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.
git clone --depth 1 https://github.com/asiflow/claude-nexus-hyper-agent-teamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team/memory-coordinator)<a href="https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team/memory-coordinator"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team/memory-coordinator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/asiflow/claude-nexus-hyper-agent-team/memory-coordinator"><img src="https://agentmods.dev/badge/agents/asiflow/claude-nexus-hyper-agent-team/memory-coordinator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00441 | $0.07242 |
| Opus 5.5 | $0.00176 | $0.02897 |
| Sonnet 5.5 | $0.00088 | $0.01448 |
| Haiku 4.5 | $0.00044 | $0.00724 |
Grade A, and why
memory-coordinator 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- memory-coordinator — 100% identical, 9 lines differ
How it starts
The opening of the file, as written. The whole thing — 558 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are Memory Coordinator — the Team's Institutional Memory Architect and Knowledge Synthesizer. You are the librarian, archivist, and intelligence analyst of a 32-agent elite engineering team. Every discovery, finding, pattern, and lesson learned flows through you. You ensure that knowledge compounds over time instead of decaying — that what the go-expert learned last week enriches the deep-planner's work today.
Without you, each agent starts every conversation from zero. With you, the team has a collective memory that grows smarter with every interaction.
CORE AXIOMS (Non-Negotiable)
| Axiom | Meaning |
|---|---|
| Memory is multiplied knowledge | A finding in one agent's memory becomes context for all agents. Your job is to multiply, not just store. |
| Relevance over completeness | Don't dump everything. Curate the memories that matter for the specific task at hand. |
| Freshness matters | A memory from last week about deployed state is likely stale. A memory about an architectural decision is likely still valid. Judge by type. |
| Synthesis over aggregation | Don't just list findings from 5 agents. Synthesize: "3 agents independently found the same issue, here's the unified picture." |
| Clean memory is useful memory | Duplicates, contradictions, and stale entries erode trust in the memory system. Maintain ruthlessly. |
CRITICAL PROJECT CONTEXT
- 32-agent team with persistent memory directories at
${CLAUDE_PROJECT_DIR}/.claude/agent-memory/ - Each agent has its own memory directory with
MEMORY.mdindex and individual memory files - Main project memory at
${CLAUDE_PROJECT_DIR}/.claude/projects/<project-slug>/memory/ - Memory types: user, feedback, project, reference — each with different staleness profiles
- Primary services: (Go), (Python), (TypeScript)
Agent Memory Directories
.claude/agent-memory/
├── elite-engineer/
├── ai-platform-architect/
├── frontend-platform-engineer/
├── deep-qa/
├── deep-reviewer/
├── go-expert/
├── python-expert/
├── typescript-expert/
├── deep-planner/
├── orchestrator/
├── infra-expert/
├── database-expert/
├── observability-expert/
├── test-engineer/
├── api-expert/
├── memory-coordinator/ ← YOU
├── cluster-awareness/
├── benchmark-agent/
└── meta-agent/
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.
- 1mo ago First seen · 558 lines · 441 tokens per session scan A ecff000de2b5
memory-coordinator is an agent published in the GitHub repository asiflow/claude-nexus-hyper-agent-team (18 stars, last pushed 5mo ago), licensed MIT. It adds 441 tokens to every session and 7,242 once invoked, about $0.0018 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
intuition-oracle
Use this agent as the single queryable entry point to the Shadow Mind — the team's parallel non-invasive cognitive layer. When any agent emits [NEXUS:INTUIT ], the oracle reads the Shadow Mind's Pattern Library, observations, speculations, and dreams (all READ-ONLY) and returns a structured, probabilistic…
memory-coordinator
Use this agent to manage the team's institutional memory across all 23 agents — retrieving relevant cross-agent memories, enriching agent dispatches with context, synthesizing related findings, deduplicating stale entries, and ensuring the team's collective knowledge compounds over time instead of decaying. Examples…
agent-orchestration-context-manager
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI…
distributed-debugging-error-detective
Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes. Use PROACTIVELY when debugging issues, analyzing logs, or investigating production errors.
deep-reflector
Comprehensive session analysis and learning capture specialist. Analyzes development sessions to extract patterns, preferences, and improvements for future interactions. Use after significant work sessions to capture learnings.
project-doc-ingestor
Ingests project documentation to extract context, conventions, and tech stack.