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.
Scanned 9/19/2026
Install to Claude Code
npx -y skills add majinmagros/magros.ai-skills --skill research-ops --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: research-ops
description: 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.
metadata:
origin: ECC
---
# 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
## Exemplo
```text
"Compara ferramenta A vs B hoje": exa-search (descoberta) → deep-research (síntese citada)
→ market-research se vira recomendação ranqueada → knowledge-ops p/ guardar
Separa: fato com fonte / evidência do usuário / inferência / recomendação
Nunca: responder do cache quando busca fresca é barata
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