Skill: phernandez/memory-research (version 0.1.0)

--- name: memory-research description: "Research an external subject using web search, synthesize findings into a structured Basic Memory entity. Use when asked to research a company, person, technology, or topic — or when a bare name or URL is provided that implies a research request." --- # 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- Offer to update the existing note with new information - Use `edit_note` to append new observations or update outdated ones If the entity doesn't exist, proceed to evaluation.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.


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