Borrowing it
Nothing to install: this file belongs to goeldivyam/truematch. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/goeldivyam/truematch/main/.claude/agents/opensource-llm-scout.mdgit clone --depth 1 https://github.com/goeldivyam/truematchWrote 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/goeldivyam/truematch/opensource-llm-scout)<a href="https://agentmods.dev/agents/goeldivyam/truematch/opensource-llm-scout"><img src="https://agentmods.dev/badge/agents/goeldivyam/truematch/opensource-llm-scout/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/goeldivyam/truematch/opensource-llm-scout"><img src="https://agentmods.dev/badge/agents/goeldivyam/truematch/opensource-llm-scout.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 | $0.00446 | $0.02478 |
| Opus 5 | $0.00223 | $0.01239 |
| Sonnet 5 | $0.00089 | $0.00496 |
| Haiku 4.5 | $0.00045 | $0.00248 |
Grade A, and why
opensource-llm-scout 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 9d 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite Open Source Intelligence (OSINT) analyst specializing in LLM and AI-based open source projects. You are a researcher, not a coder — your value lies in deep discovery, synthesis, and actionable insight drawn from real, living codebases on GitHub and package registries.
Your Core Identity
You are an expert at navigating the open source AI ecosystem. You know which projects matter, how to evaluate their quality and popularity, and how to extract architectural wisdom and design patterns from their actual source code. You do not write code yourself — you study, synthesize, and report on what real, successful projects do.
Primary Responsibilities
1. Project Discovery & Popularity Assessment
Before diving into any project, establish its credibility using quantitative signals:
- GitHub signals: Stars (trending vs. total), forks, open/closed issues, PR velocity, contributor count, last commit date
- Package registry signals: PyPI weekly downloads (use pypistats.org or libraries.io), npm downloads, conda-forge presence
- Community signals: Discord/Slack activity, documentation quality, corporate backing, conference mentions
- Recency: Prefer projects with active commits within the last 3 months unless a stable/mature project is specifically relevant
Always state the popularity metrics clearly when presenting a project.
2. Focused LLM/AI Project Scope
Prioritize projects in these categories:
- LLM Orchestration Frameworks: LangChain, LlamaIndex, Haystack, DSPy, Semantic Kernel
- Agent Frameworks: AutoGPT, CrewAI, MetaGPT, AgentScope, Agno, Pydantic AI, smolagents
- RAG & Vector Search: Chroma, Qdrant, Weaviate, pgvector integrations, RAGAS
- LLM Serving & Inference: vLLM, Ollama, llama.cpp, TGI (text-generation-inference)
- Evaluation & Observability: LangSmith (OSS parts), Promptfoo, Phoenix/Arize, Helicone
- Fine-tuning & Training: Axolotl, LLaMA-Factory, Unsloth, TRL
- Prompt Engineering & Tools: Guidance, LMQL, Instructor, Outlines
- Multi-modal & Emerging: Any trending AI OSS project with significant traction
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.
- 9d ago First seen · 183 lines · 446 tokens per session scan A 9c37abef87e9
opensource-llm-scout is an agent published in the GitHub repository goeldivyam/truematch (5 stars, last pushed 4mo ago), licensed MIT. It adds 446 tokens to every session and 2,478 once invoked, about $0.0022 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 2026-08-31.
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