cxas-loss-analysis

name	cxas-loss-analysis
description	Retrieves non-contained CCAI Insights conversations (losses), uses agent intelligence to cluster them into common failure patterns, and generates a markdown report. Use when you need to analyze failure patterns and build targeted regression/evaluation reports.

Insights Loss Analysis & Report Generator

This skill instructs you (the AI Agent) to retrieve recent conversations from CCAI Insights, isolate escalated/non-contained sessions (losses), analyze their root causes to group them into failure patterns, and write a professional Markdown report.

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Execution Routine

Follow these steps in exact sequence:

Step 1: Parameter Verification

Verify that the user has provided the following required parameters:

• project_id: GCP Project ID hosting Insights.
• location: Insights location (e.g., us).
• app_id: Target CXAS App ID (e.g., db9ee866-28db-458b-b835-78137c974779).
• output_dir: Directory where the final report and test cases will be saved.

And the following optional parameters if they wish to scope the analysis:

• start_time: RFC 3339 timestamp for start of time period (e.g., 2026-05-20T00:00:00Z).
• end_time: RFC 3339 timestamp for end of time period (e.g., 2026-05-26T23:59:59Z).
• filter: Custom API filter string to apply (overrides the default loss filter -labels.sessionContained="true").
• limit: Maximum conversations to retrieve and process (default: 500).

Step 2: Extract Loss Transcripts

Run the lightweight data-extraction script to dump the loss transcripts into chunked JSON files in your workspace.

Command Template:

python3 -P .agents/skills/cxas-loss-analysis/scripts/fetch_losses.py \
  --project-id "{project_id}" \
  --location "{location}" \
  --app-id "{app_id}" \
  --limit {limit} \
  --output-file "{output_dir}/raw_losses.json" \
  [--start-time "{start_time}"] \
  [--end-time "{end_time}"] \
  [--filter "{filter}"]

Note: Always run python using the virtual environment's executable with the -P flag (e.g., .venv/bin/python -P) to avoid path pollution.

Step 3: Read Transcripts & Summarize Escalations

Use the view_file or other file-reading tools to read the generated {output_dir}/raw_losses.json file. Extract the list of chunks (which contains paths to the chunked JSON files).

For each chunk file in the chunks list:

1. Read the chunk file to load the batch of transcripts.
2. For each conversation transcript: a. Analyze the conversation between the customer (user) and the virtual agent (agent). b. Identify if the user displayed "AI aversion":
   • Definition: Sessions where the user did not meaningfully engage with the agent or expressed a strong preference for a human agent (e.g., immediately asking for "human", "agent", "representative" in the first 1-2 turns without describing their issue, or explicitly stating they do not want to talk to an AI/robot).
   • If "AI aversion" is detected, mark this session as ignored from the core loss analysis. Note the reason (e.g., "AI aversion: User demanded human agent immediately"). c. For non-ignored genuine losses:
   • Identify why the conversation escalated or was not contained.
   • Formulate a concise, 1-sentence primary reason for failure/escalation (max 20 words). E.g., "Virtual agent failed to authenticate the user due to repeated pin entry errors."

Step 4: Cluster Failures into Loss Patterns

Review the complete list of genuine (non-ignored) failure reasons you generated in Step 3. Using your analytical capabilities, group these failure reasons into 8 to 10 distinct, mutually exclusive failure patterns to provide granular insights.

For each pattern, define:

1. Pattern ID: A simple key (e.g., pattern_1, pattern_2, ...).
2. Name: A short, descriptive name (e.g., "Authentication Loop", "Unsupported Customer Intent", "Agent Transfer on Disambiguation").
3. Description: A clear 1-2 sentence description explaining the pattern and what triggers it.

Step 5: Categorize All Sessions

Map every analyzed conversation_id to either:

• One of the 8 to 10 defined failure patterns.
• ignored_ai_aversion if the user displayed AI aversion.

Keep track of this mapping for the final report.

Step 6: Write the Markdown Report

Compile your analysis into a structured Markdown report and write it to {output_dir}/loss_patterns_report.md. Use the following structure:

# Loss Patterns Analysis Report

**Project**: `{project_id}`
**App ID**: `{app_id}`

## Executive Summary

A sample of up to {limit} conversations matching the filter was selected for detailed manual analysis and clustering to identify key patterns.

## Loss Patterns Distribution

| Pattern ID | Name | Count | Percentage of Genuine Losses |
| --- | --- | --- | --- |
| `pattern_1` | Pattern Name | Count | Pct% |
| ... | ... | ... | ... |

*Note: Ignored AI aversion sessions are excluded from the pattern distribution.*

## Detailed Patterns Breakdown

### `pattern_1`: Pattern Name

**Description**: Pattern description.
**Total Conversations**: Count

#### Examples & Failure Reasons:
- **Session `{conversation_id_1}`**: Failure reason from Step 3.
- **Session `{conversation_id_2}`**: Failure reason from Step 3.

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## Appendix: Ignored Sessions (AI Aversion)
The following sessions were ignored from the pattern analysis because the user displayed AI aversion:
- **Session `{conversation_id_3}`**: AI aversion reason (e.g., *"User demanded human agent immediately"*).
- **Session `{conversation_id_4}`**: AI aversion reason.

Step 7: Present Summary to User

Present a clear summary of your findings directly in the chat, pointing the user to {output_dir}/loss_patterns_report.md and highlighting the key patterns and the adjusted containment rate.