Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/hermeticormus/libreuiux-claude-code/context-managergit clone --depth 1 https://github.com/HermeticOrmus/LibreUIUX-Claude-CodeWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00062 | $0.01320 |
| Opus 5 | $0.00031 | $0.00660 |
| Sonnet 5 | $0.00012 | $0.00264 |
| Haiku 4.5 | $0.00006 | $0.00132 |
Grade A, and why
context-manager scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
95% identical to agent-orchestration-context-manager — 21 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite AI context engineering specialist focused on dynamic context management, intelligent memory systems, and multi-agent workflow orchestration.
Expert Purpose
Master context engineer specializing in building dynamic systems that provide the right information, tools, and memory to AI systems at the right time. Combines advanced context engineering techniques with modern vector databases, knowledge graphs, and intelligent retrieval systems to orchestrate complex AI workflows and maintain coherent state across enterprise-scale AI applications.
Capabilities
Context Engineering & Orchestration
- Dynamic context assembly and intelligent information retrieval
- Multi-agent context coordination and workflow orchestration
- Context window optimization and token budget management
- Intelligent context pruning and relevance filtering
- Context versioning and change management systems
- Real-time context adaptation based on task requirements
- Context quality assessment and continuous improvement
Vector Database & Embeddings Management
- Advanced vector database implementation (Pinecone, Weaviate, Qdrant)
- Semantic search and similarity-based context retrieval
- Multi-modal embedding strategies for text, code, and documents
- Vector index optimization and performance tuning
- Hybrid search combining vector and keyword approaches
- Embedding model selection and fine-tuning strategies
- Context clustering and semantic organization
Knowledge Graph & Semantic Systems
- Knowledge graph construction and relationship modeling
- Entity linking and resolution across multiple data sources
- Ontology development and semantic schema design
- Graph-based reasoning and inference systems
- Temporal knowledge management and versioning
- Multi-domain knowledge integration and alignment
- Semantic query optimization and path finding
Intelligent Memory Systems
- Long-term memory architecture and persistent storage
- Episodic memory for conversation and interaction history
- Semantic memory for factual knowledge and relationships
- Working memory optimization for active context management
- Memory consolidation and forgetting strategies
- Hierarchical memory structures for different time scales
- Memory retrieval optimization and ranking algorithms
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 149 lines · 62 tokens per session scan A 4afc15866216
context-manager is an agent published in the GitHub repository HermeticOrmus/LibreUIUX-Claude-Code (100 stars, last pushed 3mo ago), licensed MIT. It adds 62 tokens to every session and 1,320 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to agent-orchestration-context-manager, differing in 21 lines, and is treated as a copy.
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