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beads-knowledge

Capture solved problems as structured JSONL entries in .beads/memory/knowledge.jsonl.

50|2|Updated Feb 8, 2026
Search Tags:#recall#beads#memory#knowledge#traceability#jsonl
One-click install
npx skills add https://github.com/roberto-mello/beads-compound-plugin --skill beads-knowledge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: beads-knowledge
Source: https://github.com/roberto-mello/beads-compound-plugin/tree/main/plugins/beads-compound/gemini/skills/beads-knowledge
Command: npx skills add https://github.com/roberto-mello/beads-compound-plugin --skill beads-knowledge

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Capture solved problems as structured JSONL entries in .beads/memory/knowledge.jsonl and log bead comments to maintain traceability back to specific work items.

Core Features & Use Cases

  • Append solved problem details as JSONL entries for fast recall and searchable history.
  • Log bead comments for traceability and context linking to work items.
  • Classify each entry with knowledge prefixes (LEARNED, DECISION, FACT, PATTERN, INVESTIGATION) to support targeted recall.

Quick Start

Record a solved problem as a JSONL knowledge entry and add a bead comment for traceability.

Frequently Asked Questions about beads-knowledge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I store solved engineering problems as searchable knowledge for future sessions?▼

You can store solved engineering problems as structured JSONL entries in .beads/memory/knowledge.jsonl, logging details with a unique key, type, content, tags, and timestamp for fast recall and searchable history across sessions.

What's the best way to maintain traceability between debugging decisions and specific work items?▼

Maintaining traceability is done by logging bead comments alongside your knowledge entries, which links the recorded engineering fixes, design decisions, and debugging context directly back to specific work items.

How does classifying knowledge entries with prefixes like LEARNED or DECISION improve recall?▼

Classifying knowledge entries with prefixes like LEARNED, DECISION, FACT, PATTERN, and INVESTIGATION supports targeted recall by categorizing the JSONL data, making it easier to filter and auto-recall specific types of solved problems.

Can I use JSONL knowledge bases to auto-recall past debugging sessions into my current workflow?▼

Yes, knowledge entries stored as structured JSONL can auto-recall into future sessions, bringing past debugging sessions, design decisions, and engineering fixes forward without manual searching.

What data structure is used to record engineering fixes for fast recall and searchable history?▼

Engineering fixes are recorded as structured JSONL entries where each item contains a unique key, type, content, source, tags, timestamp, and bead reference to ensure fast recall and a searchable history.

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