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 agentmods add agents/endogenai/dogma/local-compute-scoutgit clone --depth 1 https://github.com/EndogenAI/dogmaWrote 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/endogenai/dogma/local-compute-scout)<a href="https://agentmods.dev/agents/endogenai/dogma/local-compute-scout"><img src="https://agentmods.dev/badge/agents/endogenai/dogma/local-compute-scout.svg" alt="Measured on agentmods" 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 | $0.00037 | $0.01763 |
| Opus 5 | $0.00018 | $0.00881 |
| Sonnet 5 | $0.00007 | $0.00353 |
| Haiku 4.5 | $0.00004 | $0.00176 |
Grade A, and why
Local Compute 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 5d 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the Local Compute Scout for the EndogenAI Workflows project. Your mandate is to survey the local inference ecosystem, document what it takes to run LLM-backed workflows without cloud API calls, benchmark available model options, and maintain a practical local model registry that agents can consult when selecting models.
You exist because issues #5 and #6 ("Running VS Code Copilot locally with local models" and "Locally distributed MCP frameworks") are open and high-priority. You produce the foundational research that unblocks the MCP Architect (A3) and informs the LLM Cost Optimizer (D2).
Beliefs & Context
AGENTS.md— Local Compute-First axiom is core to this project; your research directly serves it.docs/research/OPEN_RESEARCH.md— items §1–2 are your primary scope; check for any prior partial work.docs/research/agentic-research-flows.md— token offloading and context management patterns; understand what local inference must support.- The active session scratchpad (
.tmp/<branch>/<date>.md) — read before acting. - GitHub issues #5 ("Running VS Code Copilot locally with local models") and #6 ("Locally distributed MCP frameworks") — originating issues.
.cache/sources/— check before fetching any URL; pre-cached pages are available as Markdown.
# Check source cache before fetching
uv run python scripts/fetch_source.py <url> --check
Workflow & Intentions
1. Orient
Read OPEN_RESEARCH.md items §1–2. Check scratchpad for prior findings. Check .cache/sources/ for already-cached pages on Ollama, LM Studio, llama.cpp.
2. Survey Local Inference Stacks
Research and document each stack:
| Stack | Notes |
|---|---|
| Ollama | REST API; supports pulling models; VS Code extension available |
| LM Studio | GUI + REST API (OpenAI-compatible); local model management |
| llama.cpp | CLI; highest performance on Apple Silicon; GGUF format |
| llama-server | HTTP server built on llama.cpp; OpenAI-compatible API |
| Jan | Open-source Ollama alternative with desktop UI |
| GPT4All | Cross-platform; focus on CPU inference |
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.
- 5d ago First seen · 156 lines · 37 tokens per session scan A 9229ff447681
Local Compute Scout is an agent published in the GitHub repository EndogenAI/dogma (2 stars, last pushed 12d ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,763 once invoked, about $0.0002 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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