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docs: bring README up to date with v0.6.0 / v0.6.1 merges #783

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19 changes: 13 additions & 6 deletions README.md
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Expand Up@@ -122,6 +122,9 @@ Credit analysis, expensive query detection, warehouse right-sizing, unused resou
### Cross-Dialect Translation
Transpile SQL between Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, MySQL, SQL Server, and DuckDB.

### Cross-Warehouse Data Parity
Row-level and column-level diffing across 12 warehouses (including cross-dialect pairs like Postgres ↔ Snowflake or Databricks ↔ Fabric) via the `data_diff` tool and `/data-parity` skill. Five algorithms — `auto`, `joindiff`, `hashdiff`, `profile`, and `cascade` — partitioned execution for 100M+ row tables, and a `profile`-only mode for PII / PHI / PCI environments.

@coderabbitaicoderabbitaiBotMay 3, 2026

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⚠️ Potential issue | 🟡 Minor | ⚡ Quick win

Align scale claim with tool docs to avoid overstating capability.

At Line 126, README says partitioned execution is for “100M+ row tables,” but the tool description in packages/opencode/src/altimate/tools/data-diff.ts frames partitioning as recommended for “>10M rows.” Please make these thresholds consistent (or cite benchmark context if 100M+ is intentional).

🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@README.md` at line 126, README currently claims "partitioned execution for
100M+ row tables" while the tool docs in
packages/opencode/src/altimate/tools/data-diff.ts recommend partitioning for
">10M rows"; update one or the other so both are consistent: either change the
README phrasing to match the tool doc (e.g., "recommended for >10M rows") or
update the tool doc to justify and match "100M+ rows" and add a brief citation
to benchmark context; reference the strings "100M+ row tables" in README and the
threshold text ">10M rows" in data-diff.ts (and mention
`data_diff`/`/data-parity` in the doc update) when making the change.

@cubic-dev-aicubic-dev-aiBotMay 3, 2026

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P3: The threshold "100M+ row tables" appears inconsistent with the tool implementation in data-diff.ts, which recommends partitioned execution for >10M rows. Consider aligning this claim with the actual tool threshold to avoid overstating the capability.

Prompt for AI agents
Check if this issue is valid — if so, understand the root cause and fix it. At README.md, line 126:
<comment>The threshold "100M+ row tables" appears inconsistent with the tool implementation in `data-diff.ts`, which recommends partitioned execution for >10M rows. Consider aligning this claim with the actual tool threshold to avoid overstating the capability.</comment>
<file context>
@@ -122,6 +122,9 @@ Credit analysis, expensive query detection, warehouse right-sizing, unused resou
Transpile SQL between Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, MySQL, SQL Server, and DuckDB.
+### Cross-Warehouse Data Parity
+Row-level and column-level diffing across 12 warehouses (including cross-dialect pairs like Postgres ↔ Snowflake or Databricks ↔ Fabric) via the `data_diff` tool and `/data-parity` skill. Five algorithms — `auto`, `joindiff`, `hashdiff`, `profile`, and `cascade` — partitioned execution for 100M+ row tables, and a `profile`-only mode for PII / PHI / PCI environments.
+
### PII Detection & Safety
</file context>
Suggested change
Row-level and column-level diffing across 12 warehouses (including cross-dialect pairs like Postgres ↔ Snowflake or Databricks ↔ Fabric) via the `data_diff` tool and `/data-parity` skill. Five algorithms — `auto`, `joindiff`, `hashdiff`, `profile`, and `cascade` — partitioned execution for 100M+ row tables, and a `profile`-only mode for PII / PHI / PCI environments.
Row-level and column-level diffing across 12 warehouses (including cross-dialect pairs like Postgres ↔ Snowflake or Databricks ↔ Fabric) via the `data_diff` tool and `/data-parity` skill. Five algorithms — `auto`, `joindiff`, `hashdiff`, `profile`, and `cascade` — partitioned execution for 10M+ row tables, and a `profile`-only mode for PII / PHI / PCI environments.


### PII Detection & Safety
Automatic column scanning for PII across 15 categories with 30+ regex patterns. Safety checks and policy enforcement before query execution.

Expand DownExpand Up@@ -151,15 +154,15 @@ Each mode has scoped permissions, tool access, and SQL write-access control.

## Supported Warehouses

Snowflake · BigQuery · Databricks · PostgreSQL · Redshift · ClickHouse · DuckDB · MySQL · SQL Server · Oracle · SQLite · MongoDB
Snowflake · BigQuery · Databricks · PostgreSQL · Redshift · ClickHouse · DuckDB · MySQL · SQL Server · Microsoft Fabric · Oracle · SQLite · MongoDB

First-class support with schema indexing, query execution, and metadata introspection. SSH tunneling available for secure connections.

## Works with Any LLM

Model-agnostic — bring your own provider or run locally.

Anthropic · OpenAI · Google Gemini · Google Vertex AI · Amazon Bedrock · Azure OpenAI · Mistral · Groq · DeepInfra · Cerebras · Cohere · Together AI · Perplexity · xAI · OpenRouter · Ollama · GitHub Copilot
Anthropic · OpenAI · Google Gemini · Google Vertex AI · Amazon Bedrock · Azure OpenAI · Databricks AI Gateway · Snowflake Cortex · Mistral · Groq · DeepInfra · Cerebras · Cohere · Together AI · Perplexity · xAI · OpenRouter · Ollama · LM Studio · GitHub Copilot

## Skills

Expand All@@ -178,12 +181,16 @@ Contributions welcome — docs, SQL rules, warehouse connectors, and TUI improve

## Changelog

- **v0.5.11** (March 2026) — `altimate-code check` CLI command, data-viz improvements, Codespaces support, session tracing, 52 CI test fix
- **v0.6.1** (April 2026) — BigQuery finops fixes (correct `INFORMATION_SCHEMA` columns, multi-region support), advisory `anti-slop` workflow
- **v0.6.0** (April 2026) — cross-warehouse data parity (`data_diff` tool + `/data-parity` skill), Microsoft Fabric / MSSQL support, Databricks AI Gateway provider, Amazon Bedrock custom-endpoints guide
- **v0.5.21** (April 2026) — automated dbt unit test generation (`/dbt-unit-tests`), manifest parse cache, dialect-aware `sql_explain`
- **v0.5.20** (April 2026) — Altimate model auto-selection, password URI percent-encoding, `trace list` pagination
- **v0.5.18** (April 2026) — native GitLab MR review, Altimate LLM Gateway provider, MCP env-var interpolation
- **v0.5.16** (March 2026) — ClickHouse driver support, agent loop detection, error classification overhaul
- **v0.5.14** (March 2026) — MongoDB driver support, skill follow-up suggestions
- **v0.5.11** (March 2026) — `altimate-code check` CLI command, data-viz improvements, Codespaces support, session tracing
- **v0.5.7** (March 2026) — impact analysis tool, training import, CI check command, `--max-turns` budget, LM Studio provider
- **v0.5.6** (March 2026) — skill CLI & TUI management, GitHub skill install, Snowflake Cortex provider
- **v0.5.5** (March 2026) — auto-discover MCP servers from VS Code, Cursor, Claude Code, Copilot configs
- **v0.5.3** (March 2026) — bundle skills/dbt-tools in npm binary, marker guard CI
- **v0.5.1** (March 2026) — simplified 3-mode agents (builder/analyst/plan), SQL write access control
- **v0.5.0** (March 2026) — smooth streaming mode, builtin skills, `/configure-claude` and `/configure-codex` commands
- **v0.4.x and earlier** — [See full changelog →](CHANGELOG.md)

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