burnlens

    1

    Audit Claude Code token usage, identify high-cost MCP servers, and apply automated fixes to lower API bills.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+22 more

    burnlens

    Example session with this skill installed

    Show me where my tokens went in the last few sessions and help me cut the costs.

    • Read your context and instructions
    • Compiled the burnlens

    I have analyzed your Claude Code transcripts. Total usage reached 1.2M tokens ($18.40 equivalent) with a 42% cache hit rate. The primary cost driver is the postgres-mcp server, which accounts for 60% of context overhead due to large schema fetches.

    Token Usage Analysis

    CategoryTokensCost (Est)Primary Driver
    MCP Servers720,000$10.80postgres-mcp (schema introspection)
    Subagents240,000$3.60research-agent (duplicate searches)
    Tool Calls150,000$2.25ls -R calls in large directories
    System/Base90,000$1.75Baseline context

    Recommended Optimizations

    Restrict MCP Scope: The postgres-mcp server is fetching full table schemas on every query. I can update your .claude/settings.local.json to include a table_filter.
    2.

    Ignore Directories: 15% of your tokens are spent indexing node_modules. I can add this to your .claudeignore.
    3.

    Subagent Throttling: The research-agent is recursing too deeply. I can modify the CLAUDE.md instructions to limit search depth.

    Next steps

    • Run python3 "burnlens/scripts/run.py" fixes to apply the MCP filtering.
    • Open the full dashboard at ~/.burnlens/dashboard.html for a per-session breakdown.
    • Check savings in 3 sessions to see the cost reduction impact.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Claude Code token consumption can spiral when MCP servers, subagents, and heavy context accumulation go unchecked. Burnlens parses local session transcripts to pinpoint exactly where your budget is going and provides actionable fixes to reduce overhead.

    What it does

    • Cost breakdown by work type, specific tools, MCP servers, and subagents.
    • Cache analysis to identify hit rates and optimization opportunities.
    • Usage dashboard generation in HTML and plain-text formats for local review.
    • Automated fixes that modify project settings or documentation to trim context.
    • Savings tracking to measure the impact of applied optimizations over time.

    How it works

    1. Analyze usage by running the dashboard command to parse ~/.claude/projects transcripts.
    2. Review insights to identify high-cost tools or subagents via the summary and HTML report.
    3. Execute fixes to generate a list of recommendations for project-level token reduction.
    4. Track savings to compare before and after metrics once new sessions have been processed.

    Frameworks & tools

    This skill requires Python 3.8 or newer. It processes local Claude Code transcripts stored in the standard configuration directories and operates entirely offline.

    Why this beats prompting it yourself

    Raw transcripts are massive and unstructured. Burnlens automates the parsing, categorization, and cost-calculation logic that a manual prompt cannot handle without hitting context limits itself.

    Use cases

    • Identify which MCP server is inflating context windows.
    • Generate a visual breakdown of project spend for cost management.
    • Apply automated rules to .claude/settings.local.json to cap token usage.
    • Validate if recent workflow changes actually lowered your API bill.

    Known limitations

    Requires local access to Claude Code transcript directories. Savings metrics require a few sessions of post-fix usage to calculate accurately.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Requires

    W3
    Python

    Listed5 days ago

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