ConnectOnion is an open-source, template-first toolkit for building, debugging, deploying, and operating AI agents. Developers use its command-line tools and Python runtime to create agents, add tools, connect services, deploy them, and make them callable by other agents, while the catalogue entries are related agents, skills, and instructions.
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/openonion/connectonionWrote 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/openonion/connectonion/agent-prompt-dream-memory-consolidation)<a href="https://agentmods.dev/agents/openonion/connectonion/agent-prompt-dream-memory-consolidation"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-dream-memory-consolidation/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/openonion/connectonion/agent-prompt-dream-memory-consolidation"><img src="https://agentmods.dev/badge/agents/openonion/connectonion/agent-prompt-dream-memory-consolidation.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 | $0.00045 | $0.00728 |
| Opus 5 | $0.00023 | $0.00364 |
| Sonnet 5 | $0.00009 | $0.00146 |
| Haiku 4.5 | $0.00005 | $0.00073 |
Grade A, and why
Agent Prompt: Dream memory consolidation 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dream: Memory Consolidation
You are performing a dream — a reflective pass over your memory files. Synthesize what you've learned recently into durable, well-organized memories so that future sessions can orient quickly.
Memory directory: ${MEMORY_DIR}
${MEMORY_DIR_CONTEXT}
Session transcripts: ${TRANSCRIPTS_DIR} (large JSONL files — grep narrowly, don't read whole files)
Phase 1 — Orient
lsthe memory directory to see what already exists- Read
${INDEX_FILE}to understand the current index - Skim existing topic files so you improve them rather than creating duplicates
- If
logs/orsessions/subdirectories exist (assistant-mode layout), review recent entries there
Phase 2 — Gather recent signal
Look for new information worth persisting. Sources in rough priority order:
- Daily logs (
logs/YYYY/MM/YYYY-MM-DD.md) if present — these are the append-only stream - Existing memories that drifted — facts that contradict something you see in the codebase now
- Transcript search — if you need specific context (e.g., "what was the error message from yesterday's build failure?"), grep the JSONL transcripts for narrow terms:
grep -rn "<narrow term>" ${TRANSCRIPTS_DIR}/ --include="*.jsonl" | tail -50
Don't exhaustively read transcripts. Look only for things you already suspect matter.
Phase 3 — Consolidate
For each thing worth remembering, write or update a memory file at the top level of the memory directory. Use the memory file format and type conventions from your system prompt's auto-memory section — it's the source of truth for what to save, how to structure it, and what NOT to save.
Focus on:
- Merging new signal into existing topic files rather than creating near-duplicates
- Converting relative dates ("yesterday", "last week") to absolute dates so they remain interpretable after time passes
- Deleting contradicted facts — if today's investigation disproves an old memory, fix it at the source
Phase 4 — Prune and index
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.
- 11d ago First seen · 67 lines · 0 tokens per session scan A b1728bb9eaf5
Agent Prompt: Dream memory consolidation is an agent published in the GitHub repository openonion/connectonion (1,480 stars, last pushed yesterday), licensed Apache-2.0. It adds 45 tokens to every session and 728 once invoked, about $0.0002 per session on Opus 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
memory-consolidator
Use this agent ONLY when a human has just run /memory-seed and the new L1 atoms need folding into scenes and persona. Automatic consolidation no longer goes through this agent - it runs headless, outside the session. Do not invoke this agent on your own initiative.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.