---
id: daily-research-ops
name: "research-ops"
url: https://skills.yangsir.net/skill/daily-research-ops
author: affaan-m
domain: data-ai
tags: ["research", "information-retrieval", "monitoring", "competitive-intelligence", "due-diligence"]
install_count: 2500
rating: 4.30 (8 reviews)
github: https://github.com/affaan-m/everything-claude-code
---

# research-ops

> 研究操作员，用于当前事件研究、选项比较、人员公司信息充实，或将重复查询转化为监控工作流

**Stats**: 2,500 installs · 4.3/5 (8 reviews)

## Before / After 对比

### 研究效率

**Before**:

手动搜索多个信息源、切换工具、整理数据，一次深度研究需要 2-3 小时，且难以持续跟踪

**After**:

自动化聚合多源数据、智能比对选项、持续监控更新，30 分钟完成深度研究并自动预警

| Metric | Before | After | Change |
|---|---|---|---|
| 研究时间 | 180分钟 | 30分钟 | -83% |

## Readme

# research-ops

# 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

Weekly Installs541Repository[affaan-m/everyt…ude-code](https://github.com/affaan-m/everything-claude-code)GitHub Stars157.6KFirst Seen11 days agoSecurity Audits[Gen Agent Trust HubPass](/affaan-m/everything-claude-code/research-ops/security/agent-trust-hub)[SocketPass](/affaan-m/everything-claude-code/research-ops/security/socket)[SnykWarn](/affaan-m/everything-claude-code/research-ops/security/snyk)Installed oncodex511opencode495gemini-cli492cursor491kimi-cli491antigravity491

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*Source: https://skills.yangsir.net/skill/daily-research-ops*
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