interview-me for Openclaw

Interview-me turns vague requests into structured, actionable intent through an adaptive one-question-at-a-time interview.

thcjp
v1.0.0
Aug 10, 2026
0
243
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install interview-me

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install interview-me using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is interview-me?

interview-me is an AI agent skill for discovering what users actually need rather than simply executing an unclear initial request. It conducts a focused, sequential interview and continues asking targeted questions until it reaches approximately 95% confidence in the underlying intent.

As part of Openclaw Skills, it can support development automation, data processing, workflow organization, format conversion, and downstream skill integrations. The skill accepts JSON, text, or Markdown input and returns a structured success response with the processed result, execution metadata, and error information.

interview-me Use Cases

  • Clarifying ambiguous development or automation requests before execution
  • Gathering requirements for data analysis, transformation, and workflow orchestration
  • Converting unstructured user input into an actionable specification
  • Preparing structured context for another AI agent skill or automation pipeline
  • Handling batch-oriented processing through configurable options
  • Standardizing intake workflows where manual clarification is slow or error-prone
  • Triggering an interview when a user request lacks sufficient detail
  • Avoiding use in scenarios that require continuous real-time human intervention or have no meaningful requirements to clarify

How interview-me Works

  1. Receive the user-provided content and optional format and options parameters.
  2. Validate that the input is present and that the declared format is supported: json, text, or markdown.
  3. Analyze the initial request to identify ambiguities, assumptions, constraints, and missing decision criteria.
  4. Ask one targeted question at a time instead of presenting a long questionnaire.
  5. Incorporate each answer into the evolving intent model and select the next highest-value question.
  6. Continue the interview until the underlying goal is sufficiently clear, targeting approximately 95% confidence.
  7. Structure the clarified result for execution, export, batch processing, or integration with other Openclaw Skills.
  8. Return a status envelope containing success, data.result, metadata, and an error value when applicable.
  9. Apply validation, retry, fallback, or escalation behavior when input, data sources, API access, permissions, or processing steps fail.

interview-me Setup

Prerequisites

  • An AI agent platform that supports SKILL.md, such as Claude Code, Cursor, Codex, or Gemini CLI.
  • Windows, macOS, or Linux.
  • Access to an LLM API supplied by the agent platform.
  • A required data source, described by the deployment or GitHub-based source configuration.
  • Prepared input content in JSON, text, or Markdown format.

Install and register the skill

The source document does not specify a package-manager installation command. Install or copy the interview-me skill directory into the skills location supported by your agent, ensuring that SKILL.md remains at the expected path.

mkdir -p ~/.openclaw/skills/interview-me
cp SKILL.md ~/.openclaw/skills/interview-me/SKILL.md

Register or reload the skill from your agent's configuration, then verify that the agent can discover interview-me and its read and exec tool permissions.

Configure credentials

Provide the LLM API credentials through the agent platform's supported secret or environment-variable mechanism. Do not commit API keys to the skill directory.

export LLM_API_KEY="your-api-key"

Invoke the skill with required content and optional format and options values. Confirm that the source data is reachable, permissions are correct, and logs are enabled for troubleshooting and auditing.

interview-me Data Schema & Taxonomy

Request parameters

Field Type Required Description
content string Yes User content or request to clarify and process.
format string No Input format: json, text, or markdown.
options object No Advanced settings such as output style, batch size, and processing behavior.

Response envelope

{
  "success": true,
  "data": {
    "result": "Clarified and structured interview-me result",
    "metadata": {
      "skill": "interview-me",
      "version": "1.0.0",
      "pricing_tier": "L2-advanced"
    }
  },
  "error": null
}

Data organization

  • content is the source request being analyzed.
  • format identifies how the input should be parsed and normalized.
  • options stores optional execution preferences and batch-processing controls.
  • data.result contains the clarified, structured output produced by the interview workflow.
  • data.metadata records skill identity, version, and pricing tier for traceability.
  • error is null for successful execution and contains failure information when processing does not complete.
  • No specific output filenames or persistent storage layout are defined in the source document; file paths should therefore be supplied by the host agent or integration.

interview-me Advanced Features

  • One-question-at-a-time interviewing focused on uncovering latent user intent
  • Confidence-oriented clarification targeting approximately 95% intent certainty
  • Support for JSON, text, and Markdown input normalization
  • Structured response envelopes suitable for agent-to-agent handoffs and automation pipelines
  • Configurable options for output style, batch size, and advanced processing behavior
  • Batch operations and result export support when enabled by the host workflow
  • Built-in error recovery with automatic retries and downgrade or fallback handling
  • GitHub-based source validation for improved accuracy and traceability where configured
  • Compatibility with Claude Code, Cursor, Codex, Gemini CLI, and other SKILL.md-compatible agents
  • Integration with other Openclaw Skills to create multi-step development and productivity workflows
  • Operational safeguards for empty input, invalid formats, oversized data, API failures, permission errors, rate limits, and timeouts
  • Security guidance covering API protection, access control, sensitive-data handling, audit logs, backups, and system updates

SKILL.md


Loading

Related Openclaw Skills

METADATA

Github Stars: 0
forks: 0

Featured*