conversation-review

---

name: conversation-review description: Use when a semantic/context layer curator wants to review past analysis conversations in bulk, categorize user feedback into actionable improvement areas, and apply targeted changes to the semantic model or context layer. Covers the full workflow from collecting conversations and classifying feedback through to creating action items and applying them on a branch.

Overview

This skill is for semantic and context layer curators who want to use accumulated user feedback to drive continuous improvement. It structures the work into two phases:

1. Categorize — review past conversations with feedback and classify each piece of feedback into an actionable improvement category
2. Act — turn the categorized feedback into a concrete list of changes, then apply them to the semantic model or context layer on a branch

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Prerequisites

Before starting, ensure a workspace and branch are set. Use get_session_workspace_and_branch to check the current context. If nothing is set, use list_workspaces and set_session_workspace_and_branch. See the workspace-branch skill for the full reference.

You will be reading the model as-is (use prod branch for reading), then applying changes on a development branch later.

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Phase 1: Collect Conversations

1.1 Fetch conversations with feedback

Use list_analysis_chats to retrieve past conversations. Focus on those that have received feedback — positive or negative.

list_analysis_chats(limit=50, offset=0)

Paginate with offset until you have covered the desired time window or conversation count. For each conversation, note:

• conversation_id
• title
• feedback (the stored feedback text, if any)
• agent and domain
• created_at and created_by

Filter to conversations with feedback. Skip conversations with no feedback unless the user explicitly wants to review all conversations.

If a conversation has no feedback or the stored feedback is inaccurate, use provide_analysis_feedback to record the correct label before categorizing:

provide_analysis_feedback(conversation_id="abc123", feedback="Data Issue: net revenue metric missing")
provide_analysis_feedback(conversation_id="def456", feedback="Good")

This keeps the stored feedback in sync with the categorization, so future reviews start from an accurate baseline.

1.2 Read the full conversation when needed

If the feedback text alone is not enough to understand what went wrong, retrieve the full conversation content with get_stored_conversation:

get_stored_conversation(conversation_id="abc123")

This returns all final responses and plan/interpretation messages. Read these to understand what the AI actually did — what question it interpreted, what approach it took, and what it concluded.

To also see individual analysis steps (useful when you want to identify which step produced wrong data), pass with_step_ids: true:

get_stored_conversation(conversation_id="abc123", with_step_ids=true)

This includes step_start and step_insight entries with step_id values you can then pass to get_analysis_step_details to retrieve the exact semantic query and SQL used in that step.

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Phase 2: Categorize Feedback

For each conversation with feedback, assign it to one or more of the following action categories. A single conversation can yield multiple categories.

Feedback → Action Category Mapping

Feedback Signal	Root Cause	Action Category
Data Issue: X metric doesn't exist	Missing aggregation	Add metric
Data Issue: wrong value / incorrect result	Metric or attribute SQL is wrong	Fix calculation
Data Issue: missing dimension / can't slice by X	Missing attribute	Add attribute
Wrong Judgement: used wrong field	Ambiguous names or descriptions	Improve metadata
Wrong Judgement: missed a standing rule	Missing instruction	Add instruction
Wrong Judgement: didn't follow a procedure	Missing analytical skill	Add skill
Wrong Judgement: lacked business context	Missing knowledge or memory	Add knowledge / memory
Wrong Judgement: wrong domain or agent	Agent misconfigured	Update agent
Chart Issue	Visualization only	No semantic action
Good / positive	Working as intended	No action
Other: X	Ambiguous	Manual review

Categorization Output

Produce a table summarizing all categorized feedback before proceeding:

Conversation	Feedback	Category	Proposed Change
abc123 — "Revenue by region"	Data Issue: net revenue metric missing	Add metric	Add net_revenue to orders entity
def456 — "Churn analysis"	Wrong Judgement: used gross revenue	Add instruction	"Always use orders.net_revenue for revenue questions"
ghi789 — "Top products"	Good	No action	—

Group the proposed changes by category to make patterns visible. If three conversations all flag the same missing metric, that is one action item, not three.

---

Phase 3: Build Action Items

Turn the categorized feedback into a concrete, prioritized action list. Each action item should be self-contained enough to execute in a single MCP call.

Action Item Format

For each item, record:

• Type: what kind of change (add_metric, fix_attribute, add_instruction, add_skill, add_knowledge, add_memory, update_agent, improve_description)
• Target: the entity, field, agent, or context item to create or update
• Evidence: the conversation IDs and feedback text that motivated this item
• What to do: a one-sentence description of the specific change

Action Item Types and Tools

Type	Tool(s)
Add metric	create_object with metric YAML
Add calculated attribute	create_object with calculated attribute YAML
Fix calculation	get_field to read current YAML → update_object with corrected SQL
Improve metadata	get_entity or get_field → update_object with improved description or display_name
Add instruction	create_context_item with subtype: instruction
Add skill	create_context_item with subtype: skill
Add knowledge	create_context_item with subtype: knowledge and external_source
Add memory	create_context_item with subtype: event
Update agent	get_agent → update_agent with corrected domain, context globs, or description

Prioritization

Present the action list sorted by impact × frequency:

1. Changes that would have fixed multiple conversations (high frequency)
2. Changes to core metrics or standing rules (high impact)
3. Minor metadata improvements and one-off additions

Review the list with the user before applying any changes. Ask: "Would you like to proceed with all items, or select a subset?"

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Phase 4: Apply Changes on a Branch

4.1 Choose a branch

Ask the user whether to apply changes on:

• A new branch — recommended for a significant batch of changes:

  create_workspace_branch(workspace_id="...", branch_name="conversation-review-<date>")
  

• An existing development branch — if work is already in progress:

  set_session_workspace_and_branch(workspace_id="...", branch_id="<branch>")
  

Never apply changes directly to prod.

4.2 Apply semantic layer changes

For each semantic layer action item, apply in this order to avoid dependency issues:

1. New entities (if any) — create_entity
2. New calculated attributes — create_object with calculated attribute YAML
3. New metrics — create_object with metric YAML
4. Updated calculations or metadata — update_object

After each create_object or update_object, display the returned ui_url to the user.

Refer to the attribute-creation, metric-creation, or entity-creation skills for the correct YAML structure.

4.3 Apply context layer changes

For each context layer action item:

1. Run list_context_items to check for existing items that overlap — update rather than duplicate.
2. Apply create_context_item or update_context_item.
3. Display the returned ui_url after each call.

Refer to the context-item-creation skill for naming conventions, folder structure, and type-specific parameters.

4.4 Validate

After all changes are applied, run validation on the affected objects. Use validate_object on any YAML you are unsure about before committing it. For a broader check, use the validation skill.

4.5 Review branch history and open a PR

Use get_branch_history to review what changed on the branch. Then:

create_pr_for_working_branch(
  title="Model improvements from conversation review — <date>",
  description="Changes derived from reviewing N conversations with feedback. Addresses: <list of categories>."
)

Display the returned PR URL to the user.

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Full Workflow Summary

set_session_workspace_and_branch (confirm prod for reading)
    │
    ▼
list_analysis_chats (paginate, collect all with feedback)
    │
    ▼
[For each conversation with feedback]
  → read feedback text
  → optionally: get_stored_conversation for full context
  → optionally: get_analysis_step_details for a specific step
    │
    ▼
Categorize feedback → produce categorization table
    │
    ▼
Build action item list → review with user
    │
    ▼
create_workspace_branch (or switch to existing dev branch)
    │
    ├── Semantic layer: create_object / update_object (entities, calculated attributes, metrics)
    ├── Context layer: create_context_item / update_context_item
    └── Agents: update_agent
    │
    ▼
validate_object (for any uncertain YAML)
    │
    ▼
get_branch_history (confirm what changed)
    │
    ▼
create_pr_for_working_branch → display PR URL

---

Tips

• Batch similar items. If five conversations all lack the same metric, create it once and note all five as the motivation.
• Prefer fixing the semantic layer over adding instructions. If a Data Issue can be resolved by correcting a metric's SQL, do that — don't add an instruction telling the AI to work around the wrong value.
• Instructions must be short. If the proposed instruction is longer than two sentences, consider whether it should be a skill instead. See the context-item-creation skill for the boundary.
• Check for existing items before creating. Run list_context_items and search_model before adding anything — duplicate instructions or metrics with slightly different names are worse than none.
• Start small. Apply the highest-priority items first, validate, then continue. A PR with five focused changes is easier to review than one with twenty.