Research Ops

Use this when the user asks to research something current, compare options, enrich people or companies, or turn repeated lookups into a monitored workflow.

This is the operator wrapper around the repo's research stack. It is not a replacement for deep-research, exa-search, or market-research; it tells you when and how to use them together.

Skill Stack

Pull these ECC-native skills into the workflow when relevant:

• exa-search for fast current-web discovery
• deep-research for multi-source synthesis with citations
• market-research when the end result should be a recommendation or ranked decision
• lead-intelligence when the task is people/company targeting instead of generic research
• knowledge-ops when the result should be stored in durable context afterward

When to Use

• user says "research", "look up", "compare", "who should I talk to", or "what's the latest"
• the answer depends on current public information
• the user already supplied evidence and wants it factored into a fresh recommendation
• the task may be recurring enough that it should become a monitor instead of a one-off lookup

Guardrails

• do not answer current questions from stale memory when fresh search is cheap
• separate:

• do not spin up a heavyweight research pass if the answer is already in local code or docs

Workflow

1. Start from what the user already gave you

Normalize any supplied material into:

• already-evidenced facts
• needs verification
• open questions

2. Classify the ask

Choose the right lane before searching:

• quick factual answer
• comparison or decision memo
• lead/enrichment pass
• recurring monitoring candidate

3. Take the lightest useful evidence path first

• use exa-search for fast discovery
• escalate to deep-research when synthesis or multiple sources matter
• use market-research when the outcome should end in a recommendation
• hand off to lead-intelligence when the real ask is target ranking or warm-path discovery

4. Report with explicit evidence boundaries

For important claims, say whether they are:

• sourced facts
• user-supplied context
• inference
• recommendation

5. Decide whether the task should stay manual

If the user is likely to ask the same research question repeatedly, say so explicitly and recommend a monitoring or workflow layer instead of repeating the same manual search forever.

Output Format

QUESTION TYPE
factual / comparison / enrichment / monitoring

EVIDENCE
sourced facts
user-provided context

INFERENCE
what follows from the evidence

RECOMMENDATION
answer or next move
whether this should become a monitor

Pitfalls

• do not mix inference into sourced facts without labeling it
• do not ignore user-provided evidence
• do not use a heavy research lane for a question local repo context can answer
• do not give freshness-sensitive answers without dates

Verification

• important claims are labeled by evidence type
• freshness-sensitive outputs include dates
• the final recommendation matches the actual research mode used