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 skills/coco-research/coco/local-llmnpx skills add coco-research/coco --skill local-llmgit clone --depth 1 https://github.com/coco-research/cocoWrote 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/coco-research/coco/local-llm)<a href="https://agentmods.dev/skills/coco-research/coco/local-llm"><img src="https://agentmods.dev/badge/skills/coco-research/coco/local-llm.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 | $0.00058 | $0.07014 |
| Opus 5 | $0.00029 | $0.03507 |
| Sonnet 5 | $0.00012 | $0.01403 |
| Haiku 4.5 | $0.00006 | $0.00701 |
Grade A, and why
local-llm scanned grade A with 1 finding 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 2d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
(a one-off script, a curl command, etc.), you don't get this retry for free -- The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 2d ago First seen · 409 lines · 58 tokens per session scan A 074950671e19
local-llm is a skill published in the GitHub repository coco-research/coco (218 stars, last pushed today), with no licence file. It adds 58 tokens to every session and 7,014 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
pageindex
PageIndex - Vectorless reasoning-based RAG for document retrieval.
infrastructure-llm
Skill for the LLM infrastructure module providing local Large Language Model integration via Ollama. Covers client initialization, prompt templates, output validation, manuscript review generation, conversation context, and CLI usage. Use when querying LLMs, generating manuscript reviews, validating LLM outputs, or…
infrastructure-search-monid
Monid HTTP API client for discovering, inspecting, and running hundreds of data endpoints through one wallet (discover/inspect/run/poll/balance). Includes an offline USD-per-1k comparison table for direct search APIs (Exa, Brave, Tavily, Serper, SerpAPI) versus Monid's per-endpoint gateway pricing. Use when the user…
template-autoresearch-project
AutoResearch loop exemplar — deterministic ML candidate evaluation, evidence registries, claim ledgers, artifact manifests, readiness gates.
Meta-Analysis Source API
Core orchestration guidelines and MCP interactions for the src/ library.
wshobson-rag-implementation
Skill "wshobson-rag-implementation" from ItamarZand88/awesome-agent-conventions, covering rag implementation, when to use this skill, core components, 1. vector databases and 2. embeddings.