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Add impact analysis, training import tools, and CI validation features (#350)
* feat: improve product value — onboarding UX, impact analysis, training scaling, CI checks - Fix first-time user experience: show tips for new users (was hidden), add onboarding hint with /connect and /discover guidance - Add prioritized beginner tips for data engineers (12 curated tips shown to new users instead of random selection from 107) - Increase training limits from 20→50 entries/kind and 16KB→48KB budget to support enterprise teams with rich glossaries and standards - Add impact_analysis tool: dbt DAG-aware change assessment showing downstream blast radius, affected tests, and column-level impact - Add training_import tool: bulk import from markdown docs (style guides, glossaries, playbooks) into the training system - Add /ci-check skill template for pre-merge SQL quality validation - Expand /discover to detect cloud warehouse credentials (Snowflake config, BigQuery service accounts, DATABASE_URL, Databricks tokens) - Rewrite quickstart docs with progressive verification steps and feature discovery table https://claude.ai/code/session_01M6rR2wXn4PfMoUASy1qghV * feat: enterprise governance — yolo deny-rule enforcement and --max-turns budget - Fix yolo mode to respect explicit deny rules from session config instead of blindly auto-approving all permissions. Deny rules now block even in yolo mode with a clear "BLOCKED by deny rule" message. - Add --max-turns CLI flag for CI/headless budget enforcement. Aborts the session when the assistant exceeds the configured turn limit, preventing runaway agents from burning API credits indefinitely. Addresses enterprise governance gaps identified by platform eng review: teams of 15+ engineers running CI pipelines need spend controls and safety guarantees that yolo mode won't bypass critical deny rules. https://claude.ai/code/session_01M6rR2wXn4PfMoUASy1qghV * fix: resolve typecheck errors and add missing altimate_change markers - Fix TS2719 in impact-analysis.ts and training-import.ts with @ts-expect-error (tsgo false positive — identical pattern works in other tools) - Fix manifest.tests → manifest.test_count (DbtManifestResult has no tests array) - Fix targetModel.sql → model reference (DbtModelInfo has no sql property) - Add altimate_change markers to tips.tsx, home.tsx, discover.txt Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: resolve all typecheck errors for PR #350 - Add playwright-core devDependency for tracing-viewer test types - Fix implicit any and noUncheckedIndexedAccess in tracing-viewer tests - Fix TS2719 in impact-analysis.ts and training-import.ts (@ts-expect-error) - Fix missing DbtManifestResult.tests and DbtModelInfo.sql properties - Add missing altimate_change markers to tips.tsx, home.tsx, discover.txt Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * fix: update TRAINING_MAX_PATTERNS_PER_KIND test to match new limit of 50 The PR increased the limit from 20 to 50 for enterprise teams, but the test still asserted `toBe(20)`. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude <noreply@anthropic.com>
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‎.github/meta/commit.txt‎

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<<<<<<< Updated upstream
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fix: add missing `altimate_change` markers for `experimental` block in `opencode.jsonc`
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The `experimental` config added in #311 was missing upstream markers,
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causing the Marker Guard CI check to fail on main.
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=======
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fix: add try/catch and input sanitization to TUI install/create (#341)
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Root cause of silent failures: `onConfirm` async callbacks had no
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try/catch, so any thrown error was swallowed and no result toast shown.
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Fixes:
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- Wrap all install/create logic in try/catch with error toast
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- Strip trailing dots from input (textarea was appending `.`)
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- Strip `.git` suffix from URLs (users paste from browser)
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- Trim whitespace and validate before proceeding
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- "Installing..." toast now shows 60s duration with helpful text
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("This may take a moment while the repo is cloned")
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- Empty input shows immediate error instead of proceeding
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>>>>>>> Stashed changes
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Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

‎bun.lock‎

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‎docs/docs/quickstart.md‎

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@@ -69,21 +69,43 @@ Auto-detects your dbt projects, warehouse credentials, and installed tools. See
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---
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## Step 4: Build Your First Artifact
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## Step 4: Verify It Works
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In the TUI, try these prompts or describe your own use case:
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In the TUI, type a simple prompt to confirm everything is connected:
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```
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What SQL anti-patterns does this query have: SELECT * FROM orders o JOIN customers c ON o.id = c.order_id WHERE UPPER(c.name) = 'ACME'
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```
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Look at my snowflake account and do a comprehensive Analysis our Snowflake credit consumption over the last 30 days. After doing this generate a dashboard for my consumption.
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If you connected a warehouse with `/discover`, try:
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```
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Show me the tables in my warehouse
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```
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Build me a real time, interactive dashboard for my macbook system metrics and health. Use python, iceberg, dbt for various time slices.
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If you have a dbt project, try:
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```
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Scan my dbt project and summarize the models
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```
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---
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## Step 5: Explore Data Engineering Features
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Once basics are working, explore these commands:
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| Command | What it does |
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|---------|-------------|
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| `/sql-review` | Review SQL for correctness, performance, and best practices |
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| `/cost-report` | Analyze warehouse spending and find optimization opportunities |
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| `/dbt-docs` | Generate or improve dbt model documentation |
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| `/generate-tests` | Auto-generate dbt tests for your models |
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| `/migrate-sql` | Translate SQL between warehouse dialects |
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| `/ci-check` | Run pre-merge SQL quality validation on changed files |
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| `/train @docs/style-guide.md` | Import team standards from documentation |
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**Pro tip:** Use `impact_analysis` before making breaking changes to understand which downstream dbt models will be affected.
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- [Full Setup](getting-started.md): All warehouse configs, LLM providers, advanced setup
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- [Agent Modes](data-engineering/agent-modes.md): Choose the right agent for your task
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- [CI & Automation](data-engineering/guides/ci-headless.md): Run altimate in automated pipelines
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- Train your AI teammate: Use `/teach` and `/train` to build team-specific knowledge that persists across sessions

‎packages/opencode/src/altimate/tools/impact-analysis.ts‎

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// altimate_change - Impact analysis tool for dbt DAG-aware change assessment
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//
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// Combines dbt manifest parsing with column-level lineage to show downstream
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// impact of model/column changes across the entire DAG.
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import z from "zod"
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import { Tool } from "../../tool/tool"
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import { Dispatcher } from "../native"
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export const ImpactAnalysisTool = Tool.define("impact_analysis", {
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description: [
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"Analyze the downstream impact of a model or column change across the dbt DAG.",
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"Combines dbt manifest parsing with column-level lineage to show all affected",
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"models, tests, exposures, and sources. Use before making breaking changes to",
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"understand blast radius.",
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"",
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"Examples:",
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'- impact_analysis({ model: "stg_orders", change_type: "remove" })',
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'- impact_analysis({ model: "stg_orders", column: "order_id", change_type: "rename" })',
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'- impact_analysis({ manifest_path: "target/manifest.json", model: "dim_customers", change_type: "retype" })',
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].join("\n"),
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parameters: z.object({
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model: z
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.string()
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.describe("dbt model name to analyze impact for (e.g., 'stg_orders', 'dim_customers')"),
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column: z
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.string()
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.optional()
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.describe("Specific column to trace impact for. If omitted, analyzes model-level impact."),
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change_type: z
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.enum(["remove", "rename", "retype", "add", "modify"])
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.describe("Type of change being considered"),
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manifest_path: z
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.string()
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.optional()
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.default("target/manifest.json")
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.describe("Path to dbt manifest.json file"),
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dialect: z
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.string()
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.optional()
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.default("snowflake")
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.describe("SQL dialect for lineage analysis"),
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}),
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// @ts-expect-error tsgo TS2719 false positive — identical pattern works in other tools
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async execute(args, ctx) {
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try {
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// Step 1: Parse the dbt manifest to get the full DAG
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const manifest = await Dispatcher.call("dbt.manifest", { path: args.manifest_path })
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if (!manifest.models || manifest.models.length === 0) {
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return {
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title: "Impact: NO MANIFEST",
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metadata: { success: false },
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output: `No models found in manifest at ${args.manifest_path}. Run \`dbt compile\` first to generate the manifest.`,
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}
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}
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// Step 2: Find the target model and its downstream dependents
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const targetModel = manifest.models.find(
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(m: { name: string }) => m.name === args.model || m.name.endsWith(`.${args.model}`),
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)
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if (!targetModel) {
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const available = manifest.models
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.slice(0, 10)
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.map((m: { name: string }) => m.name)
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.join(", ")
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return {
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title: "Impact: MODEL NOT FOUND",
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metadata: { success: false },
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output: `Model "${args.model}" not found in manifest. Available models: ${available}${manifest.models.length > 10 ? ` (+${manifest.models.length - 10} more)` : ""}`,
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}
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}
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// Step 3: Build the dependency graph and find all downstream models
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const modelsByName = new Map<string, any>()
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for (const m of manifest.models) {
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modelsByName.set(m.name, m)
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}
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// Find all models that depend on the target (direct + transitive)
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const downstream = findDownstream(args.model, manifest.models)
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const direct = downstream.filter((d) => d.depth === 1)
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const transitive = downstream.filter((d) => d.depth > 1)
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// Step 4: Report test count (manifest has test_count but not individual tests)
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const affectedTestCount = manifest.test_count ?? 0
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// Step 5: If column specified, attempt column-level lineage
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let columnImpact: string[] = []
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if (args.column) {
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try {
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const lineageResult = await Dispatcher.call("lineage.check", {
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sql: `SELECT * FROM ${args.model}`, // Use model reference for lineage tracing
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dialect: args.dialect,
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})
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if (lineageResult.data?.column_dict) {
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// Find which downstream columns reference our target column
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for (const [outCol, sources] of Object.entries(lineageResult.data.column_dict)) {
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const srcArray = Array.isArray(sources) ? sources : [sources]
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if (srcArray.some((s: any) => JSON.stringify(s).includes(args.column!))) {
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columnImpact.push(outCol)
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}
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}
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}
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} catch {
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// Column lineage is best-effort — continue without it
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}
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}
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// Step 6: Format the impact report
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const output = formatImpactReport({
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model: args.model,
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column: args.column,
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changeType: args.change_type,
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direct,
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transitive,
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affectedTestCount,
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columnImpact,
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totalModels: manifest.model_count,
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})
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const totalAffected = downstream.length
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const severity =
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totalAffected === 0
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? "SAFE"
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: totalAffected <= 3
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? "LOW"
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: totalAffected <= 10
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? "MEDIUM"
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: "HIGH"
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return {
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title: `Impact: ${severity} — ${totalAffected} downstream model${totalAffected !== 1 ? "s" : ""} affected`,
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metadata: {
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success: true,
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severity,
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direct_count: direct.length,
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transitive_count: transitive.length,
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test_count: affectedTestCount,
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column_impact: columnImpact.length,
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},
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output,
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}
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} catch (e) {
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const msg = e instanceof Error ? e.message : String(e)
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return {
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title: "Impact: ERROR",
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metadata: { success: false },
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output: `Failed to analyze impact: ${msg}\n\nEnsure the dbt manifest exists (run \`dbt compile\`) and the dispatcher is running.`,
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}
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}
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},
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})
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interface DownstreamModel {
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name: string
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depth: number
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materialized?: string
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path: string[]
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}
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function findDownstream(
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targetName: string,
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models: Array<{ name: string; depends_on: string[]; materialized?: string }>,
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): DownstreamModel[] {
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const results: DownstreamModel[] = []
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const visited = new Set<string>()
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function walk(name: string, depth: number, path: string[]) {
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for (const model of models) {
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if (visited.has(model.name)) continue
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const deps = model.depends_on.map((d) => d.split(".").pop())
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if (deps.includes(name)) {
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visited.add(model.name)
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const newPath = [...path, model.name]
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results.push({
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name: model.name,
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depth,
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materialized: model.materialized,
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path: newPath,
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})
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walk(model.name, depth + 1, newPath)
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}
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}
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}
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walk(targetName, 1, [targetName])
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return results
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}
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function formatImpactReport(data: {
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model: string
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column?: string
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changeType: string
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direct: DownstreamModel[]
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transitive: DownstreamModel[]
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affectedTestCount: number
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columnImpact: string[]
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totalModels: number
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}): string {
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const lines: string[] = []
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// Header
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const target = data.column ? `${data.model}.${data.column}` : data.model
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lines.push(`Impact Analysis: ${data.changeType.toUpperCase()} ${target}`)
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lines.push("".padEnd(60, "="))
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const totalAffected = data.direct.length + data.transitive.length
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const pct = data.totalModels > 0 ? ((totalAffected / data.totalModels) * 100).toFixed(1) : "0"
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lines.push(`Blast radius: ${totalAffected}/${data.totalModels} models (${pct}%)`)
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lines.push("")
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// Risk assessment
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if (data.changeType === "remove" && totalAffected > 0) {
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lines.push("WARNING: This is a BREAKING change. All downstream models will fail.")
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lines.push("")
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} else if (data.changeType === "rename" && totalAffected > 0) {
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lines.push("WARNING: Rename requires updating all downstream references.")
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lines.push("")
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} else if (data.changeType === "retype" && totalAffected > 0) {
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lines.push("CAUTION: Type change may cause implicit casts or failures in downstream models.")
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lines.push("")
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}
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// Direct dependents
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if (data.direct.length > 0) {
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lines.push(`Direct Dependents (${data.direct.length})`)
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lines.push("".padEnd(40, "-"))
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for (const d of data.direct) {
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const mat = d.materialized ? ` [${d.materialized}]` : ""
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lines.push(` ${d.name}${mat}`)
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}
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lines.push("")
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}
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// Transitive dependents
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if (data.transitive.length > 0) {
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lines.push(`Transitive Dependents (${data.transitive.length})`)
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lines.push("".padEnd(40, "-"))
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for (const d of data.transitive) {
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const mat = d.materialized ? ` [${d.materialized}]` : ""
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const path = d.path.join(" → ")
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lines.push(` ${d.name}${mat} (via: ${path})`)
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}
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lines.push("")
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}
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// Column impact
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if (data.column && data.columnImpact.length > 0) {
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lines.push(`Affected Output Columns (${data.columnImpact.length})`)
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lines.push("".padEnd(40, "-"))
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for (const col of data.columnImpact) {
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lines.push(` ${col}`)
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}
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lines.push("")
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}
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// Affected tests
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if (data.affectedTestCount > 0) {
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lines.push(`Tests in project: ${data.affectedTestCount}`)
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lines.push("".padEnd(40, "-"))
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lines.push(` Run \`dbt test\` to verify all ${data.affectedTestCount} tests still pass after this change.`)
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lines.push("")
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}
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// No impact
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if (totalAffected === 0) {
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lines.push("No downstream models depend on this. Change is safe to make.")
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}
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// Recommendations
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if (totalAffected > 0) {
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lines.push("Recommended Actions")
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lines.push("".padEnd(40, "-"))
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if (data.changeType === "remove") {
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lines.push("1. Update all downstream models to remove references")
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lines.push("2. Run `dbt test` to verify no broken references")
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lines.push("3. Consider deprecation period before removal")
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} else if (data.changeType === "rename") {
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lines.push("1. Update all downstream SQL references to new name")
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lines.push("2. Run `dbt compile` to verify all models compile")
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lines.push("3. Run `dbt test` to verify correctness")
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} else if (data.changeType === "retype") {
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lines.push("1. Check downstream models for implicit type casts")
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lines.push("2. Verify aggregations and joins still work correctly")
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lines.push("3. Run `dbt test` with data validation")
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} else {
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lines.push("1. Review downstream models for compatibility")
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lines.push("2. Run `dbt compile` and `dbt test`")
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}
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}
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return lines.join("\n")
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}

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