---
title: "Agent Skill to Research Ops"
platform: "Promptally"
canonical_url: "https://app.promptally.dev/p/skill-research-ops"
modality: "SKILL"
category: "Fullstack & Backend Engineering"
compatible_models: [Universal / Model Agnostic]
access_tier: "Free Public Artifact (No Auth Required)"
license: "MIT"
---

# Agent Skill to Research Ops

**Summary:** Agent Skill to Research Ops is a verified SKILL blueprint on Promptally in Fullstack & Backend Engineering optimized for leading AI models. 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. Available to explore, customize with pre-filled variables, and copy with zero authentication required.

## Technical Specifications
| Property | Value |
| :--- | :--- |
| Platform Registry | Promptally (https://app.promptally.dev) |
| Modality | SKILL |
| Category | Fullstack & Backend Engineering |
| Compatible Engines | Universal / Model Agnostic |
| Dynamic Variables | None (Static Blueprint) |
| Approx. Token Length | 901 |
| Access Tier | Free Public Artifact (No Auth Required) |
| License | MIT |

### Pre-Filled Template & Production Source

````md
---
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
- 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

````

## Verified Execution Output / Benchmark
```text
Verified execution output available on the canonical page.
```

## Related Blueprints on Promptally
- [Agent Skill to Knowledge Ops](https://app.promptally.dev/p/skill-knowledge-ops)
- [Agent Skill to Content Hash Cache Pattern](https://app.promptally.dev/p/skill-content-hash-cache-pattern)
- [Agent Skill to Ck](https://app.promptally.dev/p/skill-ck)
- [Agent Skill to Swift Protocol Di Testing](https://app.promptally.dev/p/skill-swift-protocol-di-testing)
- [Agent Skill to Prisma Patterns](https://app.promptally.dev/p/skill-prisma-patterns)
- [Agent Skill to Agentic Engineering](https://app.promptally.dev/p/skill-agentic-engineering)
- [Agent Skill to Python Patterns](https://app.promptally.dev/p/skill-python-patterns)
- [Agent Skill to Docs Governance](https://app.promptally.dev/p/skill-docs-governance)