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/altaidevorg/rules-for-aiWrote 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/rules/altaidevorg/rules-for-ai/basellmflow)<a href="https://agentmods.dev/rules/altaidevorg/rules-for-ai/basellmflow"><img src="https://agentmods.dev/badge/rules/altaidevorg/rules-for-ai/basellmflow/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/rules/altaidevorg/rules-for-ai/basellmflow"><img src="https://agentmods.dev/badge/rules/altaidevorg/rules-for-ai/basellmflow.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 | $0.00000 | $0.04195 |
| Opus 5.5 | $0.00000 | $0.01678 |
| Sonnet 5.5 | $0.00000 | $0.00839 |
| Haiku 4.5 | $0.00000 | $0.00419 |
Grade A, and why
basellmflow 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 2mo 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 — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Chapter 8: BaseLlmFlow
In the previous chapter, we saw how BaseTool allows agents to extend their capabilities beyond simple text generation. But how does the agent actually manage the conversation flow when deciding to use a tool, calling it, processing the result, and generating the final response? This chapter introduces BaseLlmFlow, the internal engine within an Agent (BaseAgent / LlmAgent) that orchestrates this complex interaction cycle with the underlying BaseLlm.
Motivation and Use Case
The logic required to interact effectively with an LLM, especially when tools, planning, or agent transfers are involved, can become quite complex. It often involves:
- Preparing the request with appropriate history, instructions, and tool definitions.
- Calling the LLM API.
- Parsing the response to check for tool calls, code snippets, plan updates, or final answers.
- If a tool is called, executing the tool and packaging the result.
- Sending the tool result back to the LLM.
- Repeating the process until a final answer is generated.
- Handling potential agent transfers.
Hardcoding this intricate logic directly into the LlmAgent class would make it monolithic and difficult to customize or extend. BaseLlmFlow abstracts this control flow, separating the how of LLM interaction from the what defined by the agent's configuration (instructions, tools, etc.). This allows for different interaction patterns (flows) and modular customization through processors.
Central Use Case: An LlmAgent equipped with a search_tool receives the query "What's new with Project IDX?".
- The
LlmAgentinternally uses an instance ofBaseLlmFlow(likelyAutoFlowbecause it handles tools). - The
BaseLlmFlowprepares the request, adding thesearch_tooldefinition. - It calls the BaseLlm.
- The LLM responds with a request to call
search_tool(query="Project IDX news"). - The
BaseLlmFlowparses this, identifies thesearch_toolTool (BaseTool), executes it, and gets the search results. - It formats the results as a
FunctionResponseand adds it to the history. - It calls the LLM again with the updated history including the search results.
- The LLM now generates a summary based on the search results.
- The
BaseLlmFlowreceives this final text response and yields the corresponding Event. TheBaseLlmFlowmanaged the entire multi-step interaction loop with the LLM and the tool.
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.
- 2mo ago First seen · 298 lines · 0 tokens per session scan A 50a14b8d6131
basellmflow is a cursor rule published in the GitHub repository altaidevorg/rules-for-ai (2 stars, last pushed 1y ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,195 tokens. 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 2026-08-31.
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