Yashvardhan Thanvi

What an agent can do with the llmslim corpus

Thanatos9404/llmslim: Long-term memory for AI agents with no LLM call: an MCP server and Claude Code plugin, plus local-first context compression for LLM apps

• Look up how llmslim implements cost-optimization, llm, prompt-engineering, with answers cited to file and line.
• Reuse a worked example: the commit “make v0.6.0 playground adaptive” (8 files · Python, TypeScript).
• Answer from llmslim's real Python source instead of memory (AI, prompts and agents).

• Where does llmslim handle “make v0.6.0 playground adaptive”?
• How does llmslim implement cost-optimization?
• What does web do in llmslim?

Search the GitHub repository Thanatos9404/llmslim with XERJ (https://hub.xerj.org/corpus/thanatos9404--llmslim).
Run these in a shell, in order, and check each result before the next:
1. xerj --help  (success: it prints usage). If xerj is missing: curl -fsSL https://xerj.org/get | sh
2. git clone https://github.com/Thanatos9404/llmslim llmslim && git -C llmslim checkout 333787d3faab96cc4480d12c2a02c1538f795c66
3. Start a local node and leave it running: xerj --insecure --data-dir ./xerj-data
4. xerj autoindex ./llmslim --url http://127.0.0.1:9200 --prefix llmslim --progress plain
5. xerj autoindex map --url http://127.0.0.1:9200
Then answer my question from the indexed files and cite the ax_path of every hit. Query syntax: https://xerj.org/llms.txt
Licence: MIT. Keep the copyright notice and credit the authors.

corpus thanatos9404--llmslim · AI, prompts and agents
Related corpora: 12-factor-agents, prompt-engineering-guide, marcusaguiarmaciel-cmd/cato-plugin, banzaicolo/sourced-intel
AI search and retrieval with XERJ · Case study: reference coding for agents