improve-context

name	improve_context
description	Use this skill when the user types /improve-context or asks to capture learnings, improve documentation, or enrich the knowledge base based on this conversation.
metadata	{"author":"analytics-agent","version":"1.0"}

/improve-context Workflow

When the user types /improve-context (or asks to "capture learnings", "improve docs", "build on this conversation", or similar), execute this workflow:

Step 1 — Identify gaps from this conversation

Review the full conversation history and identify:

• Missing documentation: Topics, metrics, or business concepts the user had to explain manually
• Ambiguous tables or columns: Cases where you were uncertain which table to use, or had to ask for clarification
• Failed context lookups: Places where search_documents or search returned no results for a topic that clearly has domain knowledge behind it
• SQL errors from schema confusion: Queries that failed because column names, data types, or table relationships were unclear
• Human corrections: Cases where the user pointed out a wrong table, wrong definition, or wrong interpretation

Look at patterns across the full conversation, not just the last message. The goal is to identify the root cause of struggles — usually a documentation or metadata gap.

Step 2 — Probe for existing documentation

For each significant gap you identified, call search_documents with the relevant topic or term to check:

• Does a document already exist that covers this gap?
• If yes, is it complete, or is it missing the specific detail that caused confusion?

This step is important: don't propose creating a document that already exists. Prefer proposing updates to existing docs over creating new ones when the topic is already covered.

Step 3 — Draft improvement proposals

Based on your findings, draft 3–5 concrete, numbered improvement proposals. Each proposal should be one of:

• New doc: A new document that doesn't exist yet (e.g. "How to analyze churn", a definition guide for "net ARR")
• Update existing doc: Add missing detail to a document that already exists
• Fix description: Correct or enrich a dataset or column description that was wrong, incomplete, or missing

Format each proposal clearly, like this:

1. [New doc] "Revenue Metrics Guide" — Define net ARR vs gross ARR and specify that the `revenue` table is the source of truth for ARR calculations.
2. [Update doc] "Orders FAQ" — Add: `deleted_at IS NULL` means the order is active; non-null means soft-deleted.
3. [Fix description] `orders.status` column — Current description is empty. Propose: values are 'pending', 'confirmed', 'shipped', 'cancelled', 'refunded'.

Keep each proposal to 1–2 sentences. Be specific about what to add or change.

Step 4 — Ask for approval

Present the numbered list and ask: "Which of these would you like me to publish? Reply with the numbers, 'all', or 'none'."

Do NOT call any write-back tools until the user explicitly approves.

Step 5 — Execute approved changes

For each approved proposal, follow the save_correction skill instructions to confirm the change with the user before writing:

• New doc → Use save_correction Mode 3: find the right parent folder first via search_documents, then call with doc_title, doc_body, parent_doc_urn
• Update existing doc → Use save_correction Mode 2: call with doc_urn, doc_title, doc_body (full updated body)
• Fix description → Use save_correction Mode 1: call with entity_urn, corrected_description, and field_path if field-level

After each write, report the URN and location so the user can find it in DataHub.

Step 6 — Graceful degradation (write-back not available)

If save_correction is not available (write-back not enabled), still complete Steps 1–4 fully. At Step 5, instead of calling tools, present the complete proposed document bodies as markdown the user can copy. Include a note:

DataHub write-back is not enabled — copy these and add them manually, or enable it in Settings → Connections.