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 Lingtai-AI/lingtai --skill agent-runtimegit clone --depth 1 https://github.com/Lingtai-AI/lingtaiWrote 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/lingtai-ai/lingtai/agent-runtime)<a href="https://agentmods.dev/skills/lingtai-ai/lingtai/agent-runtime"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/agent-runtime/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/lingtai-ai/lingtai/agent-runtime"><img src="https://agentmods.dev/badge/skills/lingtai-ai/lingtai/agent-runtime.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 | $0.00039 | $0.01011 |
| Opus 5.5 | $0.00016 | $0.00404 |
| Sonnet 5.5 | $0.00008 | $0.00202 |
| Haiku 4.5 | $0.00004 | $0.00101 |
Grade A, and why
tutorial-guide-agent-runtime 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tutorial Guide — Agent Runtime Lessons
Nested tutorial-guide reference for agent runtime lessons 4–6, reached from the root tutorial-guide router. Teach live per the router's discover, don't recite rule: read the real file or run the real command before explaining it.
Lesson 4: How Agents Are Born — init.json and lingtai-agent run
Part 1: init.json
Read YOUR init.json and walk through every field you find. Do not recite a list of fields — read the file and explain what is there. The human sees the real structure.
Key patterns to explain:
- The
_fileconvention: live init fields likecovenant,pad,comment,base_prompttake inline text or a<field>_filepath to a shared file. (Older fields such asprinciple,procedures,brief,soul, and the legacypromptare migrated by the kernel but no longer seeded here.) Note: there is no seed field for the agent's character in init.json — character is durable state the agent authors for itself after creation, managed viasystem/lingtai.md/ psyche, not written into init.json. manifestcontains: llm, agent_name, language, capabilities, admin, etc.addonsconnects to external messaging services.env_filefor secrets.
Part 2: lingtai-agent run
Explain the boot sequence: read init.json → load env → resolve venv → build Agent → clean stale signals → install signal handlers → start in ASLEEP state → agent.start() blocks on shutdown.
Emphasize: the agent is a long-running Python process. It does not exit after one task.
Part 3: Heartbeat and signal files
Show your own .agent.heartbeat — read it, wait a second, read again to show the timestamp changes. Explain the signal files: .interrupt, .suspend, .sleep, .prompt.
Lesson 5: The TUI — How lingtai-tui Wraps the Agent Runtime
Explain: lingtai-tui is a Go frontend, not the agent. It creates agents (writes init.json), launches them (python -m lingtai run), monitors them (.agent.heartbeat, .agent.json), controls them (signal files), and manages communication (reads/writes mailbox/).
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 Changed bcb10102871e
- 1mo ago First seen · 73 lines · 39 tokens per session scan A c2baf8c0afca
tutorial-guide-agent-runtime is a skill published in the GitHub repository Lingtai-AI/lingtai (507 stars, last pushed 6d ago), licensed Apache-2.0. It adds 39 tokens to every session and 1,011 once invoked, about $0.0002 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.
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