@ramtinj95/opencode-tokenscope

v1.5.1

Published

7 days ago

OpenCode plugin for detailed token usage analysis with breakdowns by category, visual charts, and subagent cost tracking

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ramtinj95

opencode opencode-plugin plugin tokens token-usage analysis cost-tracking

OpenCode-Tokenscope, Token Analyzer Plugin

Comprehensive token usage analysis and cost tracking for OpenCode AI sessions

Track and optimize your token usage across system prompts, user messages, tool outputs, and more. Get detailed breakdowns, accurate cost estimates, and visual insights for your AI development workflow.

Installation

Option 1: npm (Recommended)

Install globally:

npm install -g @ramtinj95/opencode-tokenscope

Add to your opencode.json (create one in your project root or ~/.config/opencode/opencode.json for global config):
```
{
  "$schema": "https://opencode.ai/config.json",
  "plugin": ["@ramtinj95/opencode-tokenscope"]
}
```
Create the /tokenscope command by creating ~/.config/opencode/command/tokenscope.md:

mkdir -p ~/.config/opencode/command
cat > ~/.config/opencode/command/tokenscope.md << 'EOF'
---
description: Analyze token usage across the current session with detailed breakdowns by category
---

Call the tokenscope tool directly without delegating to other agents.
Then cat the token-usage-output.txt. DONT DO ANYTHING ELSE WITH THE OUTPUT.
EOF

Restart OpenCode and run /tokenscope

To always get the latest version automatically, use @latest:

{
  "plugin": ["@ramtinj95/opencode-tokenscope@latest"]
}

Option 2: Install Script

curl -sSL https://raw.githubusercontent.com/ramtinJ95/opencode-tokenscope/main/plugin/install.sh | bash

Then restart OpenCode and run /tokenscope

Updating

If installed via npm:

| Config in opencode.json | Behavior | |---------------------------|----------| | "@ramtinj95/opencode-tokenscope" | Uses the version installed at install time. Never auto-updates. | | "@ramtinj95/opencode-tokenscope@latest" | Fetches latest version every time OpenCode starts. | | "@ramtinj95/[email protected]" | Pins to exact version 1.4.0. Never updates. |

To manually update:

npm update -g @ramtinj95/opencode-tokenscope

Or use @latest in your opencode.json to auto-update on OpenCode restart.

If installed via script:

Option 1: Local script (if you have the plugin installed)

bash ~/.config/opencode/plugin/install.sh --update

Option 2: Remote script (always works)

curl -sSL https://raw.githubusercontent.com/ramtinJ95/opencode-tokenscope/main/plugin/install.sh | bash -s -- --update

The --update flag skips dependency installation for faster updates.

Usage

Simply type in OpenCode:

/tokenscope

The plugin will:

Analyze the current session
Count tokens across all categories
Analyze all subagent (Task tool) child sessions recursively
Calculate costs based on API telemetry
Save detailed report to token-usage-output.txt

Options

sessionID: Analyze a specific session instead of the current one
limitMessages: Limit entries shown per category (1-10, default: 3)
includeSubagents: Include subagent child session costs (default: true)

Reading the Full Report

cat token-usage-output.txt

Features

Comprehensive Token Analysis

5 Category Breakdown: System prompts, user messages, assistant responses, tool outputs, and reasoning traces
Visual Charts: Easy-to-read ASCII bar charts with percentages and token counts
Smart Inference: Automatically infers system prompts from API telemetry (since they're not exposed in session messages)

Context Breakdown Analysis

System Prompt Components: See token distribution across base prompt, tool definitions, environment context, project tree, and custom instructions
Automatic Estimation: Estimates breakdown from cache_write tokens when system prompt content isn't directly available
Tool Count: Shows how many tools are loaded and their combined token cost

Tool Definition Cost Estimates

Per-Tool Estimates: Lists all enabled tools with estimated schema token costs
Argument Analysis: Infers argument count and complexity from actual tool calls in the session
Complexity Detection: Distinguishes between simple arguments and complex ones (arrays/objects)

Cache Efficiency Metrics

Cache Hit Rate: Visual display of cache read vs fresh input token distribution
Cost Savings: Calculates actual savings from prompt caching
Effective Rate: Shows what you're actually paying per token vs standard rates

Accurate Cost Tracking

41+ Models Supported: Comprehensive pricing database for Claude, GPT, DeepSeek, Llama, Mistral, and more
Cache-Aware Pricing: Properly handles cache read/write tokens with discounted rates
Session-Wide Billing: Aggregates costs across all API calls in your session

Subagent Cost Tracking

Child Session Analysis: Recursively analyzes all subagent sessions spawned by the Task tool
Aggregated Totals: Shows combined tokens, costs, and API calls across main session and all subagents
Per-Agent Breakdown: Lists each subagent with its type, token usage, cost, and API call count
Optional Toggle: Enable/disable subagent analysis with the includeSubagents parameter

Advanced Features

Tool Usage Stats: Track which tools consume the most tokens and how many times each is called
API Call Tracking: See total API calls for main session and subagents
Top Contributors: Identify the biggest token consumers
Model Normalization: Handles provider/model format automatically
Multi-Tokenizer Support: Uses official tokenizers (tiktoken for OpenAI, transformers for others)
Configurable Sections: Enable/disable analysis features via tokenscope-config.json

Skill Analysis

Available Skills: Shows all skills listed in the skill tool definition with their token cost
Loaded Skills: Tracks skills loaded during the session with call counts
Cumulative Token Tracking: Accurately counts token cost when skills are called multiple times

Understanding OpenCode Skill Behavior

This section explains how OpenCode handles skills and why the token counting works the way it does.

How Skills Work

Skills are on-demand instructions that agents can load via the skill tool. They have two token consumption points:

Available Skills List: Skill names and descriptions are embedded in the skill tool's description as XML. This is part of the system prompt and costs tokens on every API call.
Loaded Skill Content: When an agent calls skill({ name: "my-skill" }), the full SKILL.md content is loaded and returned as a tool result.

Why Multiple Skill Calls Multiply Token Cost

Important: OpenCode does not deduplicate skill content. Each time the same skill is called, the full content is added to context again as a new tool result.

This means if you call skill({ name: "git-release" }) 3 times and it contains 500 tokens:

Total context cost = 500 × 3 = 1,500 tokens

This behavior is by design in OpenCode. You can verify this in the source code:

| Component | Source Link | |-----------|-------------| | Skill tool execution | packages/opencode/src/tool/skill.ts | | Tool result handling | packages/opencode/src/session/message-v2.ts | | Skill pruning protection | packages/opencode/src/session/compaction.ts |

Skill Content is Protected from Pruning

OpenCode protects skill tool results from being pruned during context management. From the compaction.ts source:

const PRUNE_PROTECTED_TOOLS = ["skill"]

This means loaded skill content stays in context for the duration of the session (unless full session compaction/summarization occurs).

Recommendations

Call skills sparingly: Since each call adds full content, avoid calling the same skill multiple times
Monitor skill token usage: Use TokenScope to see which skills consume the most tokens
Consider skill size: Large skills (1000+ tokens) can quickly inflate context when called repeatedly

Example Output

═══════════════════════════════════════════════════════════════════════════
Token Analysis: Session ses_50c712089ffeshuuuJPmOoXCPX
Model: claude-opus-4-5
═══════════════════════════════════════════════════════════════════════════

TOKEN BREAKDOWN BY CATEGORY
─────────────────────────────────────────────────────────────────────────
Estimated using tokenizer analysis of message content:

Input Categories:
  SYSTEM    ██████████████░░░░░░░░░░░░░░░░    45.8% (22,367)
  USER      ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░        0.8% (375)
  TOOLS     ████████████████░░░░░░░░░░░░░░    53.5% (26,146)

  Subtotal: 48,888 estimated input tokens

Output Categories:
  ASSISTANT ██████████████████████████████     100.0% (1,806)

  Subtotal: 1,806 estimated output tokens

Local Total: 50,694 tokens (estimated)

TOOL USAGE BREAKDOWN
─────────────────────────────────────────────────────────────────────────
bash                 ██████████░░░░░░░░░░░░░░░░░░░░     34.0% (8,886)    4x
read                 ██████████░░░░░░░░░░░░░░░░░░░░     33.1% (8,643)    3x
task                 ████████░░░░░░░░░░░░░░░░░░░░░░     27.7% (7,245)    4x
webfetch             █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░      4.9% (1,286)    1x
tokenscope           ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░         0.3% (75)    2x
batch                ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░         0.0% (11)    1x

TOP CONTRIBUTORS
─────────────────────────────────────────────────────────────────────────
• System (inferred from API)   22,367 tokens (44.1%)
• bash                         8,886 tokens (17.5%)
• read                         8,643 tokens (17.0%)
• task                         7,245 tokens (14.3%)
• webfetch                     1,286 tokens (2.5%)

═══════════════════════════════════════════════════════════════════════════
MOST RECENT API CALL
─────────────────────────────────────────────────────────────────────────

Raw telemetry from last API response:
  Input (fresh):              2 tokens
  Cache read:            48,886 tokens
  Cache write:               54 tokens
  Output:                   391 tokens
  ───────────────────────────────────
  Total:                 49,333 tokens

═══════════════════════════════════════════════════════════════════════════
SESSION TOTALS (All 15 API calls)
─────────────────────────────────────────────────────────────────────────

Total tokens processed across the entire session (for cost calculation):

  Input tokens:              10 (fresh tokens across all calls)
  Cache read:           320,479 (cached tokens across all calls)
  Cache write:           51,866 (tokens written to cache)
  Output tokens:          3,331 (all model responses)
  ───────────────────────────────────
  Session Total:        375,686 tokens (for billing)

═══════════════════════════════════════════════════════════════════════════
ESTIMATED SESSION COST (API Key Pricing)
─────────────────────────────────────────────────────────────────────────

You appear to be on a subscription plan (API cost is $0).
Here's what this session would cost with direct API access:

  Input tokens:              10 × $5.00/M  = $0.0001
  Output tokens:          3,331 × $25.00/M  = $0.0833
  Cache read:           320,479 × $0.50/M  = $0.1602
  Cache write:           51,866 × $6.25/M  = $0.3242
─────────────────────────────────────────────────────────────────────────
ESTIMATED TOTAL: $0.5677

Note: This estimate uses standard API pricing from models.json.
Actual API costs may vary based on provider and context size.

═══════════════════════════════════════════════════════════════════════════
CONTEXT BREAKDOWN (Estimated from cache_write tokens)
─────────────────────────────────────────────────────────────────────────

  Base System Prompt   ████████████░░░░░░░░░░░░░░░░░░   ~42,816 tokens
  Tool Definitions (14)██████░░░░░░░░░░░░░░░░░░░░░░░░    ~4,900 tokens
  Environment Context  █░░░░░░░░░░░░░░░░░░░░░░░░░░░░░      ~150 tokens
  Project Tree         ████░░░░░░░░░░░░░░░░░░░░░░░░░░    ~4,000 tokens
  ───────────────────────────────────────────────────────────────────────
  Total Cached Context:                                  ~51,866 tokens

  Note: Breakdown estimated from first cache_write. Actual distribution may vary.

═══════════════════════════════════════════════════════════════════════════
TOOL DEFINITION COSTS (Estimated from argument analysis)
─────────────────────────────────────────────────────────────────────────

  Tool                Est. Tokens   Args   Complexity
  ───────────────────────────────────────────────────────────────────────
  task                       ~480      3   complex (arrays/objects)
  batch                      ~410      1   complex (arrays/objects)
  edit                       ~370      4   simple
  read                       ~340      3   simple
  bash                       ~340      3   simple
  ───────────────────────────────────────────────────────────────────────
  Total:                   ~4,520 tokens (14 enabled tools)

  Note: Estimates inferred from tool call arguments in this session.
        Actual schema tokens may vary +/-20%.

═══════════════════════════════════════════════════════════════════════════
CACHE EFFICIENCY
─────────────────────────────────────────────────────────────────────────

  Token Distribution:
    Cache Read:           320,479 tokens   ████████████████████████████░░  86.2%
    Fresh Input:           51,320 tokens   ████░░░░░░░░░░░░░░░░░░░░░░░░░░  13.8%
  ───────────────────────────────────────────────────────────────────────
  Cache Hit Rate:      86.2%

  Cost Analysis (claude-opus-4-5 @ $5.00/M input, $0.50/M cache read):
    Without caching:   $1.8590  (371,799 tokens x $5.00/M)
    With caching:      $0.4169  (fresh x $5.00/M + cached x $0.50/M)
  ───────────────────────────────────────────────────────────────────────
  Cost Savings:        $1.4421  (77.6% reduction)
  Effective Rate:      $1.12/M tokens  (vs. $5.00/M standard)

═══════════════════════════════════════════════════════════════════════════
SUBAGENT COSTS (4 child sessions, 23 API calls)
─────────────────────────────────────────────────────────────────────────

  docs                         $0.3190  (194,701 tokens, 8 calls)
  general                      $0.2957  (104,794 tokens, 4 calls)
  docs                         $0.2736  (69,411 tokens, 4 calls)
  general                      $0.5006  (197,568 tokens, 7 calls)
─────────────────────────────────────────────────────────────────────────
Subagent Total:            $1.3888  (566,474 tokens, 23 calls)

═══════════════════════════════════════════════════════════════════════════
SUMMARY
─────────────────────────────────────────────────────────────────────────

                          Cost        Tokens          API Calls
  Main session:      $    0.5677       375,686            15
  Subagents:         $    1.3888       566,474            23
─────────────────────────────────────────────────────────────────────────
  TOTAL:             $    1.9565       942,160            38

═══════════════════════════════════════════════════════════════════════════

Supported Models

41+ models with accurate pricing:

Claude Models

Claude Opus 4.5, 4.1, 4
Claude Sonnet 4, 4-5, 3.7, 3.5, 3
Claude Haiku 4-5, 3.5, 3

OpenAI Models

GPT-4, GPT-4 Turbo, GPT-4o, GPT-4o Mini
GPT-3.5 Turbo
GPT-5 and all its variations

Other Models

DeepSeek (R1, V2, V3)
Llama (3.1, 3.2, 3.3)
Mistral (Large, Small)
Qwen, Kimi, GLM, Grok
And more...

Free/Open models are marked with zero pricing.

Configuration

The plugin includes a tokenscope-config.json file with these defaults:

{
  "enableContextBreakdown": true,
  "enableToolSchemaEstimation": true,
  "enableCacheEfficiency": true,
  "enableSubagentAnalysis": true,
  "enableSkillAnalysis": true
}

Set any option to false to hide that section from the output.

Troubleshooting

Command `/tokenscope` Not Appearing

Verify tokenscope.md exists:

ls ~/.config/opencode/command/tokenscope.md

If missing, create it (see Installation step 3)
Restart OpenCode completely

Wrong Token Counts

The plugin uses API telemetry (ground truth). If counts seem off:

Expected ~2K difference from TUI: Plugin analyzes before its own response is added
Model detection: Check that the model name is recognized in the output

Privacy & Security

All processing is local: No session data sent to external services
Open source: Audit the code yourself

Contributing

Contributions welcome! Ideas for enhancement:

Historical trend analysis
Export to CSV/JSON/PDF
Optimization suggestions
Custom categorization rules
Real-time monitoring with alerts
Compare sessions
Token burn rate calculation

Support

Issues: GitHub Issues
Discussions: GitHub Discussions