PhyAgentOS-core is an agent framework for physical, or embodied, tasks in which software agents plan tool calls, observe what happened, and verify whether a real-world goal was achieved. It is intended for systems that operate physical agents through governed execution, recovery, evidence collection, and task-level verification. The catalogue add-ons provide workflows for working with this framework.
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 skills add PhyAgentOS/PhyAgentOS-core --skill memorygit clone --depth 1 https://github.com/PhyAgentOS/PhyAgentOS-coreWrote 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/skills/phyagentos/phyagentos-core/memory)<a href="https://agentmods.dev/skills/phyagentos/phyagentos-core/memory"><img src="https://agentmods.dev/badge/skills/phyagentos/phyagentos-core/memory/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/skills/phyagentos/phyagentos-core/memory"><img src="https://agentmods.dev/badge/skills/phyagentos/phyagentos-core/memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00011 | $0.00344 |
| Opus 5.5 | $0.00004 | $0.00138 |
| Sonnet 5.5 | $0.00002 | $0.00069 |
| Haiku 4.5 | $0.00001 | $0.00034 |
Grade A, and why
memory 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.
What it actually says
Memory
Structure
memory/MEMORY.md— Long-term facts (preferences, project context, relationships). Always loaded into your context.memory/HISTORY.md— Append-only event log. NOT loaded into context. Search it with grep-style tools or in-memory filters. Each entry starts with [YYYY-MM-DD HH:MM].
Search Past Events
Choose the search method based on file size:
- Small
memory/HISTORY.md: useread_file, then search in-memory - Large or long-lived
memory/HISTORY.md: use theexectool for targeted search
Examples:
- Linux/macOS:
grep -i "keyword" memory/HISTORY.md - Windows:
findstr /i "keyword" memory\HISTORY.md - Cross-platform Python:
python -c "from pathlib import Path; text = Path('memory/HISTORY.md').read_text(encoding='utf-8'); print('\n'.join([l for l in text.splitlines() if 'keyword' in l.lower()][-20:]))"
Prefer targeted command-line search for large history files.
When to Update MEMORY.md
Write important facts immediately using edit_file or write_file:
- User preferences ("I prefer dark mode")
- Project context ("The API uses OAuth2")
- Relationships ("Alice is the project lead")
Auto-consolidation
Old conversations are automatically summarized and appended to HISTORY.md when the session grows large. Long-term facts are extracted to MEMORY.md. You don't need to manage this.
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 · 38 lines · 11 tokens per session scan A 88d73a0d3a72
memory is a skill published in the GitHub repository PhyAgentOS/PhyAgentOS-core (2,961 stars, last pushed yesterday), licensed MIT. It adds 11 tokens to every session and 344 once invoked, about $0.0000 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 skills, from other repositories
harness-creator
Build, audit, and improve harnesses that make AI coding agents reliable: AGENTS.md/CLAUDE.md instruction files, feature/state tracking, verification gates, scope boundaries, session handoff, memory persistence, context budgets, tool-permission safety, and multi-agent coordination. Use this whenever a coding agent is…
awm-rsi-toolkit
Run AWM (Agent Workflow Memory, Wang et al. 2024) on an agent - induce reusable workflows from its successful trajectories, online over a training stream or offline from supplied experiences, then freeze the workflow memory and measure it on a published Benchmark.
openloomi-memory
OpenLoomi Memory is the long-lived context layer of OpenLoomi — a tiered, locally-stored knowledge graph that grows on its own from your Connectors, chats, and Screen Capture. Memory is what makes Chat grounded and what Loop reads before it produces a Decision. It is always on your machine (local-first), always…
continual-learn
Persist learning across turns by actively maintaining MENTALMODEL.md in the workspace. Use for iterative coding, debugging, benchmarking, or any feedback-driven task; read and update the file with actual filesystem writes before every response, never just hidden/internal memory.
personal-ip-knowledge-builder
A tool for turning interviews, chats, résumés, public content, business materials, case studies, and existing documents into a long-term personal knowledge base for an AI-assisted public profile.
campaign-operations-knowledge-builder
A campaign-operations knowledge base that organizes campaign goals, user journeys, channel responsibilities, materials, timing, risk plans, and review findings.