research-ops

Original：🇺🇸 English

Translated

Evidence-first current-state research workflow for ECC. Use when the user wants fresh facts, comparisons, enrichment, or a recommendation built from current public evidence and any supplied local context.

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Sourceaffaan-m/everything-claude-code

Added on2026-04-06

NPX Install

npx skill4agent add affaan-m/everything-claude-code research-ops

SKILL.md Content

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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

text

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