Explore what Kijulo can do, where each capability lives, and the shortest path to using it. Every use case links back here when a step needs one of these building blocks.
Explore features by domain
DB
Organize
Create structured data
Reusable setup
Model the people, projects, notes, opportunities, or any other things you care about as typed, searchable rows.
Doing it manually
◷20–45 min
Delegated to an agent
✦3–8 min
See a prompt example+
Help me: Create structured data.
Goal: Model the people, projects, notes, opportunities, or any other things you care about as typed, searchable rows.
Use the relevant data and tools available in Data → New entity type. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Data → New entity type
How to get started
1Name the kind of thing you want to track.+
Where: Open Data → New entity type.
Prepare: Make sure the required source rows, files, or URLs are available, then Name the kind of thing you want to track.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Add the fields, types, validation, and relationships it needs.+
Where: Continue in Data → New entity type.
Configure: Add the fields, types, validation, and relationships it needs.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Create rows manually, import them, or let an agent propose them.+
Where: Complete this step in Data → New entity type.
Run: Create rows manually, import them, or let an agent propose them.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Turn YouTube transcripts, PDFs, scans, images, and pasted text into editable markdown, then extract reusable knowledge from it.
Doing it manually
◷15–60 min
Delegated to an agent
✦2–8 min
See a prompt example+
Help me: Ingest a video, PDF, or document.
Goal: Turn YouTube transcripts, PDFs, scans, images, and pasted text into editable markdown, then extract reusable knowledge from it.
Use the relevant data and tools available in Smart Ingest. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Smart Ingest
How to get started
1Submit a YouTube URL, PDF, or document folder; scanned PDFs can be processed with OCR.+
Where: Open Smart Ingest.
Prepare: Make sure the required source rows, files, or URLs are available, then Submit a YouTube URL, PDF, or document folder; scanned PDFs can be processed with OCR.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Wait for Kijulo to preserve the source and create a normalized markdown artifact.+
Where: Continue in Smart Ingest.
Configure: Wait for Kijulo to preserve the source and create a normalized markdown artifact.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Open the markdown, review it, then extract facts, ideas, and relationships into Entity Types.+
Where: Complete this step in Smart Ingest.
Run: Open the markdown, review it, then extract facts, ideas, and relationships into Entity Types.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Give an agent explicit access to selected files and folders while keeping everything portable on disk.
Doing it manually
◷10–20 min
Delegated to an agent
✦2–5 min
See a prompt example+
Help me: Work with local files.
Goal: Give an agent explicit access to selected files and folders while keeping everything portable on disk.
Use the relevant data and tools available in Agent conversation → Context, and Settings → LLM → File access. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
1Add folders to the Read Allowlist and, when needed, the Write Allowlist.+
Where: Open Agent conversation → Context, and Settings → LLM → File access.
Prepare: Make sure the required source rows, files, or URLs are available, then Add folders to the Read Allowlist and, when needed, the Write Allowlist.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Start a conversation and select the exact files or folders in scope.+
Where: Continue in Agent conversation → Context, and Settings → LLM → File access.
Configure: Start a conversation and select the exact files or folders in scope.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Review every proposed file edit before applying it.+
Where: Complete this step in Agent conversation → Context, and Settings → LLM → File access.
Run: Review every proposed file edit before applying it.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Ask an AI to reason over only the rows and files you choose, with an explicit posture and bounded tools.
Doing it manually
◷30–90 min
Delegated to an agent
✦3–15 min
See a prompt example+
Help me: Start an agent conversation.
Goal: Ask an AI to reason over only the rows and files you choose, with an explicit posture and bounded tools.
Use the relevant data and tools available in Agent → New conversation. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Agent → New conversation
How to get started
1Choose a posture that matches how much the agent may do.+
Where: Open Agent → New conversation.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose a posture that matches how much the agent may do.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Select the Entity Types, rows, files, and folders it may use.+
Where: Continue in Agent → New conversation.
Configure: Select the Entity Types, rows, files, and folders it may use.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Write your goal, then review any proposed changes in the Review queue.+
Where: Complete this step in Agent → New conversation.
Run: Write your goal, then review any proposed changes in the Review queue.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Turn a useful sequence into a runnable recipe made of prompts, templates, and tool calls.
Doing it manually
◷30–60 min
Delegated to an agent
✦5–15 min
See a prompt example+
Help me: Create a Workflow.
Goal: Turn a useful sequence into a runnable recipe made of prompts, templates, and tool calls.
Use the relevant data and tools available in Library → Workflows. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Library → Workflows
How to get started
1Choose what triggers the Workflow: rows, files, or a tag.+
Where: Open Library → Workflows.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose what triggers the Workflow: rows, files, or a tag.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Add and order its template, tool-call, and user-prompt steps.+
Where: Continue in Library → Workflows.
Configure: Add and order its template, tool-call, and user-prompt steps.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Set tool and file permissions, test it, then run or schedule it.+
Where: Complete this step in Library → Workflows.
Run: Set tool and file permissions, test it, then run or schedule it.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Rank rows from one table against candidates in another using transparent, weighted criteria.
Doing it manually
◷30–60 min
Delegated to an agent
✦5–10 min
See a prompt example+
Help me: Create a Fit Plan.
Goal: Rank rows from one table against candidates in another using transparent, weighted criteria.
Use the relevant data and tools available in Fit Plans → New Fit Plan. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Fit Plans → New Fit Plan
How to get started
1Pick the source table and one or more target tables.+
Where: Open Fit Plans → New Fit Plan.
Prepare: Make sure the required source rows, files, or URLs are available, then Pick the source table and one or more target tables.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Map comparable fields and assign weights or matching rules.+
Where: Continue in Fit Plans → New Fit Plan.
Configure: Map comparable fields and assign weights or matching rules.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Run the plan, inspect the criterion breakdown, and optionally record results through an output binding.+
Where: Complete this step in Fit Plans → New Fit Plan.
Run: Run the plan, inspect the criterion breakdown, and optionally record results through an output binding.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
Run the Fit Plan and view the generated matches
PR
Think and decide
Create a Protocol
Reusable setup
Standardize reviews and decisions with reusable, typed questions instead of unstructured prose.
Doing it manually
◷20–45 min
Delegated to an agent
✦5–10 min
See a prompt example+
Help me: Create a Protocol.
Goal: Standardize reviews and decisions with reusable, typed questions instead of unstructured prose.
Use the relevant data and tools available in Library → Protocols → Create Protocol. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Library → Protocols → Create Protocol
How to get started
1Define the review or decision the Protocol should standardize.+
Where: Open Library → Protocols → Create Protocol.
Prepare: Make sure the required source rows, files, or URLs are available, then Define the review or decision the Protocol should standardize.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Add ordered steps with yes/no, scale, list, matrix, or free-text prompts.+
Where: Continue in Library → Protocols → Create Protocol.
Configure: Add ordered steps with yes/no, scale, list, matrix, or free-text prompts.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Run it for each case and keep the structured answers for comparison.+
Where: Complete this step in Library → Protocols → Create Protocol.
Run: Run it for each case and keep the structured answers for comparison.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Run a Workflow automatically on a calendar or cron schedule with controlled inputs and approval rules.
Doing it manually
◷10–20 min
Delegated to an agent
✦2–5 min
See a prompt example+
Help me: Schedule recurring work.
Goal: Run a Workflow automatically on a calendar or cron schedule with controlled inputs and approval rules.
Use the relevant data and tools available in Planning → New Schedule. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Planning → New Schedule
How to get started
1Select the Workflow and define its row, query, or folder inputs.+
Where: Open Planning → New Schedule.
Prepare: Make sure the required source rows, files, or URLs are available, then Select the Workflow and define its row, query, or folder inputs.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Choose a frequency, timezone, and optional model override.+
Where: Continue in Planning → New Schedule.
Configure: Choose a frequency, timezone, and optional model override.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Decide whether writes require approval, then monitor runs in Planning and Orchestration.+
Where: Complete this step in Planning → New Schedule.
Run: Decide whether writes require approval, then monitor runs in Planning and Orchestration.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Pull fresh data from an HTTP API on demand or on a schedule and map it into Kijulo rows.
Doing it manually
◷30–90 min
Delegated to an agent
✦10–25 min
See a prompt example+
Help me: Connect an external API.
Goal: Pull fresh data from an HTTP API on demand or on a schedule and map it into Kijulo rows.
Use the relevant data and tools available in Library → API Connectors. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Library → API Connectors
How to get started
1Choose a target Entity Type and configure the request and credential.+
Where: Open Library → API Connectors.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose a target Entity Type and configure the request and credential.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Use Dry run to inspect the response, then map response fields to columns.+
Where: Continue in Library → API Connectors.
Configure: Use Dry run to inspect the response, then map response fields to columns.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Call it from an agent or schedule it to keep rows current.+
Where: Complete this step in Library → API Connectors.
Run: Call it from an agent or schedule it to keep rows current.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Send templated, personalized messages to selected rows and keep outreach tied to structured data.
Doing it manually
◷45–120 min
Delegated to an agent
✦10–25 min
See a prompt example+
Help me: Run an outreach Campaign.
Goal: Send templated, personalized messages to selected rows and keep outreach tied to structured data.
Use the relevant data and tools available in Campaigns and Email Templates. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Campaigns and Email Templates
How to get started
1Create an Email Template with placeholders from your Entity Type.+
Where: Open Campaigns and Email Templates.
Prepare: Make sure the required source rows, files, or URLs are available, then Create an Email Template with placeholders from your Entity Type.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Choose the target rows or filtered audience for the Campaign.+
Where: Continue in Campaigns and Email Templates.
Configure: Choose the target rows or filtered audience for the Campaign.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Review the messages, send through a connected provider, and track outcomes.+
Where: Complete this step in Campaigns and Email Templates.
Run: Review the messages, send through a connected provider, and track outcomes.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
DOC
Organize
Search and synthesize documents
Repeatable action
Index source files so an agent can retrieve relevant passages and produce grounded summaries.
Doing it manually
◷30–120 min
Delegated to an agent
✦5–15 min
See a prompt example+
Help me: Search and synthesize documents.
Goal: Index source files so an agent can retrieve relevant passages and produce grounded summaries.
Use the relevant data and tools available in Files, Smart Ingest, and Agent conversations. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Files, Smart Ingest, and Agent conversations
How to get started
1Add source documents inside an allowed folder.+
Where: Open Files, Smart Ingest, and Agent conversations.
Prepare: Make sure the required source rows, files, or URLs are available, then Add source documents inside an allowed folder.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Convert scans or PDFs to markdown when needed and let Kijulo index them.+
Where: Continue in Files, Smart Ingest, and Agent conversations.
Configure: Convert scans or PDFs to markdown when needed and let Kijulo index them.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Start a scoped conversation and ask for a synthesis with links back to sources.+
Where: Complete this step in Files, Smart Ingest, and Agent conversations.
Run: Start a scoped conversation and ask for a synthesis with links back to sources.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Turn rows into focused tables, boards, filters, groups, and saved views for daily work.
Doing it manually
◷10–25 min
Delegated to an agent
✦2–5 min
See a prompt example+
Help me: Build a useful view.
Goal: Turn rows into focused tables, boards, filters, groups, and saved views for daily work.
Use the relevant data and tools available in Open an Entity Type → View switcher. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Open an Entity Type → View switcher
How to get started
1Choose table, board, tree, or calendar based on the job.+
Where: Open Open an Entity Type → View switcher.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose table, board, tree, or calendar based on the job.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Filter, sort, group, and show only the columns you need.+
Where: Continue in Open an Entity Type → View switcher.
Configure: Filter, sort, group, and show only the columns you need.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Save the configuration as a named view you can reopen.+
Where: Complete this step in Open an Entity Type → View switcher.
Run: Save the configuration as a named view you can reopen.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Plot rows with geographic fields, select areas, and analyze spatial groups.
Doing it manually
◷20–45 min
Delegated to an agent
✦5–10 min
See a prompt example+
Help me: Map and compare locations.
Goal: Plot rows with geographic fields, select areas, and analyze spatial groups.
Use the relevant data and tools available in Spatial. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Spatial
How to get started
1Add a geo_point field to an Entity Type and populate its rows.+
Where: Open Spatial.
Prepare: Make sure the required source rows, files, or URLs are available, then Add a geo_point field to an Entity Type and populate its rows.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Open Spatial and select the layer or dataset to display.+
Where: Continue in Spatial.
Configure: Open Spatial and select the layer or dataset to display.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Select areas, save views, or ask an agent to compare the selected rows.+
Where: Complete this step in Spatial.
Run: Select areas, save views, or ask an agent to compare the selected rows.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
CAL
Organize
Plan dates on the Calendar
Reusable setup
See deadlines, visits, events, and recurring work from multiple Entity Types in one calendar.
Doing it manually
◷15–30 min
Delegated to an agent
✦3–8 min
See a prompt example+
Help me: Plan dates on the Calendar.
Goal: See deadlines, visits, events, and recurring work from multiple Entity Types in one calendar.
Use the relevant data and tools available in Calendar. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Calendar
How to get started
1Add date or datetime fields to the Entity Types you want to plan.+
Where: Open Calendar.
Prepare: Make sure the required source rows, files, or URLs are available, then Add date or datetime fields to the Entity Types you want to plan.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Create a calendar layer for each relevant field.+
Where: Continue in Calendar.
Configure: Create a calendar layer for each relevant field.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Choose a time range and save useful layer combinations as views.+
Where: Complete this step in Calendar.
Run: Choose a time range and save useful layer combinations as views.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Keep working commands and operational procedures so agents can reuse what has already been verified.
Doing it manually
◷15–30 min
Delegated to an agent
✦3–8 min
See a prompt example+
Help me: Record a proven command or runbook.
Goal: Keep working commands and operational procedures so agents can reuse what has already been verified.
Use the relevant data and tools available in Command Book. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Command Book
How to get started
1Record the command or ordered procedure and the context where it applies.+
Where: Open Command Book.
Prepare: Make sure the required source rows, files, or URLs are available, then Record the command or ordered procedure and the context where it applies.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Verify it against reality and preserve the resulting evidence.+
Where: Continue in Command Book.
Configure: Verify it against reality and preserve the resulting evidence.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Let future agents look it up before guessing or repeating discovery work.+
Where: Complete this step in Command Book.
Run: Let future agents look it up before guessing or repeating discovery work.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
OK
Review and control
Review and approve agent changes
Repeatable action
Keep human control over proposed row, file, schema, and external changes before they take effect.
Doing it manually
◷10–30 min
Delegated to an agent
✦2–8 min
See a prompt example+
Help me: Review and approve agent changes.
Goal: Keep human control over proposed row, file, schema, and external changes before they take effect.
Use the relevant data and tools available in Review. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Review
How to get started
1Open the relevant Entities, Files, Schema, or Outbox tab.+
Where: Open Review.
Prepare: Make sure the required source rows, files, or URLs are available, then Open the relevant Entities, Files, Schema, or Outbox tab.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Inspect the proposed values, source, confidence, and any conflicts.+
Where: Continue in Review.
Configure: Inspect the proposed values, source, confidence, and any conflicts.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Edit if needed, then approve only the fields you want or reject the proposal.+
Where: Complete this step in Review.
Run: Edit if needed, then approve only the fields you want or reject the proposal.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Fetch fresh information automatically and record each result as structured, traceable rows in your database.
Doing it manually
◷30–90 min
Delegated to an agent
✦5–15 min
See a prompt example+
Help me: Call an API on a schedule.
Goal: Fetch fresh information automatically and record each result as structured, traceable rows in your database.
Use the relevant data and tools available in Library → API Connectors, then Planning → Schedules. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Library → API Connectors, then Planning → Schedules
How to get started
1Configure and dry-run the API request, authentication, pagination, and target table.+
Where: Open Library → API Connectors, then Planning → Schedules.
Prepare: Make sure the required source rows, files, or URLs are available, then Configure and dry-run the API request, authentication, pagination, and target table.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Map response fields to columns and choose a stable key for updates and deduplication.+
Where: Continue in Library → API Connectors, then Planning → Schedules.
Configure: Map response fields to columns and choose a stable key for updates and deduplication.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Attach the connector to a schedule, then review run history and failed records.+
Where: Complete this step in Library → API Connectors, then Planning → Schedules.
Run: Attach the connector to a schedule, then review run history and failed records.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
CLU
Think and decide
Identify clusters in your data
Repeatable action
Discover natural groups, recurring themes, and outliers across rows without classifying every item by hand.
Doing it manually
◷60–180 min
Delegated to an agent
✦5–20 min
See a prompt example+
Help me: Identify clusters in your data.
Goal: Discover natural groups, recurring themes, and outliers across rows without classifying every item by hand.
Use the relevant data and tools available in Agent conversation → selected table or view. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Agent conversation → selected table or view
How to get started
1Select the rows and meaningful fields the analysis should consider.+
Where: Open Agent conversation → selected table or view.
Prepare: Make sure the required source rows, files, or URLs are available, then Select the rows and meaningful fields the analysis should consider.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Ask the agent to propose clusters, labels, defining traits, and outliers with evidence.+
Where: Continue in Agent conversation → selected table or view.
Configure: Ask the agent to propose clusters, labels, defining traits, and outliers with evidence.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Review the grouping and save accepted cluster labels or relationships back to the table.+
Where: Complete this step in Agent conversation → selected table or view.
Run: Review the grouping and save accepted cluster labels or relationships back to the table.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
A/B
Think and decide
Compare subsets of your data
Repeatable action
Contrast cohorts, periods, regions, candidates, or any two filtered groups using the same explicit criteria.
Doing it manually
◷30–90 min
Delegated to an agent
✦5–15 min
See a prompt example+
Help me: Compare subsets of your data.
Goal: Contrast cohorts, periods, regions, candidates, or any two filtered groups using the same explicit criteria.
Use the relevant data and tools available in Saved Views → Agent conversation. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Saved Views → Agent conversation
How to get started
1Create a saved view or filter for each subset you want to compare.+
Where: Open Saved Views → Agent conversation.
Prepare: Make sure the required source rows, files, or URLs are available, then Create a saved view or filter for each subset you want to compare.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Define the measures, qualitative criteria, and time period that must stay consistent.+
Where: Continue in Saved Views → Agent conversation.
Configure: Define the measures, qualitative criteria, and time period that must stay consistent.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Ask the agent for differences, similarities, outliers, and source-linked conclusions.+
Where: Complete this step in Saved Views → Agent conversation.
Run: Ask the agent for differences, similarities, outliers, and source-linked conclusions.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
CSV
Organize
Import Excel or CSV data
Repeatable action
Bring spreadsheet data into typed Kijulo tables while preserving headers, detecting formats, and surfacing invalid rows.
Doing it manually
◷30–120 min
Delegated to an agent
✦5–20 min
See a prompt example+
Help me: Import Excel or CSV data.
Goal: Bring spreadsheet data into typed Kijulo tables while preserving headers, detecting formats, and surfacing invalid rows.
Use the relevant data and tools available in Data → Import. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Data → Import
How to get started
1Choose the Excel or CSV file and preview its sheets, headers, and detected values.+
Where: Open Data → Import.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose the Excel or CSV file and preview its sheets, headers, and detected values.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Map columns to an existing Entity Type or create a suitable structure for the import.+
Where: Continue in Data → Import.
Configure: Map columns to an existing Entity Type or create a suitable structure for the import.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Validate types and duplicates, import valid rows, then review anything that needs correction.+
Where: Complete this step in Data → Import.
Run: Validate types and duplicates, import valid rows, then review anything that needs correction.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
REL
Organize
Create relationships between data
Repeatable action
Connect people, projects, files, events, ideas, and other rows so context can be reused across tasks.
Doing it manually
◷20–60 min
Delegated to an agent
✦3–10 min
See a prompt example+
Help me: Create relationships between data.
Goal: Connect people, projects, files, events, ideas, and other rows so context can be reused across tasks.
Use the relevant data and tools available in Entity Type schema → Relationship field. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Entity Type schema → Relationship field
How to get started
1Choose the two Entity Types to connect and name what the relationship means.+
Where: Open Entity Type schema → Relationship field.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose the two Entity Types to connect and name what the relationship means.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Set its direction and cardinality, then add the relationship field to the schema.+
Where: Continue in Entity Type schema → Relationship field.
Configure: Set its direction and cardinality, then add the relationship field to the schema.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Link rows manually or let an agent propose matches for your review.+
Where: Complete this step in Entity Type schema → Relationship field.
Run: Link rows manually or let an agent propose matches for your review.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
WEB
Connect
Research the web into your database
Repeatable action
Let an agent search the web, extract relevant facts, and propose sourced rows instead of leaving findings in a chat.
Doing it manually
◷60–240 min
Delegated to an agent
✦10–30 min
See a prompt example+
Help me: Research the web into your database.
Goal: Let an agent search the web, extract relevant facts, and propose sourced rows instead of leaving findings in a chat.
Use the relevant data and tools available in Agent conversation → Web search tool. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Agent conversation → Web search tool
How to get started
1Select the target table and define the fields, scope, freshness, and trusted sources.+
Where: Open Agent conversation → Web search tool.
Prepare: Make sure the required source rows, files, or URLs are available, then Select the target table and define the fields, scope, freshness, and trusted sources.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Ask the agent to search, compare sources, and attach a URL and retrieval date to every claim.+
Where: Continue in Agent conversation → Web search tool.
Configure: Ask the agent to search, compare sources, and attach a URL and retrieval date to every claim.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Review duplicates and confidence, then approve the proposed rows and relationships.+
Where: Complete this step in Agent conversation → Web search tool.
Run: Review duplicates and confidence, then approve the proposed rows and relationships.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
N×
Automate
Run multiple agent conversations at once
Repeatable action
Delegate independent research or processing tasks concurrently and bring their structured results back into one review flow.
Doing it manually
◷60–180 min
Delegated to an agent
✦10–30 min
See a prompt example+
Help me: Run multiple agent conversations at once.
Goal: Delegate independent research or processing tasks concurrently and bring their structured results back into one review flow.
Use the relevant data and tools available in Agent → Conversations and Orchestration. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Agent → Conversations and Orchestration
How to get started
1Split the goal into independent scopes with clear inputs, outputs, and permissions.+
Where: Open Agent → Conversations and Orchestration.
Prepare: Make sure the required source rows, files, or URLs are available, then Split the goal into independent scopes with clear inputs, outputs, and permissions.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Start the conversations concurrently and monitor progress, tool calls, and failures.+
Where: Continue in Agent → Conversations and Orchestration.
Configure: Start the conversations concurrently and monitor progress, tool calls, and failures.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Review, reconcile, and approve their proposals before combining the results.+
Where: Complete this step in Agent → Conversations and Orchestration.
Run: Review, reconcile, and approve their proposals before combining the results.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
SRCH
Think and decide
Retrieve your personal data
Repeatable action
Find your own rows and files with exact filters, full-text search, or semantic meaning—even when you do not remember the wording.
Doing it manually
◷15–60 min
Delegated to an agent
✦1–5 min
See a prompt example+
Help me: Retrieve your personal data.
Goal: Find your own rows and files with exact filters, full-text search, or semantic meaning—even when you do not remember the wording.
Use the relevant data and tools available in Global Search and Agent conversation. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Global Search and Agent conversation
How to get started
1Choose whether the query needs exact fields, text matches, or semantic similarity.+
Where: Open Global Search and Agent conversation.
Prepare: Make sure the required source rows, files, or URLs are available, then Choose whether the query needs exact fields, text matches, or semantic similarity.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Limit the scope to the relevant tables and folders, then run the search.+
Where: Continue in Global Search and Agent conversation.
Configure: Limit the scope to the relevant tables and folders, then run the search.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Inspect the sources and continue with the selected results in a scoped conversation.+
Where: Complete this step in Global Search and Agent conversation.
Run: Inspect the sources and continue with the selected results in a scoped conversation.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
GO
Automate
Create a focused Agent Launcher
Reusable setup
Save a persona, model, tool preset, skills, and narrow context so repeated conversations start faster and use fewer tokens.
Doing it manually
◷15–30 min
Delegated to an agent
✦2–5 min
See a prompt example+
Help me: Create a focused Agent Launcher.
Goal: Save a persona, model, tool preset, skills, and narrow context so repeated conversations start faster and use fewer tokens.
Use the relevant data and tools available in Agent → Launchers. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Agent → Launchers
How to get started
1Name the recurring job and choose the agent persona, model, and instructions.+
Where: Open Agent → Launchers.
Prepare: Make sure the required source rows, files, or URLs are available, then Name the recurring job and choose the agent persona, model, and instructions.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Enable only the tools and skills it needs, and restrict its default data and file context.+
Where: Continue in Agent → Launchers.
Configure: Enable only the tools and skills it needs, and restrict its default data and file context.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Test the Launcher on a representative task, then reuse it whenever that job returns.+
Where: Complete this step in Agent → Launchers.
Run: Test the Launcher on a representative task, then reuse it whenever that job returns.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
TRUST
Review and control
Improve data reliability
Reusable setup
Add ownership and verification signals, then protect trusted files, rows, and tables from accidental changes.
Doing it manually
◷20–60 min
Delegated to an agent
✦5–15 min
See a prompt example+
Help me: Improve data reliability.
Goal: Add ownership and verification signals, then protect trusted files, rows, and tables from accidental changes.
Use the relevant data and tools available in Entity fields, file metadata, and lock controls. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Entity fields, file metadata, and lock controls
How to get started
1Record authorship and add reviewed and verified fields with clear definitions.+
Where: Open Entity fields, file metadata, and lock controls.
Prepare: Make sure the required source rows, files, or URLs are available, then Record authorship and add reviewed and verified fields with clear definitions.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Review existing records, preserve their sources, and set the appropriate reliability status.+
Where: Continue in Entity fields, file metadata, and lock controls.
Configure: Review existing records, preserve their sources, and set the appropriate reliability status.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Lock trusted files, rows, or tables and require explicit review before future changes.+
Where: Complete this step in Entity fields, file metadata, and lock controls.
Run: Lock trusted files, rows, or tables and require explicit review before future changes.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.
Natural next action
>_
Coding
Share a live terminal with your agent
Repeatable action
Watch the agent’s commands and output as they run, type into the same persistent terminal yourself, and deliberately share your own command results back with the agent.
Doing it manually
◷10–60 min
Delegated to an agent
✦2–15 min
See a prompt example+
Help me: Share a live terminal with your agent.
Goal: Watch the agent’s commands and output as they run, type into the same persistent terminal yourself, and deliberately share your own command results back with the agent.
Use the relevant data and tools available in Agent conversation → Terminal panel. Before making changes, inspect the available inputs and tell me what is missing. Work on one representative example first. Explain your proposed approach, preserve links to the source data, and send any write or external action to review before applying it.
Typical estimate for one setup or run. Data volume, complexity, and required human review can change the total time.
Where to find it
Agent conversation → Terminal panel
How to get started
1Open the Terminal panel in a conversation to follow live commands, output, working directory, and exit status while the agent works.+
Where: Open Agent conversation → Terminal panel.
Prepare: Make sure the required source rows, files, or URLs are available, then Open the Terminal panel in a conversation to follow live commands, output, working directory, and exit status while the agent works.
Check: Start with one representative example so you can adjust the setup safely.
Select the step again to collapse these instructions.
2Take control at any time and type your own commands into the same Terminal Session; both you and the agent keep the same shell state, current directory, and environment.+
Where: Continue in Agent conversation → Terminal panel.
Configure: Take control at any time and type your own commands into the same Terminal Session; both you and the agent keep the same shell state, current directory, and environment.
Check: Review every field, permission, and input, then use a small test case before applying it to all your data.
Select the step again to collapse these instructions.
3Select the Command Entries you want the agent to know about, attach them to your next message, and continue the conversation with the exact command and output included as context.+
Where: Complete this step in Agent conversation → Terminal panel.
Run: Select the Command Entries you want the agent to know about, attach them to your next message, and continue the conversation with the exact command and output included as context.
Verify: Inspect the result and its source data, then approve or save it only when it matches your expectations.
Select the step again to collapse these instructions.