fix: make all skills warehouse-agnostic with best-practice improvements

[suryaiyer95]

Summary

All 10 remaining skills updated to be warehouse-agnostic and follow community best practices. Research covered dbt community, Reddit, industry blogs, and official documentation.

Cross-cutting changes (all skills):

• Add "Use when" trigger phrasing to every description for LLM auto-activation
• Add dialect/warehouse detection as first workflow step (warehouse_list/dbt_profiles)
• Replace Snowflake-specific defaults with auto-detection

Per-skill improvements:

• query-optimize: Remove Snowflake default, add cross-warehouse optimization guidance
• sql-translate: Add dialect inference from SQL syntax, common migration paths, high-risk areas
• incremental-logic: Fix microbatch claim (cross-platform since dbt v1.9), add per-warehouse strategy references/
• medallion-patterns: Add convention detection via dbt_manifest, resolve naming conflict with model-scaffold
• generate-tests: Add dbt_expectations, dbt_utils, elementary packages with full test catalog in references/
• dbt-docs: Add documentation patterns by layer, doc block guidance
• impact-analysis: Add schema_diff and dbt_lineage tools, breaking change classification
• lineage-diff: Add schema_diff, dbt_manifest, dbt_lineage, modified edge detection
• model-scaffold: Add warehouse-specific materialization defaults, dbt Mesh governance patterns
• yaml-config: Add data_tests v1.8 rename, model contracts, YAML organization patterns

New reference files (progressive disclosure):

• generate-tests/references/test-packages.md — Full catalog of dbt_utils, dbt_expectations, elementary tests
• generate-tests/references/coverage-strategy.md — Per-layer test priority guide
• incremental-logic/references/{snowflake,bigquery,databricks,postgres,redshift}.md — Warehouse-specific incremental strategies

Test plan

• Verify all 10 skills load correctly with updated frontmatter
• Verify references/ files are discoverable when skills are activated
• Spot-check descriptions trigger LLM auto-activation on relevant prompts

🤖 Generated with Claude Code

[sentry]

Bug: The dbt_manifest tool does not return installed package information as claimed in the generate-tests/SKILL.md documentation, preventing the agent from detecting available test packages.
_{Severity: HIGH}

Suggested Fix

Enhance the dbt_manifest tool to parse the package_name property for resources within the manifest.json. Aggregate this data to create a list of installed packages and include it in the tool's return value. Alternatively, update the skill documentation to use a different method for detecting packages, such as reading packages.yml directly.

Prompt for AI Agent

Review the code at the location below. A potential bug has been identified by an AI
agent.
Verify if this is a real issue. If it is, propose a fix; if not, explain why it's not
valid.

Location: .altimate-code/skills/generate-tests/SKILL.md#L18-L22

Potential issue: The documentation in `generate-tests/SKILL.md` instructs the AI agent
to use the `dbt_manifest` tool to identify installed test packages like `dbt_utils`.
However, the tool's implementation in `dbt/manifest.py` does not extract or return this
information. The `parse_manifest()` function's result object lacks any field for
installed packages. This mismatch will cause the agent to fail silently when trying to
follow the documented workflow, as it cannot determine which test packages are
available. This leads to generating suboptimal tests or tests that fail at runtime
because they reference unavailable packages.

[sentry]

Bug: The sql.analyze and sql.optimize tools accept a dialect parameter but their implementations silently ignore it, leading to incorrect, non-dialect-specific analysis and optimizations.
_{Severity: HIGH}

Suggested Fix

Update the sql.analyze and sql.optimize tool implementations in server.py. Pass the dialect from the params_obj to the underlying function calls, such as guard_rewrite_sql(..., dialect=params_obj.dialect) and guard_lint(..., dialect=params_obj.dialect). This will ensure the dialect is used for SQL analysis and rewriting as intended.

Prompt for AI Agent

Review the code at the location below. A potential bug has been identified by an AI
agent.
Verify if this is a real issue. If it is, propose a fix; if not, explain why it's not
valid.

Location: .altimate-code/skills/query-optimize/SKILL.md#L22

Potential issue: The documentation in `query-optimize/SKILL.md` states that the detected
SQL `dialect` should be passed to tools like `sql.analyze` and `sql.optimize`. While
these tools accept a `dialect` parameter in their respective `SqlAnalyzeParams` and
`SqlOptimizeParams` models, the implementation silently ignores it. The underlying
functions (`guard_lint`, `guard_check_semantics`, `guard_rewrite_sql`) are called
without the `dialect` argument. This causes the tools to default to generic or
Snowflake-specific logic, providing incorrect or suboptimal analysis and optimizations
for other database dialects like Postgres or BigQuery.