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Rewrite README intro with value proposition and roadmap #2

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28 changes: 27 additions & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,33 @@
[![Docs](https://img.shields.io/badge/docs-readthedocs-blue)](https://agentic-slayer.readthedocs.io/)
[![License](https://img.shields.io/github/license/MotleyAI/slayer)](LICENSE)

A lightweight open-source semantic layer for AI agents and humans
A lightweight, open-source semantic layer that lets AI agents query data without writing SQL.

---

### The problem
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@coderabbitai coderabbitai bot Mar 25, 2026

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⚠️ Potential issue | 🟡 Minor

Fix heading level to follow markdown hierarchy.

The heading jumps from h1 to h3, skipping h2. Per markdown best practices (MD001), headings should increment by one level at a time.

📝 Proposed fix 
-### The problem
+## The problem

Apply the same fix to the other new section headings on lines 16 and 23.

📝 Committable suggestion

‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

Suggested change
### The problem
## The problem
🧰 Tools
🪛 markdownlint-cli2 (0.21.0)

[warning] 12-12: Heading levels should only increment by one level at a time
Expected: h2; Actual: h3

(MD001, heading-increment)

🤖 Prompt for AI Agents 
Verify each finding against the current code and only fix it if needed.

In `@README.md` at line 12, Change the "### The problem" heading to an H2 by
replacing "### The problem" with "## The problem" and likewise adjust the other
new section headings referenced (the headings at lines showing new sections,
e.g., the headings currently using "###" around the README content) to use
one-level-lower numbers so the document increments heading levels sequentially
(H1 -> H2 -> H3), ensuring all new headings follow the MD001 rule.


When AI agents write raw SQL, things break in production — hallucinated column names, incorrect joins, metrics that drift across queries. Existing semantic layers (Cube, dbt metrics) were built for dashboards: heavy infrastructure, slow model refresh cycles, and limited expressiveness for the kinds of ad-hoc analysis agents need.

### What SLayer does differently

- **Auto-ingestion with FK awareness** — Connect a database, and SLayer introspects the schema, detects foreign keys, and generates usable models with denormalized joins instantly. No manual modeling required to get started.
- **Dynamic model manipulation** — Agents create and edit models at runtime. Changes take effect immediately — no rebuild, no deploy, no restart.
- **Query-time expressions** — Compose derived metrics on the fly with the `fields` API (`"revenue / count"`, `"cumsum(revenue)"`, `"change_pct(revenue)"`). No need to pre-define every metric.
- **First-class time operations** — Built-in `time_shift`, `change`, `change_pct`, `cumsum`, `rank`, and `last` — all composable and nestable (e.g., `"change(cumsum(revenue))"`).

### Roadmap

- Measures from joined models
- Multistage queries
- Unpivoting
- Smart output formatting (currency, percentages)
- Auto-propagating filters
- Asof joins
- Chart generation (eCharts)
- Claude Code plugin with query skills

---

## Quick Start

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