lore

Search & Research
v1.0.0
Benign

Search and ingest knowledge from Lore, a research repository with citations.

1K downloads1K installsby @mishkinf

Setup & Installation

Install command

clawhub install mishkinf/lore

If the CLI is not installed:

Install command

npx clawhub@latest install mishkinf/lore

Or install with OpenClaw CLI:

Install command

openclaw skills install mishkinf/lore

or paste the repo link into your assistant's chat

Install command

https://github.com/openclaw/skills/tree/main/skills/mishkinf/lore

What This Skill Does

Lore is a research knowledge repository that stores documents, meeting notes, interviews, and decisions with full citations linking back to original sources. It provides MCP tools for ingesting external content and searching stored knowledge across a project. Content is deduplicated on ingest and retrievable with its original source URL.

Unlike ad-hoc note storage, Lore preserves original source URLs alongside content so answers can always be traced back to the primary document rather than a paraphrase.

When to Use It

  • Saving sprint planning notes so they're searchable in future sprints
  • Looking up why a technical decision was made before changing it
  • Ingesting a Slack thread of user feedback into a project's knowledge base
  • Checking what was discussed in past user interviews before writing a spec
  • Retaining a synthesized insight like a recurring user complaint for later reference
View original SKILL.md file
# Lore Knowledge Base

Lore is a research knowledge repository you have access to via MCP tools. It stores documents, meeting notes, interviews, and decisions with full citations — not just summaries, but the original content linked back to its source. Use it to ground your answers in evidence and to preserve important context from your conversations.

## When to Ingest Content into Lore

Push content into Lore using the `ingest` tool whenever you encounter information worth preserving:

- **After conversations**: When a user shares meeting notes, interview transcripts, or important documents, ingest them so they're searchable later.
- **External content**: When you fetch content from Slack, Notion, GitHub, email, or other systems, ingest the relevant parts into Lore.
- **Decisions and context**: When important decisions are made or context is shared that future conversations will need.

Always include:
- `source_url`: The original URL (Slack permalink, Notion page URL, GitHub issue URL) for citation linking.
- `source_name`: A human-readable label like "Slack #product-team" or "GitHub issue #42".
- `project`: The project this content belongs to.

Ingestion is idempotent — calling `ingest` with the same content twice is safe and cheap (returns immediately with `deduplicated: true`).

## When to Search Lore

Before answering questions about past decisions, user feedback, project history, or anything that might already be documented:

1. **Use `search`** for quick lookups. Pick the right mode:
   - `hybrid` (default): Best for most queries
   - `keyword`: For exact terms, names, identifiers
   - `semantic`: For conceptual queries ("user frustrations", "pain points")

2. **Use `research`** only when the question requires cross-referencing multiple sources or synthesizing findings. It costs 10x more than `search` — don't use it for simple lookups.

3. **Use `get_source`** with `include_content=true` when you need the full original text of a specific document.

## When to Retain Insights

Use `retain` (not `ingest`) for short, discrete pieces of knowledge:
- Key decisions: "We chose X because Y"
- Synthesized insights: "3/5 users mentioned Z as their top issue"
- Requirements: "Must support SSO for enterprise"

## Citation Best Practices

When presenting information from Lore, always cite your sources:
- Reference the source title and date
- Quote directly when possible
- If a `source_url` is available, link to the original

## Example Workflows

**User asks about past decisions:**
1. `search("authentication approach decisions", project: "my-app")`
2. Review results, get full source if needed: `get_source(source_id, include_content: true)`
3. Present findings with citations

**User shares meeting notes:**
1. `ingest(content: "...", title: "Sprint Planning Jan 15", project: "my-app", source_type: "meeting", source_name: "Google Meet", participants: ["Alice", "Bob"])`
2. Confirm ingestion to user

**User asks a broad research question:**
1. `research(task: "What do users think about our onboarding flow?", project: "my-app")`
2. Present the synthesized findings with citations

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Saving sprint planning notes so they're searchable in future sprints

AGENT
  1. 1Saving sprint planning notes so they're searchable in future sprints
  2. 2Looking up why a technical decision was made before changing it
  3. 3Ingesting a Slack thread of user feedback into a project's knowledge base
  4. 4Checking what was discussed in past user interviews before writing a spec
  5. 5Retaining a synthesized insight like a recurring user complaint for later reference
OUTPUT
Search and ingest knowledge from Lore, a research repository with citations.

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Last updatedFeb 26, 2026