Session Log Data

---
name: session-log-data
description: Describes the data files available in the coding agent environment after copilot-setup-steps runs. Use when analyzing downloaded session logs or aggregated usage data.
---

# Session Log Data Skill

This skill describes the data files that are automatically downloaded into the GitHub Copilot Coding Agent environment during setup. These files are available for analysis, reporting, and debugging tasks.

## When to Use This Skill

Use this skill when you need to:
- Analyze Copilot token usage patterns from downloaded data
- Generate reports or summaries from session logs or aggregated data
- Debug token tracking or usage issues using real data
- Understand model usage distribution across a team
- Calculate estimated costs from usage data

## Data Availability

These files are only present when the coding agent environment has Azure Storage configured via the `copilot` GitHub environment. If the files don't exist, Azure Storage is not configured for this repository.

## Data Sources

### 1. Session Log Files — `./session-logs/`

**What**: Raw GitHub Copilot Chat session log files downloaded from Azure Blob Storage. These contain the full conversation history including prompts, responses, model information, and tool calls.

**Structure**:
```
./session-logs/
└── {datasetId}/
    └── {machineId}/
        └── {YYYY-MM-DD}/
            ├── session-abc123.json
            └── session-def456.json
```

**Date range**: Last 7 days of session data.

**File format**: JSON files (decompressed from `.json.gz`). Each file contains a Copilot Chat session with this structure:

```json
{
  "requests": [
    {
      "message": {
        "parts": [{ "text": "user prompt text" }]
      },
      "response": [
        { "value": "assistant response text" }
      ],
      "result": {
        "metadata": {
          "modelId": "gpt-4o"
        }
      }
    }
  ]
}
```

**Key fields**:
- `requests[].message.parts[].text` — User input (input tokens)
- `requests[].response[].value` — Assistant output (output tokens)
- `requests[].result.metadata.modelId` — Model used
- `requests.length` — Number of interactions

**How to analyze**: Use `jq`, Node.js, or Python to parse and aggregate. Example:
```bash
# Count total interactions across all session files
find ./session-logs -name "*.json" -exec jq '.requests | length' {} \; | paste -sd+ | bc

# List all models used
find ./session-logs -name "*.json" -exec jq -r '.requests[].result.metadata.modelId // empty' {} \; | sort -u
```

### 2. Aggregated Usage Data — `./usage-data/usage-agg-daily.json`

**What**: Pre-aggregated daily token usage data from Azure Table Storage. This is the same data the extension syncs to the backend — rolled up by day, model, workspace, machine, and user.

**Date range**: Last 30 days (configurable via `COPILOT_TABLE_DATA_DAYS` environment variable).

**File format**: JSON array of usage entities:

```json
[
  {
    "partitionKey": "ds:default|d:2026-02-10",
    "rowKey": "m:gpt-4o|w:my-project|mc:machine123|u:user456",
    "datasetId": "default",
    "day": "2026-02-10",
    "model": "gpt-4o",
    "workspaceId": "my-project",
    "workspaceName": "My Project",
    "machineId": "machine123",
    "machineName": "My Laptop",
    "userId": "user456",
    "inputTokens": 15000,
    "outputTokens": 8000,
    "interactions": 42,
    "updatedAt": "2026-02-10T23:59:59.999Z"
  }
]
```

**Key fields**:
- `day` — Date in YYYY-MM-DD format
- `model` — AI model name (e.g., `gpt-4o`, `claude-3-5-sonnet-20241022`)
- `inputTokens` / `outputTokens` — Token counts for that day/model/workspace combination
- `interactions` — Number of Copilot interactions
- `workspaceName` — Human-readable workspace name
- `machineName` — Human-readable machine name
- `userId` — User identifier (if team sharing is enabled)

**How to analyze**: Load the JSON and aggregate. Example with Node.js:
```javascript
const data = require('./usage-data/usage-agg-daily.json');

// Total tokens by model
const byModel = {};
for (const row of data) {
  if (!byModel[row.model]) byModel[row.model] = { input: 0, output: 0, interactions: 0 };
  byModel[row.model].input += row.inputTokens;
  byModel[row.model].output += row.outputTokens;
  byModel[row.model].interactions += row.interactions;
}
console.log(byModel);
```

## Cost Estimation

Use the aggregated data together with pricing from `src/modelPricing.json`:

```javascript
const pricing = require('./src/modelPricing.json');
const data = require('./usage-data/usage-agg-daily.json');

let totalCost = 0;
for (const row of data) {
  const price = pricing.pricing[row.model];
  if (price) {
    totalCost += (row.inputTokens / 1_000_000) * price.inputCostPerMillion;
    totalCost += (row.outputTokens / 1_000_000) * price.outputCostPerMillion;
  }
}
console.log(`Estimated total cost: $${totalCost.toFixed(2)}`);
```

## Checking Data Availability

Before using the data, check if the files exist:

```bash
# Check for session logs
[ -d ./session-logs ] && echo "Session logs available" || echo "No session logs"

# Check for aggregated data
[ -f ./usage-data/usage-agg-daily.json ] && echo "Aggregated data available" || echo "No aggregated data"
```

If neither directory exists, Azure Storage is not configured. See the `azure-storage-loader` skill and `docs/BLOB-UPLOAD.md` for setup instructions.

## Related Files

- `docs/BLOB-UPLOAD.md` — Full blob upload documentation and setup guide
- `docs/BLOB-UPLOAD-QUICKSTART.md` — Quick start for blob upload and coding agent access
- `.github/skills/azure-storage-loader/SKILL.md` — Azure Table Storage loader skill
- `.github/skills/copilot-log-analysis/SKILL.md` — Session file analysis techniques
- `src/modelPricing.json` — Model pricing data for cost estimation
- `src/tokenEstimators.json` — Character-to-token estimation ratios
- `.github/workflows/copilot-setup-steps.yml` — Workflow that downloads the data