research-ops

SKILL.md

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:

sourced fact

user-provided evidence

inference

recommendation

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

Do not restart the analysis from zero if the user already built part of the model.

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

Freshness-sensitive answers should include concrete dates.

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