Memory Research

Research an external subject, synthesize what you find, and create a structured Basic Memory entity — with the user's approval.

When to Use

Explicit triggers:

"Research [subject]"
"Look up [subject]"
"What do you know about [subject]?"
"Evaluate [subject]"

Implicit triggers (also activate this skill):

A bare name: "Terraform"
A URL: "https://example.com"
A name with context: "Acme Corp — saw them at the conference"

Workflow

Step 1: Web Research

Search for current information across multiple sources. Aim for 3-5 searches to build a well-rounded picture:

[subject name] site
[subject name] overview
[subject name] news [current year]
[subject name] [relevant domain keywords]

What to gather by entity type:

Entity Type	Key Information
Organization	What they do, products/services, stage (startup/growth/public), funding, leadership, headquarters, employee count, notable partnerships or contracts
Person	Current role, organization, background, expertise, notable work, public presence
Technology	What it does, who maintains it, maturity, ecosystem, alternatives, adoption
Topic/Domain	Definition, current state, key players, trends, relevance to user's context

Step 2: Check Existing Knowledge

Before proposing a new entity, search Basic Memory:

python

search_notes(query="Acme Corp")
search_notes(query="acme")

Try name variations — full name, abbreviation, acronym, domain name.

If the entity already exists:

Report what you found in Basic Memory alongside your web research
Offer to update the existing note with new information
Use
```
edit_note
```
to append new observations or update outdated ones

If the entity doesn't exist, proceed to evaluation.

Step 3: Evaluate and Summarize

Present your findings in a structured summary. Include all relevant information organized by section:

markdown

## [Subject Name]

**Type:** [Organization / Person / Technology / Topic]

**Summary:** [2-4 sentences: what this is, why it matters, key distinguishing facts]

**Key Details:**
- [Organized by what's relevant for the entity type]
- [Stage, funding, leadership for orgs]
- [Role, expertise, affiliations for people]
- [Maturity, ecosystem, alternatives for tech]

**Relevance:** [Why this matters to the user — connection to their work, domain, or interests.
If no obvious connection: "No specific connection identified."]

**Sources:**
- [URLs of key sources consulted]

Evaluation Guidelines

Use hedging language. Web research is a snapshot, not ground truth:

"Appears to be", "Based on public information", "Estimated"
"As of [date]", "According to [source]"
Never state funding amounts, employee counts, or revenue as exact unless citing a primary source

Don't fabricate. If information isn't available, say so:

"Leadership information not publicly available"
"Funding details not disclosed"

Let the user define relevance. Don't impose a fixed evaluation framework. Instead, highlight facts and let the user draw conclusions. If the user has a specific evaluation rubric (strategic fit, buy/partner/compete, etc.), they'll tell you — apply it when asked.

Step 4: Propose Entity Creation

After presenting the summary, ask for approval:

Create Basic Memory entity for [Subject]?
  Location: [suggested-folder]/[entity-name].md
  Type: [entity type]

  [yes / no / modify]

If the user provided context with their request ("saw them at the conference"), include that context in the proposed entity.

Step 5: Create the Entity

After approval, create a structured note. Adapt the template to the entity type:

Organization

python

write_note(
  title="Acme Corp",
  directory="organizations",
  note_type="organization",
  tags=["organization", "relevant-tags"],
  content="""# Acme Corp

## Overview
[2-3 sentence description from research]

## Products & Services
- [Key offerings discovered in research]

## Background
**Stage:** [Startup / Growth / Public]
**Headquarters:** [Location]
**Employees:** [Estimate, hedged]
**Leadership:** [Key people if found]
**Founded:** [Year if found]

## Observations
- [relevance] Why this entity matters in user's context
- [source] Researched on YYYY-MM-DD
- [additional observations from research findings]

## Relations
- [Link to related entities already in the knowledge graph]"""
)

Person

python

write_note(
  title="Jane Smith",
  directory="people",
  note_type="person",
  tags=["person", "relevant-tags"],
  content="""# Jane Smith

## Overview
[Current role and affiliation. Brief background.]

## Background
**Role:** [Title at Organization]
**Expertise:** [Key domains]
**Notable:** [Publications, talks, projects if found]

## Observations
- [role] Title at Organization
- [expertise] Key technical or domain expertise
- [source] Researched on YYYY-MM-DD

## Relations
- works_at [[Organization]]"""
)

Technology

python

write_note(
  title="Technology Name",
  directory="concepts",
  note_type="concept",
  tags=["concept", "technology", "relevant-tags"],
  content="""# Technology Name

## Overview
[What it is and what problem it solves]

## Key Details
**Maintained by:** [Organization or community]
**Maturity:** [Experimental / Stable / Mature]
**License:** [If applicable]
**Alternatives:** [Comparable tools or approaches]

## Observations
- [definition] What this technology does in one sentence
- [maturity] Current state and adoption level
- [source] Researched on YYYY-MM-DD

## Relations
- [Link to related concepts, tools, or projects in the knowledge graph]"""
)

Adapt these templates freely. The key elements are: note_type/tags parameters, an overview, structured details, observations with categories, and relations.

Step 6: Store Source Context

If the user provided context with their request, capture it in the entity:

python

# User said: "Acme Corp — saw their demo at the conference last week"
edit_note(
  identifier="Acme Corp",
  operation="append",
  section="Observations",
  content="- [context] Saw their demo at conference, week of 2026-02-17"
)

This context is often the most valuable part — it's the user's relationship to the entity, which web research can't provide.

Guidelines

Always web search. Don't rely on training data alone. Research should reflect current, verifiable information.
Search Basic Memory first. Check for existing entities before creating new ones. Update rather than duplicate.
Hedge uncertain information. Use qualifiers for estimates, unverified claims, and inferred details.
Store source URLs. Include the URLs you consulted, either in observations or a Sources section. This enables the user to verify and dig deeper.
Get approval before creating. Present your findings and let the user decide whether to create the entity and what to include.
Capture user context. If the user told you why they're researching (met at a conference, evaluating as a vendor, etc.), that context belongs in the entity.
Don't over-research. 3-5 web searches is usually enough. The goal is a useful knowledge graph entry, not an exhaustive report.
Link to existing knowledge. Relate the new entity to things already in the knowledge graph. Connections compound value.

memory-research

NPX Install

Tags

SKILL.md Content

Memory Research

When to Use

Workflow

Step 1: Web Research

Step 2: Check Existing Knowledge

Step 3: Evaluate and Summarize

Evaluation Guidelines

Step 4: Propose Entity Creation

Step 5: Create the Entity

Organization

Person

Technology

Step 6: Store Source Context

Guidelines