learn

name	learn
description	Research topics via web search, fetch content, and file to inbox with provenance metadata. The first step for acquiring external knowledge. Chains into /seed and /pipeline for full processing. Supports multiple search engines and content types. Triggers on: "learn", "research", "look up", "find out about"

/learn

Research topics via web search and ingest into the knowledge pipeline.

Purpose

Acquire external knowledge on a topic. Search the web, evaluate results, fetch the most relevant content, convert it to markdown with provenance metadata, and hand it off to /seed for processing pipeline entry. This is how the knowledge base grows from external sources — every piece of acquired knowledge gets full provenance tracking.

Usage

# Research a topic
/learn "enterprise AI pricing models 2026"

# Research and auto-process through pipeline
/learn "HIPAA compliance for AI agents" --pipeline

# Research with specific sources
/learn "Monte Carlo tree search" --sources arxiv,wikipedia

# Research with result limit
/learn "competitor analysis SaaS pricing" --max 5

# Research and seed without full pipeline
/learn "Firecracker VM security model" --seed-only

# View learning history
/learn --history

Arguments

Flag	Type	Default	Description
<query>	positional	required	Research query or topic
--max	int	5	Maximum sources to fetch
--sources	string[]	all	Limit to specific sources: web, arxiv, wikipedia, github, news
--depth	enum	standard	quick (titles + snippets), standard (fetch + summarize), deep (fetch + full pipeline)
--pipeline	flag	false	Auto-run through /pipeline after seeding
--seed-only	flag	false	Seed but don't run pipeline
--tags	string[]	auto	Topic tags to apply
--reason	string	—	Why this research is needed (aids retrieval)
--history	flag	false	View past research sessions
--output-dir	path	inbox/learned/	Where to write fetched content

Workflow

1. Search — Query web search APIs with the topic. Retrieve top N results with titles, URLs, snippets.
2. Evaluate — Score results by relevance, recency, and source authority. Filter out low-quality sources (content farms, paywalled without access, duplicates).
3. Fetch — For each selected result:
   • Fetch full page content
   • Extract main content (strip navigation, ads, sidebars)
   • Convert to clean markdown
   • Preserve images if informational (diagrams, charts)
4. Annotate — Add provenance metadata to each fetched item: source URL, author, publication date, fetch date, relevance score, search query that found it.
5. Seed — Pass each annotated item to /seed for inbox entry. If --pipeline, chain directly into /pipeline.
6. Summarize — Produce a research brief: what was found, key findings across sources, gaps in coverage, suggested follow-up queries.
7. Log — Record research session: query, sources checked, sources fetched, total tokens consumed.

Output

## Research Report: "enterprise AI pricing models 2026"

**Sources searched:** 12 | **Fetched:** 5 | **Seeded:** 5
**Total content:** ~14,200 words

### Sources Acquired

| # | Title | Source | Date | Relevance | Status |
|---|-------|--------|------|-----------|--------|
| 1 | Enterprise AI Pricing: 2026 Benchmarks | Bessemer Venture Partners | 2026-02 | 0.94 | seeded |
| 2 | Per-Seat vs. Usage-Based: The AI Pricing Debate | a16z | 2026-01 | 0.89 | seeded |
| 3 | AI SaaS Pricing Survey (n=200) | OpenView Partners | 2025-12 | 0.85 | seeded |
| 4 | How We Price Our AI Product | Anthropic Blog | 2026-03 | 0.78 | seeded |
| 5 | The Death of Per-Seat Pricing | TechCrunch | 2026-02 | 0.72 | seeded |

### Key Findings (cross-source)
1. Per-seat pricing remains dominant for enterprise AI (68% of surveyed companies)
2. Usage-based pricing growing fastest (42% YoY adoption increase)
3. Median enterprise AI price: $1,800-2,500/seat/year
4. Hybrid models (base + usage) emerging as best practice

### Coverage Gaps
- No data found on AI pricing for healthcare-specific verticals
- Limited comparison with open-source alternatives
- Suggested follow-up: `/learn "AI pricing healthcare vertical"`

### Seeded Items
Files written to `inbox/learned/`:
- `2026-03-20-bessemer-ai-pricing.md`
- `2026-03-20-a16z-pricing-debate.md`
- `2026-03-20-openview-pricing-survey.md`
- `2026-03-20-anthropic-pricing.md`
- `2026-03-20-techcrunch-pricing.md`

Next step: `/pipeline inbox/learned/2026-03-20-*.md --batch` to process all.

Dependencies

• Web search capability (search API or web browsing)
• Web fetch capability (HTTP client)
• HTML-to-markdown converter
• /seed — Downstream handoff for each acquired source
• /pipeline — Optional full processing chain
• File system write access for output directory