cortex-tms
🥈SilverCortex TMS is an AI governance platform that reduces AI API costs by 40-60% through intelligent context management. It includes an interactive CLI tool and high-signal documentation standard. Benefits developers and operations teams by optimizing AI agent performance and reducing GPU cycles on outdated documents.
17360Updated 2mo ago
Intermediate30min to implementautomation
Saves ~180 min per use
Quick InstallView Source
git clone https://github.com/cortex-tms/cortex-tms.gitWorks with:
Claude
Overview
About This Skill
Cortex TMS is an open-source documentation governance platform designed specifically for AI coding agents like Claude. It scaffolds governance templates (PATTERNS.md, CLAUDE.md, ARCHITECTURE.md) that keep AI agents aligned with your project standards, then validates documentation health through automated checks including staleness detection that flags when docs drift from code changes. The tool includes CLI commands for initialization, validation, status tracking, and task archival, with support for ecosystem-specific presets (Node.js, Python, Go) and CI/CD integration. Teams use Cortex TMS to prevent AI agents from overengineering solutions, ensure code consistency, and maintain human oversight through documented approval gates.
How to Use
1. **Install Cortex TMS CLI**: Run `pip install cortex-tms` and authenticate with your API key. Verify installation with `cortex --version`. 2. **Profile API Calls**: Use `cortex profile --project [PROJECT_NAME] --days 30` to generate a cost breakdown of your top 20 API calls. Export results to CSV for analysis. 3. **Run Optimization Scan**: Execute `cortex optimize --project [PROJECT_NAME] --auto-suggest` to let Cortex TMS recommend optimizations based on your usage patterns. Review the `recommended_actions.json` file. 4. **Implement Changes**: Apply optimizations in stages using `cortex apply --call [CALL_ID] --feature [FEATURE_NAME]`. Start with low-effort changes (e.g., batching) before tackling complex ones (e.g., semantic chunking). 5. **Monitor Impact**: Use `cortex metrics --call [CALL_ID]` to track cost savings and latency improvements. Adjust thresholds in the Cortex TMS dashboard if needed. *Tip: For large projects, use the `--dry-run` flag to simulate optimizations before applying them to production.*
Use Cases
Initialize governance documentation templates for new AI-integrated projects
Detect and flag outdated governance docs before they mislead AI agents
Validate documentation health and completeness in CI/CD pipelines
Archive completed tasks to keep NEXT-TASKS.md focused and maintainable
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/cortex-tms/cortex-tmsRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Use Cortex TMS to optimize the AI API calls for [PROJECT_NAME]. Identify the top 5 most expensive API calls by cost per 1000 tokens and analyze their context windows. For each, suggest a more efficient context management strategy using Cortex TMS's [FEATURE_SET] (e.g., dynamic context pruning, retrieval optimization, or prompt compression). Provide the projected cost savings for each optimization and the estimated implementation effort (low/medium/high).
Example Output
### Cortex TMS Optimization Report for Project 'DocuBot Pro' (Generated: 2024-05-15)
**Top 5 Costliest API Calls (Last 30 Days)**
1. **Call ID: `doc-parse-001`**
- **Cost:** $1,247.89 (1,247,890 tokens)
- **Context:** Full legal document (15,000 tokens) parsed for entity extraction
- **Current Context Window:** 15,000 tokens (static)
- **Optimization:** Dynamic context pruning using Cortex TMS's `semantic_chunking` feature
- **New Context Window:** 3,200 tokens (relevant sections only)
- **Projected Savings:** $1,012.45/month (81% reduction)
- **Effort:** Low (pre-built integration)
2. **Call ID: `qa-summary-002`**
- **Cost:** $892.34 (892,340 tokens)
- **Context:** 100-page technical manual summarized
- **Current Context Window:** 10,000 tokens (full document)
- **Optimization:** Retrieval-optimized chunking with `relevance_filtering`
- **New Context Window:** 2,100 tokens (key sections + metadata)
- **Projected Savings:** $713.87/month (80% reduction)
- **Effort:** Medium (requires metadata tagging)
3. **Call ID: `email-classify-003`**
- **Cost:** $654.12 (654,120 tokens)
- **Context:** 500 emails classified by urgency (avg. 500 tokens/email)
- **Current Context Window:** 500 tokens/email (no batching)
- **Optimization:** Batch processing with `context_batching`
- **New Context Window:** 45 tokens/email (compressed prompts)
- **Projected Savings:** $588.71/month (90% reduction)
- **Effort:** Low (CLI flag adjustment)
**Total Projected Monthly Savings:** $2,315.03 (82% reduction in API costs)
**Implementation Timeline:** 2-3 days (parallelizable optimizations)
**Next Steps:**
1. Apply `semantic_chunking` to `doc-parse-001` using Cortex TMS CLI: `cortex optimize --call doc-parse-001 --feature semantic_chunking`
2. Test `relevance_filtering` on `qa-summary-002` with sample documents
3. Deploy batching for `email-classify-003` in staging environment
*Cortex TMS v2.1.4 | Documentation: https://docs.cortex-tms.ai/optimization-guide*Apply to these tools
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