tech-topic-research

Deep Research

Teach or evaluate a topic through progressive disclosure. Deliver a useful gist within seconds, then deepen the answer with parallel web research and a written study guide when the user wants more.

When to use this skill

Use this skill when the user explicitly invokes it with a topic, or when the user asks to:

• Learn about, understand, or evaluate a technology, tool, framework, library, service, concept, or methodology.
• Compare two or more such items ("X vs Y", "X or Y for {use case}", "compared to Z").
• Decide whether to adopt a tool, prepare for an interview on a topic, build a mental model, or get a curated reading list.

This skill is invoked only by explicit user request — never autonomously. The frontmatter flag disable-model-invocation: true blocks auto-matching; hosts that don't honor it MUST still refuse without an explicit user request. Explicit invocation is consent to consider running the skill; the Token cost and approval gate (Phase 1 step 5) still applies.

When NOT to use this skill

Skip this skill — it is the wrong tool — when:

• The user wants a single-fact lookup ("what version of X ships JSONB?", "who is the CEO of Y?"). One WebSearch call answers it.
• The user is debugging a specific error and wants a fix. Use a debugging workflow, not a research one.
• The user wants a quick sanity check or a yes/no answer.
• The user already has a clear opinion and just wants confirmation.
• The user explicitly asks for "a quick answer" or "in one sentence".

If you are not sure whether the request is research or a lookup, default to the lookup — research is opt-in, see the next section.

Token cost and approval gate

⚠️ This skill is heavy on token usage. A Standard run spawns four parallel haiku subagents, each issuing 4–6 web searches and several WebFetch calls. A Deep run adds an Opus subagent and a Sonnet reviewer. Total cost is typically 5–30× a normal turn, and the subagent fan-out cannot be cancelled mid-flight.