Agent skill

Session Log Data

by rajbos in rajbos/ai-engineering-fluency

Describes the data files available in the coding agent environment after copilot-setup-steps runs.

MITAuto-check passedDevelopment

Install Session Log Data

skills CLI

$ npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install rajbos/ai-engineering-fluency session-log-data --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/rajbos/ai-engineering-fluency.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/session-log-data .claude/skills/session-log-data && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "session-log-data" agent skill from https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data into .claude/skills/session-log-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "session-log-data", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install rajbos/ai-engineering-fluency session-log-data --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rajbos/ai-engineering-fluency.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.claude/skills/session-log-data .agents/skills/session-log-data && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "session-log-data" agent skill from https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data into .agents/skills/session-log-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "session-log-data", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install rajbos/ai-engineering-fluency session-log-data --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rajbos/ai-engineering-fluency.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.claude/skills/session-log-data .cursor/skills/session-log-data && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "session-log-data" agent skill from https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data into .cursor/skills/session-log-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "session-log-data", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/rajbos/ai-engineering-fluency.git --path .claude/skills/session-log-data

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install rajbos/ai-engineering-fluency session-log-data --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rajbos/ai-engineering-fluency.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.claude/skills/session-log-data .gemini/skills/session-log-data && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "session-log-data" agent skill from https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data into .gemini/skills/session-log-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "session-log-data", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install rajbos/ai-engineering-fluency session-log-data

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/rajbos/ai-engineering-fluency.git skills-src && mkdir -p .github/skills && cp -r skills-src/.claude/skills/session-log-data .github/skills/session-log-data && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "session-log-data" agent skill from https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data into .github/skills/session-log-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "session-log-data", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install rajbos/ai-engineering-fluency session-log-data --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/rajbos/ai-engineering-fluency.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.claude/skills/session-log-data .opencode/skills/session-log-data && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "session-log-data" agent skill from https://github.com/rajbos/ai-engineering-fluency/tree/main/.claude/skills/session-log-data into .opencode/skills/session-log-data/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "session-log-data", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: session-log-data
GitHub stars: 116
Token cost: ~1.7k tokens
SKILL.md length: 481 words
Files: 1
Skills in repo: 21
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Describes the data files available in the coding agent environment after copilot-setup-steps runs.

Works in 2 steps: Session Log Files — ./session-logs/ → Aggregated Usage Data —…
Analyzing downloaded session logs
SKILL.md covers When to Use This Skill, Data Availability, Data Sources and Cost Estimation, plus 2 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Session Log Data is an agent skill from rajbos/ai-engineering-fluency. 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.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. It works with Microsoft Azure. The repository describes itself as: Extension that shows information about the estimated token usage and more of AI in editors/CLI's. The licence is MIT.

When your agent uses it

Analyzing downloaded session logs
Aggregated usage data

Example prompts

“Use the session-log-data skill to describe the data files available in the coding agent environment after copilot-setup-steps runs”
“/session-log-data”

Requirements

Python 3
Node.js

Workflow steps

2 steps, taken from the step headings in SKILL.md.

Session Log Files — ./session-logs/
Aggregated Usage Data — ./usage-data/usage-agg-daily.json

What it can do on your machine

Read from SKILL.md and the folder at commit 64b51e6. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json, javascript and bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Session Log Data loads about 1.7k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 481 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~46
When it runs · the whole SKILL.md, loaded when a task matches: ~1.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from rajbos/ai-engineering-fluency at commit 64b51e6, republished under its MIT licence (© rajbos). 481 words, ~1,706 tokens.

Download SKILL.mdSave it as .claude/skills/session-log-data/SKILL.md (or your agent's skills folder).

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

Editor Type Manifest — `./session-logs/.editor-types.json`

What: A JSON manifest mapping each downloaded session file's relative path to its source editor type (e.g., "VS Code", "Copilot CLI", "JetBrains"). Built from blob metadata during the download workflow.

Format:

json

{
  "default/machineId/2026-09-14/session-abc123.json": "VS Code",
  "default/machineId/2026-09-14/events.jsonl": "Copilot CLI"
}

Usage: Load the manifest to classify session files by editor without relying on filename heuristics or content sniffing.

javascript

const editorTypes = require('./session-logs/.editor-types.json');
// editorTypes['default/machineId/2026-09-14/session-abc123.json'] === 'VS Code'

Note: Only blobs uploaded after the editorType metadata feature was added will have entries. Older uploads will be absent from the manifest.

Show full SKILL.md (195 more words)Show less

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/features/BLOB-UPLOAD.md for setup instructions.

docs/features/BLOB-UPLOAD.md — Full blob upload documentation and setup guide
docs/features/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

Files

Just SKILL.md in .claude/skills/session-log-data of rajbos/ai-engineering-fluency.

Open the folder on GitHubat commit 64b51e6

Compare with similar skills

Session Log Data next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Session Log Data compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Session Log Data this skillrajbos/ai-engineering-fluency	116	—	~1.7k	Automated safety check: Pass	MIT	today
Microsoft Code ReferenceMicrosoftDocs/mcp	1.9k	4 repos	~1.1k	Automated safety check: Pass	CC-BY-4.0	today
DeepChat Provider IntegrationThinkInAIXYZ/deepchat	6.4k	1 repos	~1.2k	Automated safety check: Pass	Apache-2.0	today
Version ReleaseNG-ZORRO/ng-zorro-antd	9.2k	—	~3.1k	Automated safety check: Pass	MIT	today
Azdo Internalmicrosoft/aspire	6.3k	—	~4.5k	Automated safety check: Pass	MIT	today
Drawio Azuresparklabx/drawio-ai-kit	652	1 repos	~1.6k	Automated safety check: Pass	MIT	5 days ago

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Works with

Microsoft Azure

Questions about Session Log Data

What does Session Log Data do?

Describes the data files available in the coding agent environment after copilot-setup-steps runs. Session Log Data is an agent skill from rajbos/ai-engineering-fluency. Describes the data files available in the coding agent environment after copilot-setup-steps runs.

When should I use Session Log Data?

Session Log Data fits situations like: analyzing downloaded session logs; aggregated usage data.

How do I install Session Log Data in Claude Code?

Run `npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a claude-code`. Or copy the skill folder (.claude/skills/session-log-data in rajbos/ai-engineering-fluency) into .claude/skills/session-log-data in your project. Claude Code loads it when a task matches its description.

How do I install Session Log Data in Codex?

Run `npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a codex`. Or copy the skill folder (.claude/skills/session-log-data in rajbos/ai-engineering-fluency) into .agents/skills/session-log-data in your project. Codex loads it when a task matches its description.

Can I use Session Log Data in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rajbos/ai-engineering-fluency --skill session-log-data -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/session-log-data, .gemini/skills/session-log-data, .github/skills/session-log-data and .opencode/skills/session-log-data in your project.

What does Session Log Data need to run?

SKILL.md names no scripts, command-line tools or credentials: Session Log Data is instructions for the agent only. Our summary lists: Python 3; Node.js.

Does Session Log Data access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Session Log Data safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Session Log Data use?

Session Log Data is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Session Log Data use?

About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Session Log Data?

Skills that share tags, products or a category with Session Log Data: Microsoft Code Reference (MicrosoftDocs/mcp, 1.9k stars), DeepChat Provider Integration (ThinkInAIXYZ/deepchat, 6.4k stars), Version Release (NG-ZORRO/ng-zorro-antd, 9.2k stars) and Azdo Internal (microsoft/aspire, 6.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Session Log Data?

rajbos (a GitHub user) maintains it in rajbos/ai-engineering-fluency, which has 116 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on October 8, 2026.

Source: rajbos/ai-engineering-fluency on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Session Log Data

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Session Log Data Skill

When to Use This Skill

Data Availability

Data Sources

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

Editor Type Manifest — ./session-logs/.editor-types.json

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

Cost Estimation

Checking Data Availability

Related Files

Files

Compare with similar skills

Similar skills

More from rajbos/ai-engineering-fluency

Related

Questions about Session Log Data

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

Editor Type Manifest — `./session-logs/.editor-types.json`

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