fix(ai) - add logs + remove dashboard building (#21440)
- add logs for thread finishing without agent message - add logs to monitor toolCall token usage - remove dashboard building via AI (before fixing it) - fix Anthropic compute
This commit is contained in:
Etienne
-11
@@ -2,7 +2,6 @@ import type { MessageDescriptor } from '@lingui/core';
|
||||
import { msg } from '@lingui/core/macro';
|
||||
import {
|
||||
type IconComponent,
|
||||
IconLayoutDashboard,
|
||||
IconPlus,
|
||||
IconSettingsAutomation,
|
||||
} from 'twenty-ui-deprecated/display';
|
||||
@@ -15,16 +14,6 @@ export type SuggestedPrompt = {
|
||||
};
|
||||
|
||||
export const DEFAULT_SUGGESTED_PROMPTS: SuggestedPrompt[] = [
|
||||
{
|
||||
id: 'dashboard',
|
||||
label: msg`Create a dashboard`,
|
||||
Icon: IconLayoutDashboard,
|
||||
prefillPrompts: [
|
||||
msg`Create a dashboard with a chart of deal value by pipeline stage (New, Meeting, Proposal, Negotiation, Closed Won/Lost) for the current quarter, and a table of my top 10 open opportunities with amount, stage and expected close date.`,
|
||||
msg`Build a dashboard that shows: (1) total pipeline value by stage for the last 3 months, (2) count of deals won vs lost per month, (3) average deal size. Use our standard pipeline stages.`,
|
||||
msg`I need a dashboard for lead conversion: number of new leads by source this month, how many moved to opportunity, and conversion rate by source. Include a simple table and a bar chart.`,
|
||||
],
|
||||
},
|
||||
{
|
||||
id: 'workflow',
|
||||
label: msg`Create a workflow`,
|
||||
|
||||
+7
-3
@@ -28,7 +28,6 @@ export const buildMcpServerInstructions = (
|
||||
` METADATA: get/create/update/delete_object_metadata | get/create/update/delete_field_metadata`,
|
||||
` Both GET tools return system items as compact summaries by default — keep that default for listing/inspecting; only set includeFullSystemObjects / includeFullSystemFields=true when you specifically need a system item's full configuration`,
|
||||
` VIEW: get_views | get_view_query_parameters | create/update/delete_view | manage view fields, filters, sorts`,
|
||||
` DASHBOARD: list_dashboards | get_dashboard | create_complete_dashboard | add/update/delete_dashboard_widget`,
|
||||
` WEBHOOK: list/create/update/delete_webhook`,
|
||||
` NAVIGATION: list/create/update/delete_navigation_menu_item`,
|
||||
` LOGIC_FUNCTION: app_{function_name} — workspace-specific; use list_logic_function_tools to discover`,
|
||||
@@ -36,8 +35,13 @@ export const buildMcpServerInstructions = (
|
||||
`Skills vs Tools:`,
|
||||
` Skills = documentation (load_skills) — teach HOW to do something, correct schemas and patterns`,
|
||||
` Tools = execution (execute_tool) — let you DO something`,
|
||||
` For complex tasks (workflows, dashboards, metadata), load the matching skill BEFORE calling tools.`,
|
||||
` ⚠️ Never call workflow, dashboard, or metadata tools without loading their skill first.`,
|
||||
` For complex tasks (workflows, metadata), load the matching skill BEFORE calling tools.`,
|
||||
` ⚠️ Never call workflow or metadata tools without loading their skill first.`,
|
||||
``,
|
||||
`Dashboards (coming soon):`,
|
||||
` Building or editing dashboards through the AI is not available yet — it is a coming soon feature.`,
|
||||
` If asked to create/build/modify a dashboard, do not attempt it: say AI-assisted dashboards are coming soon,`,
|
||||
` and offer alternatives (create views, run analytics with group_by_{objects}, or build workflows).`,
|
||||
``,
|
||||
`Route by intent:`,
|
||||
` Named entity ("Acme company") → find_many_{objects} to resolve id first, then operate on id`,
|
||||
|
||||
@@ -4,7 +4,6 @@ import { TypeOrmModule } from '@nestjs/typeorm';
|
||||
import { RecordCrudModule } from 'src/engine/core-modules/record-crud/record-crud.module';
|
||||
import { TOOL_PROVIDERS } from 'src/engine/core-modules/tool-provider/constants/tool-providers.token';
|
||||
import { ActionToolProvider } from 'src/engine/core-modules/tool-provider/providers/action-tool.provider';
|
||||
import { DashboardToolProvider } from 'src/engine/core-modules/tool-provider/providers/dashboard-tool.provider';
|
||||
import { DatabaseToolProvider } from 'src/engine/core-modules/tool-provider/providers/database-tool.provider';
|
||||
import { LogicFunctionToolProvider } from 'src/engine/core-modules/tool-provider/providers/logic-function-tool.provider';
|
||||
import { MetadataToolProvider } from 'src/engine/core-modules/tool-provider/providers/metadata-tool.provider';
|
||||
@@ -69,7 +68,6 @@ import { ToolRegistryService } from './services/tool-registry.service';
|
||||
ToolIndexResolver,
|
||||
ToolExecutorService,
|
||||
ActionToolProvider,
|
||||
DashboardToolProvider,
|
||||
DatabaseToolProvider,
|
||||
MetadataToolProvider,
|
||||
NavigationMenuItemToolProvider,
|
||||
@@ -85,7 +83,6 @@ import { ToolRegistryService } from './services/tool-registry.service';
|
||||
provide: TOOL_PROVIDERS,
|
||||
useFactory: (
|
||||
actionProvider: ActionToolProvider,
|
||||
dashboardProvider: DashboardToolProvider,
|
||||
databaseProvider: DatabaseToolProvider,
|
||||
metadataProvider: MetadataToolProvider,
|
||||
logicFunctionProvider: LogicFunctionToolProvider,
|
||||
@@ -95,7 +92,6 @@ import { ToolRegistryService } from './services/tool-registry.service';
|
||||
workflowProvider: WorkflowToolProvider,
|
||||
) => [
|
||||
actionProvider,
|
||||
dashboardProvider,
|
||||
databaseProvider,
|
||||
metadataProvider,
|
||||
logicFunctionProvider,
|
||||
@@ -106,7 +102,6 @@ import { ToolRegistryService } from './services/tool-registry.service';
|
||||
],
|
||||
inject: [
|
||||
ActionToolProvider,
|
||||
DashboardToolProvider,
|
||||
DatabaseToolProvider,
|
||||
MetadataToolProvider,
|
||||
LogicFunctionToolProvider,
|
||||
|
||||
+13
-9
@@ -145,7 +145,7 @@ describe('AiBillingService', () => {
|
||||
expect(costInDollars).toBeCloseTo(0.00675);
|
||||
});
|
||||
|
||||
it('should not subtract cached tokens from input for Anthropic', () => {
|
||||
it('should not double-count cached and cache-creation tokens for Anthropic', () => {
|
||||
mockAiModelRegistryService.getEffectiveModelConfig.mockReturnValue(
|
||||
anthropicModelConfig as ReturnType<
|
||||
AiModelRegistryService['getEffectiveModelConfig']
|
||||
@@ -156,13 +156,15 @@ describe('AiBillingService', () => {
|
||||
'claude-sonnet-4-5-20250929',
|
||||
{
|
||||
usage: {
|
||||
inputTokens: 400,
|
||||
// @ai-sdk/anthropic reports inputTokens as the FULL prompt:
|
||||
// noCache(400) + cacheRead(600) + cacheCreation(200) = 1200
|
||||
inputTokens: 1200,
|
||||
outputTokens: 500,
|
||||
totalTokens: 900,
|
||||
totalTokens: 1700,
|
||||
inputTokenDetails: {
|
||||
noCacheTokens: 400,
|
||||
cacheReadTokens: 600,
|
||||
cacheWriteTokens: 0,
|
||||
cacheWriteTokens: 200,
|
||||
},
|
||||
outputTokenDetails: { textTokens: 500, reasoningTokens: 0 },
|
||||
},
|
||||
@@ -170,7 +172,8 @@ describe('AiBillingService', () => {
|
||||
},
|
||||
);
|
||||
|
||||
// Anthropic: inputTokens already excludes cached
|
||||
// inputTokens already includes cached + cache-creation, so the
|
||||
// full-rate portion is 1200 - 600 - 200 = 400
|
||||
// inputCost = (400/1M * 3.0) = 0.0012
|
||||
// cachedCost = (600/1M * 0.3) = 0.00018
|
||||
// cacheCreationCost = (200/1M * 3.75) = 0.00075
|
||||
@@ -290,12 +293,13 @@ describe('AiBillingService', () => {
|
||||
'claude-sonnet-4-5-20250929',
|
||||
{
|
||||
usage: {
|
||||
inputTokens: 150_000,
|
||||
// Full prompt size = noCache(150k) + cacheRead(100k) = 250k
|
||||
inputTokens: 250_000,
|
||||
outputTokens: 1000,
|
||||
totalTokens: 251_000,
|
||||
cachedInputTokens: 100_000,
|
||||
inputTokenDetails: {
|
||||
noCacheTokens: 0,
|
||||
noCacheTokens: 150_000,
|
||||
cacheReadTokens: 100_000,
|
||||
cacheWriteTokens: 0,
|
||||
},
|
||||
@@ -304,8 +308,8 @@ describe('AiBillingService', () => {
|
||||
},
|
||||
);
|
||||
|
||||
// Anthropic: total input = 150k + 100k + 0 = 250k > 200k threshold
|
||||
// Uses long context rates
|
||||
// Total input = 250k > 200k threshold -> long context rates
|
||||
// full-rate portion = 250k - 100k - 0 = 150k
|
||||
// inputCost = (150_000/1M * 6.0) = 0.9
|
||||
// cachedCost = (100_000/1M * 0.6) = 0.06
|
||||
// outputCost = (1000/1M * 22.5) = 0.0225
|
||||
|
||||
+13
-10
@@ -29,10 +29,14 @@ const safeNumber = (value: number | undefined): number => {
|
||||
return Number.isFinite(result) ? result : 0;
|
||||
};
|
||||
|
||||
// Input token semantics differ by model family:
|
||||
// Anthropic: inputTokens excludes cached and cache creation tokens
|
||||
// OpenAI/xAI/Groq/Google: inputTokens includes cached tokens
|
||||
// Output token semantics also differ:
|
||||
// Input token semantics (all providers we use):
|
||||
// `inputTokens` is the FULL prompt size and already includes cached and
|
||||
// cache-creation tokens. The @ai-sdk/anthropic provider reports
|
||||
// inputTokens = noCache + cacheRead + cacheCreation, and OpenAI-style
|
||||
// providers include cached tokens (and never report cache-creation tokens).
|
||||
// So the uncached, full-rate portion is always inputTokens minus cached
|
||||
// minus cache-creation, and the full input size is just inputTokens.
|
||||
// Output token semantics still differ by model family:
|
||||
// Anthropic: outputTokens excludes reasoning (thinking) tokens
|
||||
// OpenAI/xAI/Groq/Google: outputTokens includes reasoning tokens
|
||||
export const computeCostBreakdown = (
|
||||
@@ -47,17 +51,16 @@ export const computeCostBreakdown = (
|
||||
|
||||
const isAnthropicTokenReporting = model.modelFamily === ModelFamily.CLAUDE;
|
||||
|
||||
const adjustedInputTokens = isAnthropicTokenReporting
|
||||
? rawInputTokens
|
||||
: Math.max(0, rawInputTokens - cachedInputTokens);
|
||||
const adjustedInputTokens = Math.max(
|
||||
0,
|
||||
rawInputTokens - cachedInputTokens - cacheCreationTokens,
|
||||
);
|
||||
|
||||
const adjustedOutputTokens = isAnthropicTokenReporting
|
||||
? rawOutputTokens
|
||||
: Math.max(0, rawOutputTokens - reasoningTokens);
|
||||
|
||||
const totalInputTokens = isAnthropicTokenReporting
|
||||
? rawInputTokens + cachedInputTokens + cacheCreationTokens
|
||||
: rawInputTokens + cacheCreationTokens;
|
||||
const totalInputTokens = rawInputTokens;
|
||||
|
||||
const costInfo =
|
||||
model.longContextCost &&
|
||||
|
||||
+6
-3
@@ -7,20 +7,23 @@ export const CHAT_SYSTEM_PROMPTS = {
|
||||
|
||||
For ANY non-trivial task, follow this order:
|
||||
|
||||
1. **Plan**: Identify what the user needs. Determine which domain is involved (workflows, dashboards, metadata, data, documents, etc.).
|
||||
1. **Plan**: Identify what the user needs. Determine which domain is involved (workflows, metadata, data, documents, etc.).
|
||||
2. **Load the relevant skill FIRST**: Call \`load_skills\` to get detailed instructions, correct schemas, and parameter formats BEFORE doing anything else. Skills contain critical knowledge you don't have built-in — skipping this step leads to incorrect parameters and failed tool calls.
|
||||
3. **Learn the required tools**: Call \`learn_tools\` to discover tool schemas and descriptions before using them. Pass every tool you need in a single \`learn_tools\` call (\`toolNames\` is an array) — do not make one call per tool.
|
||||
4. **Execute**: Call \`execute_tool\` to run the tools following the instructions from the skill.
|
||||
|
||||
⚠️ NEVER call a specialized tool (workflow, dashboard, metadata, etc.) without loading its matching skill first. The Available Skills section below lists all skills — look for the one that matches the user's task domain and load it.
|
||||
⚠️ NEVER call a specialized tool (workflow, metadata, etc.) without loading its matching skill first. The Available Skills section below lists all skills — look for the one that matches the user's task domain and load it.
|
||||
|
||||
Examples:
|
||||
- User asks to create a workflow → \`load_skills(["workflow-building"])\` then learn and execute workflow tools
|
||||
- User asks to build a dashboard → \`load_skills(["dashboard-building"])\` then learn and execute dashboard tools
|
||||
- User asks to export data to Excel → \`load_skills(["xlsx", "code-interpreter"])\` then \`learn_tools({toolNames: ["code_interpreter"]})\` then \`execute_tool({toolName: "code_interpreter", arguments: {...}})\`
|
||||
|
||||
For simple CRUD operations (find/create/update/delete a record), you do NOT need a skill — but you still MUST call \`learn_tools\` first to learn the tool schema, then \`execute_tool\` to run it.
|
||||
|
||||
## Dashboards (coming soon)
|
||||
|
||||
Building or editing dashboards through the AI is not available yet — it is a coming soon feature. If the user asks you to create, build, or modify a dashboard, do NOT attempt it: let them know that AI-assisted dashboards are coming soon, and offer the alternatives you can help with today (e.g. creating views, running analytics with \`group_by_*\`, or building workflows).
|
||||
|
||||
## Skills vs Tools
|
||||
|
||||
- **SKILLS** = documentation/instructions (loaded via \`load_skills\`). They teach you HOW to do something — correct schemas, parameters, and patterns. They do NOT give you execution ability.
|
||||
|
||||
+83
-4
@@ -7,7 +7,9 @@ import type {
|
||||
ExtendedUIMessage,
|
||||
ExtendedUIMessagePart,
|
||||
} from 'twenty-shared/ai';
|
||||
import { isNonEmptyString } from '@sniptt/guards';
|
||||
import { Repository } from 'typeorm';
|
||||
import { isDefined } from 'twenty-shared/utils';
|
||||
|
||||
import { InjectWorkspaceScopedRepository } from 'src/engine/twenty-orm/workspace-scoped-repository/inject-workspace-scoped-repository.decorator';
|
||||
import { WorkspaceScopedRepository } from 'src/engine/twenty-orm/workspace-scoped-repository/workspace-scoped-repository';
|
||||
@@ -274,10 +276,13 @@ export class StreamAgentChatJob {
|
||||
},
|
||||
});
|
||||
},
|
||||
onFinish: async ({ responseMessage }) => {
|
||||
onFinish: async ({ responseMessage, isAborted }) => {
|
||||
try {
|
||||
await this.handleStreamFinish({
|
||||
responseMessage,
|
||||
isAborted,
|
||||
streamError,
|
||||
outOfCredits: checkHasNoMoreAvailableCredits(),
|
||||
threadId: data.threadId,
|
||||
workspaceId: data.workspaceId,
|
||||
userWorkspaceId: data.userWorkspaceId,
|
||||
@@ -353,7 +358,6 @@ export class StreamAgentChatJob {
|
||||
type: string;
|
||||
usage?: {
|
||||
inputTokens?: number;
|
||||
inputTokenDetails?: { cacheReadTokens?: number };
|
||||
};
|
||||
totalUsage?: {
|
||||
inputTokens?: number;
|
||||
@@ -378,13 +382,12 @@ export class StreamAgentChatJob {
|
||||
}) {
|
||||
if (part.type === 'finish-step') {
|
||||
const stepInput = part.usage?.inputTokens ?? 0;
|
||||
const stepCached = part.usage?.inputTokenDetails?.cacheReadTokens ?? 0;
|
||||
const stepCacheCreation = extractCacheCreationTokens(
|
||||
part.providerMetadata,
|
||||
);
|
||||
|
||||
onUpdateCacheCreationTokens(totalCacheCreationTokens + stepCacheCreation);
|
||||
onUpdateConversationSize(stepInput + stepCached + stepCacheCreation);
|
||||
onUpdateConversationSize(stepInput);
|
||||
}
|
||||
|
||||
if (part.type === 'finish') {
|
||||
@@ -432,6 +435,9 @@ export class StreamAgentChatJob {
|
||||
|
||||
private async handleStreamFinish({
|
||||
responseMessage,
|
||||
isAborted,
|
||||
streamError,
|
||||
outOfCredits,
|
||||
threadId,
|
||||
workspaceId,
|
||||
userWorkspaceId,
|
||||
@@ -442,6 +448,9 @@ export class StreamAgentChatJob {
|
||||
userMessagePromise,
|
||||
}: {
|
||||
responseMessage: Omit<ExtendedUIMessage, 'id'>;
|
||||
isAborted: boolean;
|
||||
streamError: unknown;
|
||||
outOfCredits: boolean;
|
||||
threadId: string;
|
||||
workspaceId: string;
|
||||
userWorkspaceId: string;
|
||||
@@ -457,6 +466,23 @@ export class StreamAgentChatJob {
|
||||
modelConfig: AiModelConfig;
|
||||
userMessagePromise: Promise<{ turnId: string | null }>;
|
||||
}): Promise<void> {
|
||||
const hasText = responseMessage.parts.some(
|
||||
(part) => part.type === 'text' && isNonEmptyString(part.text),
|
||||
);
|
||||
|
||||
if (isAborted || !hasText) {
|
||||
this.logAssistantTurnWithoutText({
|
||||
responseMessage,
|
||||
isAborted,
|
||||
streamError,
|
||||
outOfCredits,
|
||||
hasText,
|
||||
threadId,
|
||||
workspaceId,
|
||||
streamUsage,
|
||||
});
|
||||
}
|
||||
|
||||
if (responseMessage.parts.length === 0) {
|
||||
return;
|
||||
}
|
||||
@@ -506,4 +532,57 @@ export class StreamAgentChatJob {
|
||||
workspaceId,
|
||||
});
|
||||
}
|
||||
|
||||
private logAssistantTurnWithoutText({
|
||||
responseMessage,
|
||||
isAborted,
|
||||
streamError,
|
||||
outOfCredits,
|
||||
hasText,
|
||||
threadId,
|
||||
workspaceId,
|
||||
streamUsage,
|
||||
}: {
|
||||
responseMessage: Omit<ExtendedUIMessage, 'id'>;
|
||||
isAborted: boolean;
|
||||
streamError: unknown;
|
||||
outOfCredits: boolean;
|
||||
hasText: boolean;
|
||||
threadId: string;
|
||||
workspaceId: string;
|
||||
streamUsage: {
|
||||
inputTokens: number;
|
||||
outputTokens: number;
|
||||
};
|
||||
}): void {
|
||||
const reason = isAborted
|
||||
? 'user-cancelled'
|
||||
: streamError
|
||||
? 'stream-error'
|
||||
: outOfCredits
|
||||
? 'credits-exhausted'
|
||||
: 'empty-completion';
|
||||
|
||||
const errorDetail =
|
||||
streamError instanceof Error
|
||||
? `${streamError.name}: ${streamError.message}`
|
||||
: isDefined(streamError)
|
||||
? String(streamError)
|
||||
: 'none';
|
||||
|
||||
this.logger.warn(
|
||||
`[AI_CHAT_NO_TEXT] Assistant turn ended without a text reply — ` +
|
||||
`reason=${reason}, threadId=${threadId}, workspaceId=${workspaceId}, ` +
|
||||
`isAborted=${isAborted}, outOfCredits=${outOfCredits}, hasText=${hasText}, ` +
|
||||
`streamError=${errorDetail}, ` +
|
||||
`inputTokens=${streamUsage.inputTokens},` +
|
||||
`responseMessage.parts=${JSON.stringify(responseMessage.parts)}`,
|
||||
);
|
||||
|
||||
if (streamError instanceof Error && isDefined(streamError.stack)) {
|
||||
this.logger.warn(
|
||||
`[AI_CHAT_NO_TEXT] streamError stack — threadId=${threadId}: ${streamError.stack}`,
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
+13
@@ -288,6 +288,7 @@ export class ChatExecutionService {
|
||||
const streamStartedAt = performance.now();
|
||||
let stepStartedAt = streamStartedAt;
|
||||
let ttftRecorded = false;
|
||||
let stepIndex = 0;
|
||||
|
||||
const emitTurnUsageEvent = async (steps: StepResult<ToolSet>[]) => {
|
||||
const usage = steps.reduce<LanguageModelUsage>(
|
||||
@@ -447,6 +448,18 @@ export class ChatExecutionService {
|
||||
hasNoMoreAvailableCredits = true;
|
||||
}
|
||||
|
||||
this.logger.log(
|
||||
`[AI_CHAT_TOKENS] step #${++stepIndex} — ` +
|
||||
`toolCallIds=[${step.toolCalls.map((toolCall) => toolCall.toolCallId).join(', ')}]: ` +
|
||||
`outputTokens=${step.usage.outputTokens ?? 0}, ` +
|
||||
`reasoningTokens=${step.usage.outputTokenDetails?.reasoningTokens ?? 0}, ` +
|
||||
`inputTokens(fullContext)=${step.usage.inputTokens ?? 0}, ` +
|
||||
`cacheReadTokens=${step.usage.inputTokenDetails?.cacheReadTokens ?? 0}, ` +
|
||||
`cacheWriteTokens=${step.usage.inputTokenDetails?.cacheWriteTokens ?? 0}, ` +
|
||||
`cacheCreationTokens=${extractCacheCreationTokens(step.providerMetadata)}, ` +
|
||||
`totalTokens=${step.usage.totalTokens ?? 0}`,
|
||||
);
|
||||
|
||||
for (const toolResult of step.toolResults) {
|
||||
const output = toolResult.output as ToolOutput | undefined;
|
||||
|
||||
|
||||
+4
-32
@@ -276,38 +276,6 @@ Also create additional views for the standard objects (People, Companies, Opport
|
||||
Navigate to each view after creating it. Wait 3 seconds.
|
||||
|
||||
Loop STEP 8 for all the custom objects
|
||||
|
||||
STEP 9: Create a multi-tab dashboard that tells the full story of the business.
|
||||
|
||||
Use create_complete_dashboard to create the first tab, then add_dashboard_tab + add_dashboard_widget for subsequent tabs.
|
||||
|
||||
**Structure: 3 tabs**
|
||||
|
||||
Tab 1 — "Overview": high-level KPIs and charts across the whole workspace
|
||||
- Row 0: 3–4 AGGREGATE_CHART widgets (KPIs) — one per key metric (e.g. total revenue from Opportunities, count of active People, count of open deals). columnSpan 3–4, rowSpan 3.
|
||||
- Row 3: 1–2 BAR_CHART or LINE_CHART widgets showing trends over time (group by a DATE_TIME field with MONTH granularity). columnSpan 6, rowSpan 7.
|
||||
- Row 3: 1 PIE_CHART showing distribution by a SELECT field (e.g. status, type). columnSpan 6, rowSpan 7.
|
||||
- Row 10: 1 STANDALONE_RICH_TEXT widget summarising the dashboard story. columnSpan 12, rowSpan 3.
|
||||
|
||||
Tab 2 — "[Domain object] pipeline" (e.g. "Deals", "Applications", "Repairs"): focus on Opportunities enriched with domain data
|
||||
- Before adding the RECORD_TABLE widget, run this 3-step sequence:
|
||||
1. create_view (type TABLE, name e.g. "Active Deals") → get the new viewId
|
||||
2. create_many_view_fields on the new viewId — add 4–6 key fields (name, the new stage/status SELECT, a CURRENCY/NUMERIC field, a DATE field, linked Person or Company). Use positions 0, 1, 2… and isVisible: true.
|
||||
3. create_many_view_filters + create_view_sort — e.g. filter out CLOSED/LOST records (SELECT IS_NOT "CLOSED"), sort by value DESC
|
||||
- Row 0: 1 RECORD_TABLE widget. Set objectMetadataId to Opportunity, configuration.viewId to the dedicated view. columnSpan 12, rowSpan 8.
|
||||
- Row 8: 1 BAR_CHART grouped by the stage SELECT field. columnSpan 6, rowSpan 7.
|
||||
- Row 8: 1 PIE_CHART or AGGREGATE_CHART on the CURRENCY field. columnSpan 6, rowSpan 7.
|
||||
|
||||
Tab 3 — "[Domain people role] list" (e.g. "Clients", "Candidates", "Contacts"): focus on People enriched with domain data
|
||||
- Before adding the RECORD_TABLE widget, run this 3-step sequence:
|
||||
1. create_view (type TABLE, name e.g. "All Clients") → get the new viewId
|
||||
2. create_many_view_fields — add 4–5 key fields (name, email, the new SELECT/status field, a DATE field, linked Company)
|
||||
3. create_view_sort — sort by createdAt DESC or by name ASC
|
||||
- Row 0: 1 RECORD_TABLE widget with the dedicated view. columnSpan 12, rowSpan 8.
|
||||
- Row 8: 2–3 AGGREGATE_CHART KPIs (count, totals). columnSpan 4, rowSpan 3.
|
||||
- Row 11: 1 BAR_CHART or LINE_CHART. columnSpan 12, rowSpan 7.
|
||||
|
||||
After creating the dashboard, navigate to the dashboard page.
|
||||
`,
|
||||
isCustom: false,
|
||||
},
|
||||
@@ -437,6 +405,10 @@ After creating a tab, use its returned tabId as pageLayoutTabId when calling add
|
||||
- When modifying a chart, confirm whether the user wants to change settings or change chart type
|
||||
- Use RECORD_TABLE widgets to give users direct access to filtered record lists without leaving the dashboard`,
|
||||
isCustom: false,
|
||||
// Dashboard tools are temporarily disabled in AI chat / MCP because the
|
||||
// generated dashboards are not reliable yet. Keeping the skill defined
|
||||
// (inactive) so it can be re-enabled once the tooling is trustworthy.
|
||||
isActive: false,
|
||||
},
|
||||
}),
|
||||
|
||||
|
||||
+12
-2
@@ -14,6 +14,7 @@ export type CreateStandardSkillContext = {
|
||||
description: string | null;
|
||||
content: string;
|
||||
isCustom: boolean;
|
||||
isActive?: boolean;
|
||||
};
|
||||
|
||||
export type CreateStandardSkillArgs = StandardBuilderArgs<'skill'> & {
|
||||
@@ -21,7 +22,16 @@ export type CreateStandardSkillArgs = StandardBuilderArgs<'skill'> & {
|
||||
};
|
||||
|
||||
export const createStandardSkillFlatMetadata = ({
|
||||
context: { skillName, name, label, icon, description, content, isCustom },
|
||||
context: {
|
||||
skillName,
|
||||
name,
|
||||
label,
|
||||
icon,
|
||||
description,
|
||||
content,
|
||||
isCustom,
|
||||
isActive = true,
|
||||
},
|
||||
workspaceId,
|
||||
twentyStandardApplicationId,
|
||||
now,
|
||||
@@ -37,7 +47,7 @@ export const createStandardSkillFlatMetadata = ({
|
||||
description,
|
||||
content,
|
||||
isCustom,
|
||||
isActive: true,
|
||||
isActive,
|
||||
workspaceId,
|
||||
applicationId: twentyStandardApplicationId,
|
||||
applicationUniversalIdentifier:
|
||||
|
||||
Reference in New Issue
Block a user