context-engineerContext window optimizer — analyze, audit, and optimize your agent's context utilization. Know exactly where your tokens go before they're sent.
Install via ClawdBot CLI:
clawdbot install tkuehnl/context-engineerUse this skill when the user wants to:
# Analyze workspace context files — token counts, efficiency scores, recommendations
python3 skills/context-engineer/context.py analyze --workspace ~/.openclaw/workspace
# Analyze with a custom budget and save a snapshot for later comparison
python3 skills/context-engineer/context.py analyze --workspace ~/.openclaw/workspace --budget 128000 --snapshot before.json
# Audit tool definitions for overhead and overlap
python3 skills/context-engineer/context.py audit-tools --config ~/.openclaw/openclaw.json
# Generate a comprehensive context engineering report
python3 skills/context-engineer/context.py report --workspace ~/.openclaw/workspace --format terminal
# Compare two snapshots to see projected token savings
python3 skills/context-engineer/context.py compare --before before.json --after after.json--workspace PATH — Path to workspace directory (default: ~/.openclaw/workspace)--config PATH — Path to OpenClaw config file (default: ~/.openclaw/openclaw.json)--budget N — Context window token budget (default: 200000)--snapshot FILE — Save analysis snapshot to FILE for later comparison--format terminal — Output format (currently: terminal)Generated Mar 1, 2026
A company building AI-powered customer service agents uses this skill to audit and optimize system prompts and tool definitions, ensuring the agent stays within token limits while handling complex queries efficiently. It helps identify redundant tool overhead and compress memory files to maximize context for live interactions.
An edtech startup deploys AI tutors that require extensive knowledge bases; this skill analyzes workspace files like MEMORY.md to remove bloat and optimize token usage, allowing the tutor to reference more educational content without exceeding context budgets. It enables before/after comparisons to track efficiency gains.
A large corporation integrates AI agents into internal workflows for data processing; this skill audits tool definitions for redundancy and analyzes system prompts to reduce token consumption, ensuring the agent can handle large datasets and complex instructions within model constraints. It provides reports to measure optimization progress.
Developers creating AI-powered Discord bots use this skill to optimize context utilization by analyzing skill overhead and tool definitions, ensuring the bot responds quickly and accurately within token limits during community interactions. It helps identify unused tools and compress prompts for better performance.
Offer this skill as part of a subscription-based platform where users pay monthly for access to context engineering tools, reports, and analytics. Revenue comes from tiered plans based on usage levels, such as number of analyses or workspace sizes, targeting AI developers and enterprises.
Provide professional services using this skill to audit and optimize clients' AI agents, charging per project or hourly for context engineering reports and implementation support. Revenue is generated from one-time engagements or retainer contracts with businesses seeking to reduce token costs and improve performance.
Release this skill as a free open-source tool to attract users, then monetize through premium features like advanced analytics, automated optimization, or integration with proprietary platforms. Revenue streams include paid upgrades, enterprise licenses, and partnerships with AI infrastructure providers.
💬 Integration Tip
Start by running the analyze command on your workspace to get a baseline report, then use the audit-tools command to identify and remove redundant tool definitions for immediate token savings.
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