 중: {목표/제약과 비목표/성공 기준}. Never mix languages in one message.
  • The clarified brief follows the same rule: write its headings and labels in the user's language. Translate those terms, never transliterate them.

Answer options — every question ships with candidates.

After the question sentence, offer the likely answers as a short numbered list: two to four real candidates, then one final free-input entry. Each candidate is an answer the user could actually pick — drawn from the request, repo evidence, or the tradeoff the question is really about, never filler to reach a count, and never a candidate whose text is itself a bare number (it would collide with reply-by-number). The last entry is always the open door, in the user's language — for example English N) Something else — type your answer, Korean N) 기타 — 직접 입력.

  • A number, an option's own words, or a completely different free-text answer are all valid; free text is always accepted, even when it matches no option. Never re-ask because the reply was not a listed option.
  • Options mirror the user's language, like the header and the question.
  • The list is an answer palette, not extra questions; it does not break the one-question rule.

Mid-interview check — this is not a stop rule.

Before asking the question that would be Round 4, offer the choice instead: say where things stand and ask whether to keep going or plan now — your own words, the user's language, one short sentence, with the same option shape: keep going / plan now / free input. The check is not a round: emit it without a header. If the user chooses to continue, the next question is Round 4; if they choose to plan, stop rule 2 applies.

Stop rules — the first match ends the interview.

  1. All three dimensions resolved. Emit the clarified brief and continue to planning.
  2. The user asks to stop. "Just plan it", "그냥 해줘", or any explicit request to proceed ends questioning immediately, at any round. Emit the brief and record each unresolved dimension as an assumption with the value you are assuming.
  3. Budget reached at Round 6. After the Round 6 answer, do not ask another question. Say plainly that you are moving to the brief with what you have, name what stayed unresolved, and continue.

These are stop rules you follow, not caps OMH enforces. When torn between one more question and stopping, stop and plan.

Show full SKILL.md (397 more words)Show less

Use When

Use before planning or execution when requirements are materially ambiguous.

Strong routing signals: `deep-interview`, `$deep-interview`, `interview me`, `don't assume`, `clarify`, `feature shaping`, `ambiguous product request`, `one question`, `要件を詰めて`, `曖昧な要求`, `一問一答で確認`, `オンボーディング`, `온보딩`, `부드럽게`, `모호한 제품 요청`, `기획자`, `개발자 사이`, `澄清需求`, `需求不明确`, `一次问一个问题`

Catalog Metadata

Category: clarification Phase: discovery Hermes role: planner Quality tier: clarity-gated Reasoning demand: light

Quality bar:

  • Ask exactly one blocking question per turn unless the wrapper explicitly supports a structured batch.
  • Offer two to four candidate answers plus a free-input option with every question, and accept free text over the list at any time.
  • Tie each question to a missing decision that changes the plan, handoff, or stop condition.
  • Before the first question, load references/ambiguity-taxonomy.md and score every category Clear/Partial/Missing, then spend the round budget worst-first and write each accepted answer back into the artifact being clarified.
  • Emit a clarified brief with non-goals and acceptance criteria before planning or delegation.

Handoff policy:

Run directly in Hermes or the chat wrapper; produce a clarified brief before any coding handoff is prepared.

Required inputs:

  • initial request
  • known repo facts
  • current ambiguity

Expected outputs:

  • clarified brief
  • non-goals
  • decision boundaries

Artifact expectations:

  • clarity summary or transcript when the wrapper supports it

Safety rules:

  • Ask one question at a time.
  • Gather discoverable repo facts before asking the user.
  • Stop interviewing when all three clarity dimensions are resolved, the user asks to stop, or round 6 is reached.

Runtime Evidence

Preferred harness for this skill: deep-interview.

sh
omh runtime record --skill deep-interview --harness deep-interview --status started

Record observed delegation results; otherwise return not_available or not_observed. Prepared OMH routing is not execution, review, CI, merge-readiness, or merge evidence.

  • Treat wrapper memory/context summaries as advisory local context, not proof of opaque Hermes memory reads or changes. Preserve workflow intent and stop conditions; verify before claiming completion. Reply in the user's own words and the host's own voice: its SOUL.md persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a stop condition or a decision the user owns ends the turn, offer the next action as a question rather than declaring what will not be done.

Use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.

Shared product, compatibility, topology, memory, harness, and execution rules: omh-routing/references/skill-common-rail.md. Load it when applicable; otherwise name an unavailable capability.

© rlaope, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/ulw-interview of rlaope/oh-my-hermes.

  • SKILL.md
  • references/ambiguity-taxonomy.md

Open the folder on GitHubat commit f772a94

Compare with similar skills

Ulw Interview next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Interview Meaddyosmani/agent-skills103k6 repos~3.8kAutomated safety check: PassMIT
Grillingpietheinstrengholt/rssmonster56432 repos~510Automated safety check: PassMIT
Agentic Workflow Designerdotnet/Open-XML-SDK4.6k2 repos~3.5kAutomated safety check: PassMIT
Ask User QuestionMemTensor/MemOS12k—~1kAutomated safety check: PassApache-2.0

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Categories

Questions about Ulw Interview

What does Ulw Interview do?

[omh] Vague, underspecified request: one-question-at-a-time clarification. Ulw Interview is an agent skill from rlaope/oh-my-hermes. [omh] Vague, underspecified request: one-question-at-a-time clarification.

When should I use Ulw Interview?

Ulw Interview fits situations like: the user says: deep-interview; feature shaping; ambiguous product request.

How do I install Ulw Interview in Claude Code?

Run `npx skills add rlaope/oh-my-hermes --skill ulw-interview -a claude-code`. Or copy the skill folder (skills/ulw-interview in rlaope/oh-my-hermes) into .claude/skills/ulw-interview in your project. Claude Code loads it when a task matches its description.

How do I install Ulw Interview in Codex?

Run `npx skills add rlaope/oh-my-hermes --skill ulw-interview -a codex`. Or copy the skill folder (skills/ulw-interview in rlaope/oh-my-hermes) into .agents/skills/ulw-interview in your project. Codex loads it when a task matches its description.

Can I use Ulw Interview in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rlaope/oh-my-hermes --skill ulw-interview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ulw-interview, .gemini/skills/ulw-interview, .github/skills/ulw-interview and .opencode/skills/ulw-interview in your project.

What does Ulw Interview need to run?

SKILL.md names no scripts, command-line tools or credentials: Ulw Interview is instructions for the agent only.

Does Ulw Interview access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ulw Interview safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Ulw Interview use?

Ulw Interview is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ulw Interview use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.2k tokens, read only when the agent opens those files.

What are the alternatives to Ulw Interview?

Skills that share tags, products or a category with Ulw Interview: Using Superpowers (farm-fe/farm, 5.6k stars), Interview Me (addyosmani/agent-skills, 103k stars), Grilling (pietheinstrengholt/rssmonster, 564 stars) and Agentic Workflow Designer (dotnet/Open-XML-SDK, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ulw Interview?

rlaope (a GitHub user) maintains it in rlaope/oh-my-hermes, which has 3,207 GitHub stars. The repository holds 143 skills in this directory. The repository was last updated on October 8, 2026.

Source: rlaope/oh-my-hermes on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.