2026-05-16 12:06:34 +02:00
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import { describe, expect, it, vi } from 'vitest';
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import { z } from 'zod';
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import type { SDKMessage } from '@anthropic-ai/claude-agent-sdk';
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test: split cli tests from source tree (#216)
* feat(cli): define full warehouse dialect contract
* test(cli): keep dialect edge tests focused
* fix(cli): stabilize dialect contract foundation
* refactor(connectors): own read-only query preparation
* refactor(connectors): resolve dialects through registry
* refactor(connectors): keep concrete dialect classes internal
* chore(workspace): enforce dialect import boundary
* refactor(cli): resolve relationship dialect at scan boundary
* refactor(cli): use dialect display parsing for entity details
* refactor(cli): use dialect display parsing for warehouse catalog
* refactor(cli): use dialect SQL in relationship workflows
* test(cli): verify solid dialect scan workflow closure
* test: split cli tests from source tree
* refactor(cli): standardize BigQuery scope listing
* feat(sqlite): implement connector scope listing
* test(connectors): cover required table listing
* feat(cli): add warehouse driver registry
* refactor(setup): route scope discovery through driver registry
* refactor(cli): route local query execution through driver registry
* refactor(historic-sql): route dialect support through driver registry
* refactor(cli): test warehouse connections through driver registry
* fix(cli): close driver registry type export gaps
* Improve setup daemon diagnostics
* refactor(setup): centralize rail-prefixed diagnostics + query-history fallback
Extract errorMessage, writePrefixedLines, and flushPrefixedBufferedCommandOutput
into clack.ts so the setup wizard, managed daemons, and embedding/agent steps
share one rail-formatted writer. setup-databases.ts also adds a
"disable query history and retry" option when the schema-context build fails
and query history is the likely culprit, surfaced via a new
failed-query-history-unavailable status.
* fix(cli): carry catalog through the picker so BigQuery/Snowflake/SQL Server scope filters match
The setup picker's KtxTableListEntry was a 2-level { schema, name }, so
qualifiedTableId always wrote db.name into enabled_tables. When BigQuery,
Snowflake, or SQL Server later ran fast ingest, their introspect step filtered
the scope set with scopedTableNames(scope, { catalog: projectId|database, db })
— catalog was non-null on the introspect side but null in the scope refs, so
every entry was rejected, the live-database adapter staged zero table files,
and detect() failed with 'Adapter "live-database" did not recognize fetched
source output'.
Align the picker boundary with the canonical 3-level KtxTableRef:
- Add catalog: string | null to KtxTableListEntry.
- BigQuery/Snowflake/SQL Server listTables populate catalog from the
resolved projectId / database; Postgres/MySQL/ClickHouse/SQLite set null.
- qualifiedTableId emits catalog.schema.name when catalog is non-null
(resolveEnabledTables already accepts the 3-part shape) and
schemasFromEnabledTables now goes through parseDottedTableEntry so it
recovers the schema correctly from both 2-part and 3-part entries.
- Export parseDottedTableEntry from enabled-tables.ts (@internal) for picker
reuse.
Update listTables expectations in all seven connector tests and the setup /
picker test fixtures. Add a picker regression test that covers the
catalog-bearing round-trip (save + refine).
* fix(cli): allow debug telemetry under opt-out env
2026-05-26 08:49:05 +02:00
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import { ClaudeCodeKtxLlmRuntime, mapClaudeCodeStopReason, runClaudeCodeAuthProbe } from '../../../src/context/llm/claude-code-runtime.js';
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2026-05-16 12:06:34 +02:00
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async function* stream(messages: SDKMessage[]): AsyncGenerator<SDKMessage, void> {
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for (const message of messages) {
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yield message;
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}
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}
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function initMessage(overrides: Partial<Extract<SDKMessage, { type: 'system'; subtype: 'init' }>> = {}): Extract<
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SDKMessage,
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{ type: 'system'; subtype: 'init' }
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> {
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return {
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type: 'system',
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subtype: 'init',
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apiKeySource: 'none' as never, // pragma: allowlist secret
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claude_code_version: '0.3.142',
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cwd: '/tmp/project',
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tools: [],
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mcp_servers: [],
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model: 'claude-sonnet-4-6',
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permissionMode: 'dontAsk',
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slash_commands: [],
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output_style: 'default',
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skills: [],
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plugins: [],
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uuid: '00000000-0000-4000-8000-000000000001',
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session_id: 'session-id',
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...overrides,
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};
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}
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function resultMessage(overrides: Partial<Extract<SDKMessage, { type: 'result' }>> = {}): Extract<
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SDKMessage,
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{ type: 'result' }
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> {
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return {
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type: 'result',
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subtype: 'success',
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duration_ms: 1,
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duration_api_ms: 1,
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is_error: false,
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num_turns: 1,
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result: 'ok',
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stop_reason: null,
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total_cost_usd: 0,
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usage: {} as never,
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modelUsage: {},
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permission_denials: [],
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errors: [],
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uuid: '00000000-0000-4000-8000-000000000002',
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session_id: 'session-id',
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...overrides,
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} as Extract<SDKMessage, { type: 'result' }>;
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}
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describe('ClaudeCodeKtxLlmRuntime', () => {
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it('passes isolation options and scrubbed env to text generation', async () => {
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const query = vi.fn((_input: any) => stream([initMessage(), resultMessage({ result: 'hello' })]));
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const runtime = new ClaudeCodeKtxLlmRuntime({
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projectDir: '/tmp/project',
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modelSlots: { default: 'sonnet' },
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query,
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env: { ANTHROPIC_API_KEY: 'sk-ant-test', PATH: '/usr/bin' }, // pragma: allowlist secret
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});
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await expect(runtime.generateText({ role: 'default', prompt: 'say hello' })).resolves.toBe('hello');
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expect(query).toHaveBeenCalledWith({
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prompt: 'say hello',
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options: expect.objectContaining({
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cwd: '/tmp/project',
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model: 'claude-sonnet-4-6',
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maxTurns: 1,
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settingSources: [],
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skills: [],
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plugins: [],
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tools: [],
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2026-05-18 13:38:06 +02:00
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managedSettings: {
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allowManagedMcpServersOnly: true,
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allowedMcpServers: [],
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},
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strictMcpConfig: true,
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2026-05-16 12:06:34 +02:00
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allowedTools: [],
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permissionMode: 'dontAsk',
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persistSession: false,
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env: expect.not.objectContaining({ ANTHROPIC_API_KEY: 'sk-ant-test' }),
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}),
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});
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});
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fix(snowflake): unblock multi-schema ingest and relationship discovery (#204)
* feat(setup): drop redundant Snowflake schema prompt; fall back to free-text on listSchemas failure
Snowflake setup previously asked for a single schema as free text, then
ran a multiselect against the discovered schemas — two schema questions
back-to-back, with the first being only a session bootstrap. The SDK's
`schema` is optional, so the bootstrap step is unnecessary.
- Remove the free-text Snowflake schema prompt; only pass `schema` to
snowflake-sdk when one is configured.
- When `listSchemas()` fails (e.g. role lacks SHOW SCHEMAS), prompt the
user for a comma-separated list, persist it as `schema_names`, and use
it as both the table-list filter and the multiselect default. Applies
to every driver with a scope-discovery spec, not just Snowflake.
- Update docs to lead with `schema_names`; keep `schema_name` as a
documented single-schema shorthand.
* fix(snowflake): keep introspecting when primary-key discovery is denied
The PK query joins INFORMATION_SCHEMA.TABLE_CONSTRAINTS and
INFORMATION_SCHEMA.KEY_COLUMN_USAGE, which require grants the
connection role may not have. Previously a 'SQL compilation error:
Object ANALYTICS.INFORMATION_SCHEMA.KEY_COLUMN_USAGE does not exist
or not authorized' aborted the entire introspect — schemas, columns,
and row counts were all discarded over a missing nice-to-have.
Wrap the constraint query in try/catch, log a one-line warning per
schema, and return an empty PK map. Columns end up with
primaryKey=false; relationship inference still has FK and profiling
to fall back on.
* fix(scan): unblock relationship discovery on Snowflake
Two adjacent bugs prevented the scan's relationship pipeline from producing
any joins on a Snowflake warehouse:
- relationship-profiling.ts fell through to a default `GROUP_CONCAT` branch
for unknown drivers. Snowflake has no GROUP_CONCAT, so every per-table
profile query failed with "Unknown function GROUP_CONCAT". Add an explicit
Snowflake branch that uses LISTAGG with a literal '\x1f' delimiter
(Snowflake requires the delimiter to be a constant, so CHR(31) is rejected).
- description-generation.ts destructured `connector.sampleTable` and
`connector.sampleColumn` into bare locals, losing the `this` binding when
the class-method connectors (Snowflake, Postgres, MySQL) were invoked.
Every sample call threw "Cannot read properties of undefined (reading
'assertConnection')" and degraded LLM descriptions to metadata-only
prompts. Call the methods through the connector instead.
Without these, even after the primary-key probe is allowed to fail softly,
the scan ends up with 0 validated relationships and an empty `joins:` block
in every shard YAML.
* test(scan): cover table-ref helpers
* feat(scan): plumb tableScope through live-database introspection port
* feat(scan): apply tableScope during metadata fetch
* feat(scan): enforce table scope at fetch boundary
* feat(scan): pool Snowflake sessions and batch enrichment for faster ingest (#206)
* feat(cli): add RSA key-pair auth option to Snowflake setup wizard
Extends the interactive Snowflake setup flow with an authentication-method
prompt (password vs RSA/JWT key-pair). The RSA branch collects a private-key
path (env/file/absolute) and an optional passphrase; the resulting connection
config records `authMethod: 'rsa'` with `privateKey` and `passphrase` instead
of `password`.
* feat(scan): pool Snowflake sessions
* fix(scan): reuse structural snapshots and cleanup connectors
* feat(scan): parallelize relationship profiling
* feat(scan): batch table description generation
* docs: document Snowflake ingest concurrency knobs
* fix(scan): close Snowflake ingest perf verification gaps
* fix(scan): keep batched description failure bounded
* feat(scan): dispatch query-history probes by connection driver
Extract historic-sql dialect resolution into a shared helper so the
status-project readiness check and the local ingest factory agree on
which connections enable query history and which probe to run. The
status command now picks the postgres/snowflake/bigquery probe based on
the connection's driver instead of always reporting against postgres,
which previously caused snowflake connections with queryHistory.enabled
to surface a misleading "driver is snowflake" failure.
Also drops a noisy console.warn from Snowflake primary-key discovery —
INFORMATION_SCHEMA.KEY_COLUMN_USAGE is commonly ungranted for read-only
roles and the FK + profiling paths handle the empty PK map already.
* fix(llm): allow StructuredOutput tool and raise maxTurns for generateObject
The Claude Code agent SDK announces an internal pseudo-tool named
StructuredOutput in the system/init message whenever outputFormat is set
to { type: 'json_schema' }. The runtime's isolation check built its
allowedToolIds set only from MCP tool ids and treated StructuredOutput
as an unexpected host-injected tool, so every generateObject call threw
"Claude Code runtime isolation failed: tools=StructuredOutput ..." and
the table-descriptions and relationship-LLM-proposal enrichment stages
recorded null output across the board.
Whitelist StructuredOutput specifically in generateObject's
allowedToolIds — the check also enforces missing_tools symmetry, so
generateText and runAgentLoop, which do not see StructuredOutput, must
not require it.
generateObject also ran with maxTurns: 1, which the model intermittently
breached when it emitted thinking text before the structured response.
Raised to 5 to give the schema-bound call enough headroom without
allowing unbounded loops. The existing tests now exercise the path with
an init message that announces StructuredOutput so the regression cannot
slip back in.
* chore(scripts): add ktx-reset.sh project-cleanup helper
Convenience script for repeatable ingest testing: takes a project
directory and prunes everything except ktx.yaml and .ktx/secrets/, so
the next ktx setup or ktx ingest run starts from a known-clean state.
2026-05-23 10:41:30 +02:00
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it('validates structured output with the caller schema and whitelists the SDK StructuredOutput tool', async () => {
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2026-05-16 12:06:34 +02:00
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const schema = z.object({ answer: z.string() });
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fix(snowflake): unblock multi-schema ingest and relationship discovery (#204)
* feat(setup): drop redundant Snowflake schema prompt; fall back to free-text on listSchemas failure
Snowflake setup previously asked for a single schema as free text, then
ran a multiselect against the discovered schemas — two schema questions
back-to-back, with the first being only a session bootstrap. The SDK's
`schema` is optional, so the bootstrap step is unnecessary.
- Remove the free-text Snowflake schema prompt; only pass `schema` to
snowflake-sdk when one is configured.
- When `listSchemas()` fails (e.g. role lacks SHOW SCHEMAS), prompt the
user for a comma-separated list, persist it as `schema_names`, and use
it as both the table-list filter and the multiselect default. Applies
to every driver with a scope-discovery spec, not just Snowflake.
- Update docs to lead with `schema_names`; keep `schema_name` as a
documented single-schema shorthand.
* fix(snowflake): keep introspecting when primary-key discovery is denied
The PK query joins INFORMATION_SCHEMA.TABLE_CONSTRAINTS and
INFORMATION_SCHEMA.KEY_COLUMN_USAGE, which require grants the
connection role may not have. Previously a 'SQL compilation error:
Object ANALYTICS.INFORMATION_SCHEMA.KEY_COLUMN_USAGE does not exist
or not authorized' aborted the entire introspect — schemas, columns,
and row counts were all discarded over a missing nice-to-have.
Wrap the constraint query in try/catch, log a one-line warning per
schema, and return an empty PK map. Columns end up with
primaryKey=false; relationship inference still has FK and profiling
to fall back on.
* fix(scan): unblock relationship discovery on Snowflake
Two adjacent bugs prevented the scan's relationship pipeline from producing
any joins on a Snowflake warehouse:
- relationship-profiling.ts fell through to a default `GROUP_CONCAT` branch
for unknown drivers. Snowflake has no GROUP_CONCAT, so every per-table
profile query failed with "Unknown function GROUP_CONCAT". Add an explicit
Snowflake branch that uses LISTAGG with a literal '\x1f' delimiter
(Snowflake requires the delimiter to be a constant, so CHR(31) is rejected).
- description-generation.ts destructured `connector.sampleTable` and
`connector.sampleColumn` into bare locals, losing the `this` binding when
the class-method connectors (Snowflake, Postgres, MySQL) were invoked.
Every sample call threw "Cannot read properties of undefined (reading
'assertConnection')" and degraded LLM descriptions to metadata-only
prompts. Call the methods through the connector instead.
Without these, even after the primary-key probe is allowed to fail softly,
the scan ends up with 0 validated relationships and an empty `joins:` block
in every shard YAML.
* test(scan): cover table-ref helpers
* feat(scan): plumb tableScope through live-database introspection port
* feat(scan): apply tableScope during metadata fetch
* feat(scan): enforce table scope at fetch boundary
* feat(scan): pool Snowflake sessions and batch enrichment for faster ingest (#206)
* feat(cli): add RSA key-pair auth option to Snowflake setup wizard
Extends the interactive Snowflake setup flow with an authentication-method
prompt (password vs RSA/JWT key-pair). The RSA branch collects a private-key
path (env/file/absolute) and an optional passphrase; the resulting connection
config records `authMethod: 'rsa'` with `privateKey` and `passphrase` instead
of `password`.
* feat(scan): pool Snowflake sessions
* fix(scan): reuse structural snapshots and cleanup connectors
* feat(scan): parallelize relationship profiling
* feat(scan): batch table description generation
* docs: document Snowflake ingest concurrency knobs
* fix(scan): close Snowflake ingest perf verification gaps
* fix(scan): keep batched description failure bounded
* feat(scan): dispatch query-history probes by connection driver
Extract historic-sql dialect resolution into a shared helper so the
status-project readiness check and the local ingest factory agree on
which connections enable query history and which probe to run. The
status command now picks the postgres/snowflake/bigquery probe based on
the connection's driver instead of always reporting against postgres,
which previously caused snowflake connections with queryHistory.enabled
to surface a misleading "driver is snowflake" failure.
Also drops a noisy console.warn from Snowflake primary-key discovery —
INFORMATION_SCHEMA.KEY_COLUMN_USAGE is commonly ungranted for read-only
roles and the FK + profiling paths handle the empty PK map already.
* fix(llm): allow StructuredOutput tool and raise maxTurns for generateObject
The Claude Code agent SDK announces an internal pseudo-tool named
StructuredOutput in the system/init message whenever outputFormat is set
to { type: 'json_schema' }. The runtime's isolation check built its
allowedToolIds set only from MCP tool ids and treated StructuredOutput
as an unexpected host-injected tool, so every generateObject call threw
"Claude Code runtime isolation failed: tools=StructuredOutput ..." and
the table-descriptions and relationship-LLM-proposal enrichment stages
recorded null output across the board.
Whitelist StructuredOutput specifically in generateObject's
allowedToolIds — the check also enforces missing_tools symmetry, so
generateText and runAgentLoop, which do not see StructuredOutput, must
not require it.
generateObject also ran with maxTurns: 1, which the model intermittently
breached when it emitted thinking text before the structured response.
Raised to 5 to give the schema-bound call enough headroom without
allowing unbounded loops. The existing tests now exercise the path with
an init message that announces StructuredOutput so the regression cannot
slip back in.
* chore(scripts): add ktx-reset.sh project-cleanup helper
Convenience script for repeatable ingest testing: takes a project
directory and prunes everything except ktx.yaml and .ktx/secrets/, so
the next ktx setup or ktx ingest run starts from a known-clean state.
2026-05-23 10:41:30 +02:00
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const query = vi.fn((_input: any) =>
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stream([
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initMessage({ tools: ['StructuredOutput'] }),
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resultMessage({ structured_output: { answer: 'yes' } }),
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]),
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);
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2026-05-16 12:06:34 +02:00
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const runtime = new ClaudeCodeKtxLlmRuntime({
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projectDir: '/tmp/project',
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modelSlots: { default: 'sonnet' },
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query,
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env: {},
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});
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await expect(runtime.generateObject({ role: 'default', prompt: 'json', schema })).resolves.toEqual({ answer: 'yes' });
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expect(query.mock.calls[0][0].options.outputFormat).toMatchObject({
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type: 'json_schema',
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schema: expect.objectContaining({ type: 'object' }),
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});
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});
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it('registers only exact KTX MCP tool ids and denies non-KTX tools', async () => {
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const query = vi.fn((_input: any) =>
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stream([
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initMessage({ tools: ['mcp__ktx__load_skill'], mcp_servers: [{ name: 'ktx', status: 'connected' }] }),
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{
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type: 'assistant',
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message: { role: 'assistant', content: [] },
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parent_tool_use_id: null,
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uuid: '00000000-0000-4000-8000-000000000003',
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session_id: 'session-id',
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} as unknown as SDKMessage,
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resultMessage({ subtype: 'error_max_turns', is_error: true }),
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]),
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);
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const runtime = new ClaudeCodeKtxLlmRuntime({
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projectDir: '/tmp/project',
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modelSlots: { default: 'sonnet' },
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query,
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env: {},
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});
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const onStepFinish = vi.fn();
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await runtime.runAgentLoop({
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modelRole: 'default',
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systemPrompt: 'system',
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userPrompt: 'user',
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toolSet: {
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load_skill: {
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name: 'load_skill',
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description: 'Load skill.',
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inputSchema: z.object({ name: z.string() }),
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execute: async () => ({ markdown: 'loaded' }),
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},
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},
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stepBudget: 1,
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telemetryTags: { operationName: 'test' },
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onStepFinish,
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});
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const options = query.mock.calls[0][0].options;
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expect(options.allowedTools).toEqual(['mcp__ktx__load_skill']);
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2026-05-18 13:38:06 +02:00
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expect(options.managedSettings).toEqual({
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allowManagedMcpServersOnly: true,
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allowedMcpServers: [{ serverName: 'ktx' }],
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|
|
});
|
|
|
|
|
expect(options.strictMcpConfig).toBe(true);
|
2026-05-16 12:06:34 +02:00
|
|
|
expect(await options.canUseTool('mcp__ktx__load_skill', {}, { signal: new AbortController().signal, toolUseID: '1' })).toEqual({
|
|
|
|
|
behavior: 'allow',
|
|
|
|
|
toolUseID: '1',
|
|
|
|
|
});
|
|
|
|
|
expect(await options.canUseTool('Bash', {}, { signal: new AbortController().signal, toolUseID: '2' })).toMatchObject({
|
|
|
|
|
behavior: 'deny',
|
|
|
|
|
toolUseID: '2',
|
|
|
|
|
});
|
|
|
|
|
expect(onStepFinish).toHaveBeenCalledWith({ stepIndex: 1, stepBudget: 1 });
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('treats host-discovered commands skills and agents as non-fatal init metadata for text and auth probe', async () => {
|
|
|
|
|
const hostDiscoveredInit = initMessage({
|
|
|
|
|
slash_commands: ['/help', '/compact', '/clear', '/user-command'],
|
|
|
|
|
skills: ['pdf', 'docx'],
|
|
|
|
|
agents: ['claude', 'Explore', 'general-purpose'],
|
|
|
|
|
});
|
|
|
|
|
const textQuery = vi.fn((_input: any) => stream([hostDiscoveredInit, resultMessage({ result: 'hello' })]));
|
|
|
|
|
const runtime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query: textQuery,
|
|
|
|
|
env: { ANTHROPIC_API_KEY: 'sk-ant-test', PATH: '/usr/bin' }, // pragma: allowlist secret
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(runtime.generateText({ role: 'default', prompt: 'say hello' })).resolves.toBe('hello');
|
|
|
|
|
const textOptions = textQuery.mock.calls[0][0].options;
|
|
|
|
|
expect(textOptions).toMatchObject({
|
|
|
|
|
settingSources: [],
|
|
|
|
|
skills: [],
|
|
|
|
|
plugins: [],
|
|
|
|
|
tools: [],
|
2026-05-18 13:38:06 +02:00
|
|
|
managedSettings: {
|
|
|
|
|
allowManagedMcpServersOnly: true,
|
|
|
|
|
allowedMcpServers: [],
|
|
|
|
|
},
|
|
|
|
|
strictMcpConfig: true,
|
2026-05-16 12:06:34 +02:00
|
|
|
allowedTools: [],
|
|
|
|
|
permissionMode: 'dontAsk',
|
|
|
|
|
persistSession: false,
|
|
|
|
|
env: expect.not.objectContaining({ ANTHROPIC_API_KEY: 'sk-ant-test' }),
|
|
|
|
|
});
|
|
|
|
|
expect(textOptions.disallowedTools).toEqual(expect.arrayContaining(['Agent', 'Task', 'Bash']));
|
|
|
|
|
expect(await textOptions.canUseTool('Agent', {}, { signal: new AbortController().signal, toolUseID: 'agent' })).toMatchObject({
|
|
|
|
|
behavior: 'deny',
|
|
|
|
|
toolUseID: 'agent',
|
|
|
|
|
});
|
|
|
|
|
expect(await textOptions.canUseTool('Skill', {}, { signal: new AbortController().signal, toolUseID: 'skill' })).toMatchObject({
|
|
|
|
|
behavior: 'deny',
|
|
|
|
|
toolUseID: 'skill',
|
|
|
|
|
});
|
|
|
|
|
expect(
|
|
|
|
|
await textOptions.canUseTool('SlashCommand', {}, { signal: new AbortController().signal, toolUseID: 'slash' }),
|
|
|
|
|
).toMatchObject({
|
|
|
|
|
behavior: 'deny',
|
|
|
|
|
toolUseID: 'slash',
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
const probeQuery = vi.fn((_input: any) => stream([hostDiscoveredInit, resultMessage({ result: 'ok' })]));
|
|
|
|
|
await expect(
|
|
|
|
|
runClaudeCodeAuthProbe({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
model: 'sonnet',
|
|
|
|
|
query: probeQuery,
|
|
|
|
|
env: { ANTHROPIC_AUTH_TOKEN: 'token', HOME: '/Users/test' },
|
|
|
|
|
}),
|
|
|
|
|
).resolves.toEqual({ ok: true });
|
|
|
|
|
expect(probeQuery.mock.calls[0][0].options).toMatchObject({
|
|
|
|
|
settingSources: [],
|
|
|
|
|
skills: [],
|
|
|
|
|
plugins: [],
|
|
|
|
|
tools: [],
|
|
|
|
|
allowedTools: [],
|
|
|
|
|
permissionMode: 'dontAsk',
|
|
|
|
|
persistSession: false,
|
|
|
|
|
env: expect.objectContaining({ HOME: '/Users/test' }),
|
|
|
|
|
});
|
|
|
|
|
expect(probeQuery.mock.calls[0][0].options.env).not.toEqual(
|
|
|
|
|
expect.objectContaining({ ANTHROPIC_AUTH_TOKEN: 'token' }),
|
|
|
|
|
);
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('allows host-discovered context during agent loops while requiring exact KTX MCP tools and servers', async () => {
|
|
|
|
|
const query = vi.fn((_input: any) =>
|
|
|
|
|
stream([
|
|
|
|
|
initMessage({
|
|
|
|
|
tools: ['mcp__ktx__load_skill'],
|
|
|
|
|
mcp_servers: [{ name: 'ktx', status: 'connected' }],
|
|
|
|
|
slash_commands: ['/help', '/compact', '/clear'],
|
|
|
|
|
skills: ['memory-agent', 'doc-reader'],
|
|
|
|
|
agents: ['claude', 'Plan', 'Explore'],
|
|
|
|
|
}),
|
|
|
|
|
{
|
|
|
|
|
type: 'assistant',
|
|
|
|
|
message: { role: 'assistant', content: [] },
|
|
|
|
|
parent_tool_use_id: null,
|
|
|
|
|
uuid: '00000000-0000-4000-8000-000000000006',
|
|
|
|
|
session_id: 'session-id',
|
|
|
|
|
} as unknown as SDKMessage,
|
|
|
|
|
resultMessage({ subtype: 'error_max_turns', is_error: true }),
|
|
|
|
|
]),
|
|
|
|
|
);
|
|
|
|
|
const runtime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query,
|
|
|
|
|
env: {},
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
runtime.runAgentLoop({
|
|
|
|
|
modelRole: 'default',
|
|
|
|
|
systemPrompt: 'system',
|
|
|
|
|
userPrompt: 'user',
|
|
|
|
|
toolSet: {
|
|
|
|
|
load_skill: {
|
|
|
|
|
name: 'load_skill',
|
|
|
|
|
description: 'Load skill.',
|
|
|
|
|
inputSchema: z.object({ name: z.string() }),
|
|
|
|
|
execute: async () => ({ markdown: 'loaded' }),
|
|
|
|
|
},
|
|
|
|
|
},
|
|
|
|
|
stepBudget: 1,
|
|
|
|
|
telemetryTags: { operationName: 'test' },
|
|
|
|
|
}),
|
feat(cli): profile ingest runs and split model vs tool time (#249)
* feat(cli): profile ingest runs to find where wall-clock time goes
Add opt-in profiling for `ktx ingest`. Each timed phase, work unit, and
agent loop now records durationMs / step count / token usage in the
trace, and a post-run aggregator rolls them up into a "where did the
time go" report printed to stderr.
Enable per run with KTX_PROFILE_INGEST (1/true -> human table, json ->
raw structured profile) or persistently via `ingest.profile` in
ktx.yaml. The json form emits raw milliseconds, token counts, and a
summary.headline one-line diagnosis so coding agents can parse it
directly; json wins when both env and config request profiling.
- runtime-port: RunLoopMetrics (totalMs, usage, stepCount,
stepBoundariesMs) plus onMetrics callbacks on text/object generation
- ai-sdk + claude-code runtimes: capture per-loop timing and token usage
- work-unit-executor and stages 3/4: thread metrics into trace events
- ingest-bundle.runner: time worktree / triage / clustering / index /
reconcile / squash phases and emit the profile in a finally block
(best-effort; never affects the run outcome)
- ingest-profile: new trace+transcript aggregator with table/json formatters
- config: ingest.profile flag; docs: profiling section in ktx-ingest.mdx
* fix(cli): flush tool-call logs before reading ingest profile
Tool transcripts are appended fire-and-forget so the agent hot path never
blocks on logging. The ingest profiler read them before the writes settled,
so per-work-unit toolMs (and the model-vs-tool split derived from it) could
be incomplete. Track in-flight appends and expose flushToolCallLogs() —
bounded by a timeout so it can never hang — and flush before the profiler
reads the transcript.
2026-06-01 15:49:17 +02:00
|
|
|
).resolves.toMatchObject({ stopReason: 'budget' });
|
2026-05-16 12:06:34 +02:00
|
|
|
|
|
|
|
|
const options = query.mock.calls[0][0].options;
|
|
|
|
|
expect(options.allowedTools).toEqual(['mcp__ktx__load_skill']);
|
2026-05-18 13:38:06 +02:00
|
|
|
expect(options.managedSettings).toEqual({
|
|
|
|
|
allowManagedMcpServersOnly: true,
|
|
|
|
|
allowedMcpServers: [{ serverName: 'ktx' }],
|
|
|
|
|
});
|
|
|
|
|
expect(options.strictMcpConfig).toBe(true);
|
2026-05-16 12:06:34 +02:00
|
|
|
expect(await options.canUseTool('mcp__ktx__load_skill', {}, { signal: new AbortController().signal, toolUseID: '1' })).toEqual({
|
|
|
|
|
behavior: 'allow',
|
|
|
|
|
toolUseID: '1',
|
|
|
|
|
});
|
|
|
|
|
expect(await options.canUseTool('Task', {}, { signal: new AbortController().signal, toolUseID: '2' })).toMatchObject({
|
|
|
|
|
behavior: 'deny',
|
|
|
|
|
toolUseID: '2',
|
|
|
|
|
});
|
|
|
|
|
expect(await options.canUseTool('Skill', {}, { signal: new AbortController().signal, toolUseID: '3' })).toMatchObject({
|
|
|
|
|
behavior: 'deny',
|
|
|
|
|
toolUseID: '3',
|
|
|
|
|
});
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('still rejects unexpected tools, missing KTX tools, plugins, and non-KTX MCP servers from init messages', async () => {
|
|
|
|
|
const query = vi.fn((_input: any) =>
|
|
|
|
|
stream([
|
|
|
|
|
initMessage({
|
|
|
|
|
tools: ['Bash'],
|
|
|
|
|
mcp_servers: [{ name: 'filesystem', status: 'connected' }],
|
|
|
|
|
plugins: [{ name: 'host-plugin', path: '/tmp/plugin' }],
|
|
|
|
|
}),
|
|
|
|
|
resultMessage({ result: 'hello' }),
|
|
|
|
|
]),
|
|
|
|
|
);
|
|
|
|
|
const runtime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query,
|
|
|
|
|
env: {},
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
runtime.generateText({
|
|
|
|
|
role: 'default',
|
|
|
|
|
prompt: 'say hello',
|
|
|
|
|
tools: {
|
|
|
|
|
load_skill: {
|
|
|
|
|
name: 'load_skill',
|
|
|
|
|
description: 'Load skill.',
|
|
|
|
|
inputSchema: z.object({ name: z.string() }),
|
|
|
|
|
execute: async () => ({ markdown: 'loaded' }),
|
|
|
|
|
},
|
|
|
|
|
},
|
|
|
|
|
}),
|
|
|
|
|
).rejects.toThrow(
|
|
|
|
|
/Claude Code runtime isolation failed: .*tools=Bash.*missing_tools=mcp__ktx__load_skill.*mcp_servers=filesystem.*plugins=host-plugin/,
|
|
|
|
|
);
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('passes scrubbed env to object generation and agent loops', async () => {
|
|
|
|
|
const schema = z.object({ answer: z.string() });
|
|
|
|
|
const objectQuery = vi.fn((_input: any) =>
|
fix(snowflake): unblock multi-schema ingest and relationship discovery (#204)
* feat(setup): drop redundant Snowflake schema prompt; fall back to free-text on listSchemas failure
Snowflake setup previously asked for a single schema as free text, then
ran a multiselect against the discovered schemas — two schema questions
back-to-back, with the first being only a session bootstrap. The SDK's
`schema` is optional, so the bootstrap step is unnecessary.
- Remove the free-text Snowflake schema prompt; only pass `schema` to
snowflake-sdk when one is configured.
- When `listSchemas()` fails (e.g. role lacks SHOW SCHEMAS), prompt the
user for a comma-separated list, persist it as `schema_names`, and use
it as both the table-list filter and the multiselect default. Applies
to every driver with a scope-discovery spec, not just Snowflake.
- Update docs to lead with `schema_names`; keep `schema_name` as a
documented single-schema shorthand.
* fix(snowflake): keep introspecting when primary-key discovery is denied
The PK query joins INFORMATION_SCHEMA.TABLE_CONSTRAINTS and
INFORMATION_SCHEMA.KEY_COLUMN_USAGE, which require grants the
connection role may not have. Previously a 'SQL compilation error:
Object ANALYTICS.INFORMATION_SCHEMA.KEY_COLUMN_USAGE does not exist
or not authorized' aborted the entire introspect — schemas, columns,
and row counts were all discarded over a missing nice-to-have.
Wrap the constraint query in try/catch, log a one-line warning per
schema, and return an empty PK map. Columns end up with
primaryKey=false; relationship inference still has FK and profiling
to fall back on.
* fix(scan): unblock relationship discovery on Snowflake
Two adjacent bugs prevented the scan's relationship pipeline from producing
any joins on a Snowflake warehouse:
- relationship-profiling.ts fell through to a default `GROUP_CONCAT` branch
for unknown drivers. Snowflake has no GROUP_CONCAT, so every per-table
profile query failed with "Unknown function GROUP_CONCAT". Add an explicit
Snowflake branch that uses LISTAGG with a literal '\x1f' delimiter
(Snowflake requires the delimiter to be a constant, so CHR(31) is rejected).
- description-generation.ts destructured `connector.sampleTable` and
`connector.sampleColumn` into bare locals, losing the `this` binding when
the class-method connectors (Snowflake, Postgres, MySQL) were invoked.
Every sample call threw "Cannot read properties of undefined (reading
'assertConnection')" and degraded LLM descriptions to metadata-only
prompts. Call the methods through the connector instead.
Without these, even after the primary-key probe is allowed to fail softly,
the scan ends up with 0 validated relationships and an empty `joins:` block
in every shard YAML.
* test(scan): cover table-ref helpers
* feat(scan): plumb tableScope through live-database introspection port
* feat(scan): apply tableScope during metadata fetch
* feat(scan): enforce table scope at fetch boundary
* feat(scan): pool Snowflake sessions and batch enrichment for faster ingest (#206)
* feat(cli): add RSA key-pair auth option to Snowflake setup wizard
Extends the interactive Snowflake setup flow with an authentication-method
prompt (password vs RSA/JWT key-pair). The RSA branch collects a private-key
path (env/file/absolute) and an optional passphrase; the resulting connection
config records `authMethod: 'rsa'` with `privateKey` and `passphrase` instead
of `password`.
* feat(scan): pool Snowflake sessions
* fix(scan): reuse structural snapshots and cleanup connectors
* feat(scan): parallelize relationship profiling
* feat(scan): batch table description generation
* docs: document Snowflake ingest concurrency knobs
* fix(scan): close Snowflake ingest perf verification gaps
* fix(scan): keep batched description failure bounded
* feat(scan): dispatch query-history probes by connection driver
Extract historic-sql dialect resolution into a shared helper so the
status-project readiness check and the local ingest factory agree on
which connections enable query history and which probe to run. The
status command now picks the postgres/snowflake/bigquery probe based on
the connection's driver instead of always reporting against postgres,
which previously caused snowflake connections with queryHistory.enabled
to surface a misleading "driver is snowflake" failure.
Also drops a noisy console.warn from Snowflake primary-key discovery —
INFORMATION_SCHEMA.KEY_COLUMN_USAGE is commonly ungranted for read-only
roles and the FK + profiling paths handle the empty PK map already.
* fix(llm): allow StructuredOutput tool and raise maxTurns for generateObject
The Claude Code agent SDK announces an internal pseudo-tool named
StructuredOutput in the system/init message whenever outputFormat is set
to { type: 'json_schema' }. The runtime's isolation check built its
allowedToolIds set only from MCP tool ids and treated StructuredOutput
as an unexpected host-injected tool, so every generateObject call threw
"Claude Code runtime isolation failed: tools=StructuredOutput ..." and
the table-descriptions and relationship-LLM-proposal enrichment stages
recorded null output across the board.
Whitelist StructuredOutput specifically in generateObject's
allowedToolIds — the check also enforces missing_tools symmetry, so
generateText and runAgentLoop, which do not see StructuredOutput, must
not require it.
generateObject also ran with maxTurns: 1, which the model intermittently
breached when it emitted thinking text before the structured response.
Raised to 5 to give the schema-bound call enough headroom without
allowing unbounded loops. The existing tests now exercise the path with
an init message that announces StructuredOutput so the regression cannot
slip back in.
* chore(scripts): add ktx-reset.sh project-cleanup helper
Convenience script for repeatable ingest testing: takes a project
directory and prunes everything except ktx.yaml and .ktx/secrets/, so
the next ktx setup or ktx ingest run starts from a known-clean state.
2026-05-23 10:41:30 +02:00
|
|
|
stream([
|
|
|
|
|
initMessage({ tools: ['StructuredOutput'] }),
|
|
|
|
|
resultMessage({ structured_output: { answer: 'yes' } }),
|
|
|
|
|
]),
|
2026-05-16 12:06:34 +02:00
|
|
|
);
|
|
|
|
|
const objectRuntime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query: objectQuery,
|
|
|
|
|
env: { ANTHROPIC_API_KEY: 'sk-ant-test', AWS_PROFILE: 'prod', PATH: '/usr/bin' }, // pragma: allowlist secret
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(objectRuntime.generateObject({ role: 'default', prompt: 'json', schema })).resolves.toEqual({
|
|
|
|
|
answer: 'yes',
|
|
|
|
|
});
|
|
|
|
|
expect(objectQuery.mock.calls[0][0].options.env).toEqual(expect.objectContaining({ PATH: '/usr/bin' }));
|
2026-05-18 13:38:06 +02:00
|
|
|
expect(objectQuery.mock.calls[0][0].options.managedSettings).toEqual({
|
|
|
|
|
allowManagedMcpServersOnly: true,
|
|
|
|
|
allowedMcpServers: [],
|
|
|
|
|
});
|
2026-05-16 12:06:34 +02:00
|
|
|
expect(objectQuery.mock.calls[0][0].options.env).not.toEqual(
|
|
|
|
|
expect.objectContaining({ ANTHROPIC_API_KEY: 'sk-ant-test', AWS_PROFILE: 'prod' }), // pragma: allowlist secret
|
|
|
|
|
);
|
|
|
|
|
|
|
|
|
|
const agentQuery = vi.fn((_input: any) =>
|
|
|
|
|
stream([
|
|
|
|
|
initMessage({ tools: ['mcp__ktx__load_skill'], mcp_servers: [{ name: 'ktx', status: 'connected' }] }),
|
|
|
|
|
{
|
|
|
|
|
type: 'assistant',
|
|
|
|
|
message: { role: 'assistant', content: [] },
|
|
|
|
|
parent_tool_use_id: null,
|
|
|
|
|
uuid: '00000000-0000-4000-8000-000000000004',
|
|
|
|
|
session_id: 'session-id',
|
|
|
|
|
} as unknown as SDKMessage,
|
|
|
|
|
resultMessage({ subtype: 'error_max_turns', is_error: true }),
|
|
|
|
|
]),
|
|
|
|
|
);
|
|
|
|
|
const agentRuntime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query: agentQuery,
|
|
|
|
|
env: { ANTHROPIC_AUTH_TOKEN: 'token', CLAUDE_CODE_USE_VERTEX: '1', HOME: '/Users/test' },
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await agentRuntime.runAgentLoop({
|
|
|
|
|
modelRole: 'default',
|
|
|
|
|
systemPrompt: 'system',
|
|
|
|
|
userPrompt: 'user',
|
|
|
|
|
toolSet: {
|
|
|
|
|
load_skill: {
|
|
|
|
|
name: 'load_skill',
|
|
|
|
|
description: 'Load skill.',
|
|
|
|
|
inputSchema: z.object({ name: z.string() }),
|
|
|
|
|
execute: async () => ({ markdown: 'loaded' }),
|
|
|
|
|
},
|
|
|
|
|
},
|
|
|
|
|
stepBudget: 1,
|
|
|
|
|
telemetryTags: { operationName: 'test' },
|
|
|
|
|
});
|
|
|
|
|
expect(agentQuery.mock.calls[0][0].options.env).toEqual(expect.objectContaining({ HOME: '/Users/test' }));
|
2026-05-18 13:38:06 +02:00
|
|
|
expect(agentQuery.mock.calls[0][0].options.managedSettings).toEqual({
|
|
|
|
|
allowManagedMcpServersOnly: true,
|
|
|
|
|
allowedMcpServers: [{ serverName: 'ktx' }],
|
|
|
|
|
});
|
2026-05-16 12:06:34 +02:00
|
|
|
expect(agentQuery.mock.calls[0][0].options.env).not.toEqual(
|
|
|
|
|
expect.objectContaining({ ANTHROPIC_AUTH_TOKEN: 'token', CLAUDE_CODE_USE_VERTEX: '1' }),
|
|
|
|
|
);
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-28 02:09:53 +02:00
|
|
|
it('counts only assistant turns the SDK counts toward num_turns', async () => {
|
|
|
|
|
const assistantMessage = (
|
|
|
|
|
overrides: Partial<Extract<SDKMessage, { type: 'assistant' }>> & { uuid: string },
|
|
|
|
|
): SDKMessage =>
|
|
|
|
|
({
|
|
|
|
|
type: 'assistant',
|
|
|
|
|
message: { role: 'assistant', content: [], stop_reason: 'end_turn' },
|
|
|
|
|
parent_tool_use_id: null,
|
|
|
|
|
session_id: 'session-id',
|
|
|
|
|
...overrides,
|
|
|
|
|
}) as unknown as SDKMessage;
|
|
|
|
|
|
|
|
|
|
const query = vi.fn((_input: any) =>
|
|
|
|
|
stream([
|
|
|
|
|
initMessage(),
|
|
|
|
|
assistantMessage({
|
|
|
|
|
uuid: '00000000-0000-4000-8000-0000000000a1',
|
|
|
|
|
error: 'max_output_tokens',
|
|
|
|
|
}),
|
|
|
|
|
assistantMessage({
|
|
|
|
|
uuid: '00000000-0000-4000-8000-0000000000a2',
|
|
|
|
|
message: { role: 'assistant', content: [], stop_reason: 'pause_turn' } as never,
|
|
|
|
|
}),
|
|
|
|
|
assistantMessage({ uuid: '00000000-0000-4000-8000-0000000000a3' }),
|
|
|
|
|
{
|
|
|
|
|
type: 'assistant',
|
|
|
|
|
message: { role: 'assistant', content: [], stop_reason: 'end_turn' },
|
|
|
|
|
parent_tool_use_id: 'tool-use-1',
|
|
|
|
|
uuid: '00000000-0000-4000-8000-0000000000a4',
|
|
|
|
|
session_id: 'session-id',
|
|
|
|
|
} as unknown as SDKMessage,
|
|
|
|
|
resultMessage({ subtype: 'success', terminal_reason: 'completed' }),
|
|
|
|
|
]),
|
|
|
|
|
);
|
|
|
|
|
const runtime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query,
|
|
|
|
|
env: {},
|
|
|
|
|
});
|
|
|
|
|
const onStepFinish = vi.fn();
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
runtime.runAgentLoop({
|
|
|
|
|
modelRole: 'default',
|
|
|
|
|
systemPrompt: 'system',
|
|
|
|
|
userPrompt: 'user',
|
|
|
|
|
toolSet: {},
|
|
|
|
|
stepBudget: 40,
|
|
|
|
|
telemetryTags: { operationName: 'test' },
|
|
|
|
|
onStepFinish,
|
|
|
|
|
}),
|
feat(cli): profile ingest runs and split model vs tool time (#249)
* feat(cli): profile ingest runs to find where wall-clock time goes
Add opt-in profiling for `ktx ingest`. Each timed phase, work unit, and
agent loop now records durationMs / step count / token usage in the
trace, and a post-run aggregator rolls them up into a "where did the
time go" report printed to stderr.
Enable per run with KTX_PROFILE_INGEST (1/true -> human table, json ->
raw structured profile) or persistently via `ingest.profile` in
ktx.yaml. The json form emits raw milliseconds, token counts, and a
summary.headline one-line diagnosis so coding agents can parse it
directly; json wins when both env and config request profiling.
- runtime-port: RunLoopMetrics (totalMs, usage, stepCount,
stepBoundariesMs) plus onMetrics callbacks on text/object generation
- ai-sdk + claude-code runtimes: capture per-loop timing and token usage
- work-unit-executor and stages 3/4: thread metrics into trace events
- ingest-bundle.runner: time worktree / triage / clustering / index /
reconcile / squash phases and emit the profile in a finally block
(best-effort; never affects the run outcome)
- ingest-profile: new trace+transcript aggregator with table/json formatters
- config: ingest.profile flag; docs: profiling section in ktx-ingest.mdx
* fix(cli): flush tool-call logs before reading ingest profile
Tool transcripts are appended fire-and-forget so the agent hot path never
blocks on logging. The ingest profiler read them before the writes settled,
so per-work-unit toolMs (and the model-vs-tool split derived from it) could
be incomplete. Track in-flight appends and expose flushToolCallLogs() —
bounded by a timeout so it can never hang — and flush before the profiler
reads the transcript.
2026-06-01 15:49:17 +02:00
|
|
|
).resolves.toMatchObject({ stopReason: 'natural' });
|
2026-05-28 02:09:53 +02:00
|
|
|
|
|
|
|
|
expect(onStepFinish).toHaveBeenCalledTimes(1);
|
|
|
|
|
expect(onStepFinish).toHaveBeenCalledWith({ stepIndex: 1, stepBudget: 40 });
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-16 12:06:34 +02:00
|
|
|
it('logs and ignores onStepFinish callback errors', async () => {
|
|
|
|
|
const query = vi.fn((_input: any) =>
|
|
|
|
|
stream([
|
|
|
|
|
initMessage(),
|
|
|
|
|
{
|
|
|
|
|
type: 'assistant',
|
|
|
|
|
message: { role: 'assistant', content: [] },
|
|
|
|
|
parent_tool_use_id: null,
|
|
|
|
|
uuid: '00000000-0000-4000-8000-000000000005',
|
|
|
|
|
session_id: 'session-id',
|
|
|
|
|
} as unknown as SDKMessage,
|
|
|
|
|
resultMessage({ subtype: 'success', terminal_reason: 'completed' }),
|
|
|
|
|
]),
|
|
|
|
|
);
|
|
|
|
|
const logger = {
|
|
|
|
|
debug: vi.fn(),
|
|
|
|
|
log: vi.fn(),
|
|
|
|
|
warn: vi.fn(),
|
|
|
|
|
error: vi.fn(),
|
|
|
|
|
};
|
|
|
|
|
const runtime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query,
|
|
|
|
|
env: {},
|
|
|
|
|
logger,
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
runtime.runAgentLoop({
|
|
|
|
|
modelRole: 'default',
|
|
|
|
|
systemPrompt: 'system',
|
|
|
|
|
userPrompt: 'user',
|
|
|
|
|
toolSet: {},
|
|
|
|
|
stepBudget: 1,
|
|
|
|
|
telemetryTags: { operationName: 'test' },
|
|
|
|
|
onStepFinish: async () => {
|
|
|
|
|
throw new Error('callback exploded');
|
|
|
|
|
},
|
|
|
|
|
}),
|
feat(cli): profile ingest runs and split model vs tool time (#249)
* feat(cli): profile ingest runs to find where wall-clock time goes
Add opt-in profiling for `ktx ingest`. Each timed phase, work unit, and
agent loop now records durationMs / step count / token usage in the
trace, and a post-run aggregator rolls them up into a "where did the
time go" report printed to stderr.
Enable per run with KTX_PROFILE_INGEST (1/true -> human table, json ->
raw structured profile) or persistently via `ingest.profile` in
ktx.yaml. The json form emits raw milliseconds, token counts, and a
summary.headline one-line diagnosis so coding agents can parse it
directly; json wins when both env and config request profiling.
- runtime-port: RunLoopMetrics (totalMs, usage, stepCount,
stepBoundariesMs) plus onMetrics callbacks on text/object generation
- ai-sdk + claude-code runtimes: capture per-loop timing and token usage
- work-unit-executor and stages 3/4: thread metrics into trace events
- ingest-bundle.runner: time worktree / triage / clustering / index /
reconcile / squash phases and emit the profile in a finally block
(best-effort; never affects the run outcome)
- ingest-profile: new trace+transcript aggregator with table/json formatters
- config: ingest.profile flag; docs: profiling section in ktx-ingest.mdx
* fix(cli): flush tool-call logs before reading ingest profile
Tool transcripts are appended fire-and-forget so the agent hot path never
blocks on logging. The ingest profiler read them before the writes settled,
so per-work-unit toolMs (and the model-vs-tool split derived from it) could
be incomplete. Track in-flight appends and expose flushToolCallLogs() —
bounded by a timeout so it can never hang — and flush before the profiler
reads the transcript.
2026-06-01 15:49:17 +02:00
|
|
|
).resolves.toMatchObject({ stopReason: 'natural' });
|
2026-05-16 12:06:34 +02:00
|
|
|
expect(logger.warn).toHaveBeenCalledWith(expect.stringContaining('callback exploded'));
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('maps max-turn terminal reasons to budget', () => {
|
|
|
|
|
expect(mapClaudeCodeStopReason(resultMessage({ subtype: 'error_max_turns' }))).toBe('budget');
|
|
|
|
|
expect(mapClaudeCodeStopReason(resultMessage({ terminal_reason: 'max_turns' }))).toBe('budget');
|
|
|
|
|
expect(mapClaudeCodeStopReason(resultMessage({ stop_reason: 'max_turns' }))).toBe('budget');
|
|
|
|
|
expect(mapClaudeCodeStopReason(resultMessage({ subtype: 'success', terminal_reason: 'completed' }))).toBe('natural');
|
|
|
|
|
expect(mapClaudeCodeStopReason(resultMessage({ subtype: 'error_during_execution' }))).toBe('error');
|
|
|
|
|
});
|
|
|
|
|
|
feat(cli): profile ingest runs and split model vs tool time (#249)
* feat(cli): profile ingest runs to find where wall-clock time goes
Add opt-in profiling for `ktx ingest`. Each timed phase, work unit, and
agent loop now records durationMs / step count / token usage in the
trace, and a post-run aggregator rolls them up into a "where did the
time go" report printed to stderr.
Enable per run with KTX_PROFILE_INGEST (1/true -> human table, json ->
raw structured profile) or persistently via `ingest.profile` in
ktx.yaml. The json form emits raw milliseconds, token counts, and a
summary.headline one-line diagnosis so coding agents can parse it
directly; json wins when both env and config request profiling.
- runtime-port: RunLoopMetrics (totalMs, usage, stepCount,
stepBoundariesMs) plus onMetrics callbacks on text/object generation
- ai-sdk + claude-code runtimes: capture per-loop timing and token usage
- work-unit-executor and stages 3/4: thread metrics into trace events
- ingest-bundle.runner: time worktree / triage / clustering / index /
reconcile / squash phases and emit the profile in a finally block
(best-effort; never affects the run outcome)
- ingest-profile: new trace+transcript aggregator with table/json formatters
- config: ingest.profile flag; docs: profiling section in ktx-ingest.mdx
* fix(cli): flush tool-call logs before reading ingest profile
Tool transcripts are appended fire-and-forget so the agent hot path never
blocks on logging. The ingest profiler read them before the writes settled,
so per-work-unit toolMs (and the model-vs-tool split derived from it) could
be incomplete. Track in-flight appends and expose flushToolCallLogs() —
bounded by a timeout so it can never hang — and flush before the profiler
reads the transcript.
2026-06-01 15:49:17 +02:00
|
|
|
it('returns loop metrics including step count and mapped token usage', async () => {
|
|
|
|
|
const query = vi.fn((_input: any) =>
|
|
|
|
|
stream([
|
|
|
|
|
initMessage(),
|
|
|
|
|
{
|
|
|
|
|
type: 'assistant',
|
|
|
|
|
message: { role: 'assistant', content: [] },
|
|
|
|
|
parent_tool_use_id: null,
|
|
|
|
|
uuid: '00000000-0000-4000-8000-000000000006',
|
|
|
|
|
session_id: 'session-id',
|
|
|
|
|
} as unknown as SDKMessage,
|
|
|
|
|
resultMessage({
|
|
|
|
|
subtype: 'success',
|
|
|
|
|
terminal_reason: 'completed',
|
|
|
|
|
usage: { input_tokens: 50, output_tokens: 10 } as never,
|
|
|
|
|
}),
|
|
|
|
|
]),
|
|
|
|
|
);
|
|
|
|
|
const runtime = new ClaudeCodeKtxLlmRuntime({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
modelSlots: { default: 'sonnet' },
|
|
|
|
|
query,
|
|
|
|
|
env: {},
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
const result = await runtime.runAgentLoop({
|
|
|
|
|
modelRole: 'default',
|
|
|
|
|
systemPrompt: 'system',
|
|
|
|
|
userPrompt: 'user',
|
|
|
|
|
toolSet: {},
|
|
|
|
|
stepBudget: 40,
|
|
|
|
|
telemetryTags: { operationName: 'test' },
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
expect(result.metrics?.stepCount).toBe(1);
|
|
|
|
|
expect(result.metrics?.stepBoundariesMs).toHaveLength(1);
|
|
|
|
|
expect(result.metrics?.usage).toEqual({ inputTokens: 50, outputTokens: 10, totalTokens: 60 });
|
|
|
|
|
});
|
|
|
|
|
|
2026-05-16 12:06:34 +02:00
|
|
|
it('auth probe uses isolation options and a scrubbed env', async () => {
|
|
|
|
|
const query = vi.fn((_input: any) => stream([initMessage(), resultMessage({ result: 'ok' })]));
|
|
|
|
|
|
|
|
|
|
await expect(
|
|
|
|
|
runClaudeCodeAuthProbe({ projectDir: '/tmp/project', model: 'sonnet', query, env: { ANTHROPIC_API_KEY: 'sk-ant-test' } }), // pragma: allowlist secret
|
|
|
|
|
).resolves.toEqual({ ok: true });
|
|
|
|
|
expect(query.mock.calls[0][0].options).toMatchObject({
|
|
|
|
|
settingSources: [],
|
|
|
|
|
skills: [],
|
|
|
|
|
plugins: [],
|
|
|
|
|
tools: [],
|
2026-05-18 13:38:06 +02:00
|
|
|
managedSettings: {
|
|
|
|
|
allowManagedMcpServersOnly: true,
|
|
|
|
|
allowedMcpServers: [],
|
|
|
|
|
},
|
|
|
|
|
strictMcpConfig: true,
|
2026-05-16 12:06:34 +02:00
|
|
|
allowedTools: [],
|
|
|
|
|
persistSession: false,
|
|
|
|
|
env: expect.not.objectContaining({ ANTHROPIC_API_KEY: 'sk-ant-test' }),
|
|
|
|
|
});
|
|
|
|
|
});
|
|
|
|
|
|
|
|
|
|
it('reports unsupported Claude Code models without framing them as auth failures', async () => {
|
|
|
|
|
await expect(
|
|
|
|
|
runClaudeCodeAuthProbe({
|
|
|
|
|
projectDir: '/tmp/project',
|
|
|
|
|
model: 'gpt-5',
|
|
|
|
|
query: vi.fn(),
|
|
|
|
|
env: {},
|
|
|
|
|
}),
|
|
|
|
|
).resolves.toEqual({
|
|
|
|
|
ok: false,
|
|
|
|
|
message: 'Unsupported Claude Code model "gpt-5". Use sonnet, opus, haiku, or a claude-* model id.',
|
|
|
|
|
});
|
|
|
|
|
});
|
|
|
|
|
});
|