AgentSkillsCN

Interview

对 TV 流媒体可用性追踪器的指标、错误、日志以及数据库健康状况进行审查。提供可观测性、分类处理、调试支持以及问题修复方案。当您需要检查应用健康状况、排查问题，或监控系统性能时，可运用此技能。

SKILL.md
--- frontmatter
description: Interview the user to capture stable, cross-project preferences and save them to Honcho
allowed-tools: chat, create_conclusion
user-invocable: true

Honcho Interview

Kick off a short interview to learn stable, cross-project aspects of the user and store them in Honcho memory.

Guardrails

  • •Focus on global traits that are unlikely to change between projects.
  • •Avoid project-specific topics, credentials, addresses, or other sensitive information.
  • •Ask one question at a time and wait for the answer before proceeding.
  • •If an answer is vague, ask one brief clarification before saving a conclusion.
  • •If the user declines to answer, skip that topic and move on.
  • •Use existing knowledge to avoid repeating questions the memory already covers.

Pre-Interview Context (Required)

Before asking any questions, use the chat tool to get a maximally thorough overview of what is already known about the user. Present a concise summary to the user, then tailor the interview to fill gaps or confirm uncertain areas.

Example tool call format:

code
chat({ "query": "Give a maximally thorough overview of what you already know about this user, focusing on stable preferences and cross-project traits. Include any uncertainties or gaps." })

Interview Flow (Medium Depth)

Ask these questions in order, skipping any that are already answered by the pre-interview context:

  1. •Communication style: Do you prefer concise answers, detailed explanations, or a mix?
  2. •Tone: Do you prefer a direct, professional tone or a more conversational one?
  3. •Structure: Do you prefer bullet points, step-by-step instructions, or narrative explanations?
  4. •Technical depth: What level of technical detail should I assume (beginner, intermediate, expert)?
  5. •Learning preference: Do you prefer explanations first, examples first, or both together?
  6. •Code quality focus: What matters most by default (clarity, performance, tests, minimal changes)?
  7. •Collaboration style: Should I make changes directly, propose options first, or ask before edits?
  8. •Environment defaults: What OS/shell/tooling should I assume for commands and paths?

Saving Conclusions

After each answer, create exactly one concise conclusion and call create_conclusion.

Guidelines for conclusions:

  • •Use a single sentence.
  • •Make it specific and unambiguous.
  • •Avoid hedging if the user gives a clear preference.

Example tool call format:

code
create_conclusion({ "content": "Prefers concise, bullet-pointed responses with a professional tone." })

Wrap-up

When finished, briefly recap the conclusions you saved and ask if anything should be corrected. Only save a new conclusion if the user explicitly clarifies or corrects a prior answer.