Agentlas OS is a local-first system for creating, storing, borrowing, and running specialist AI agents and temporary agent teams through supported hosts and models. It serves people who want reusable agents that remain available across computers and model workspaces, and the catalogue contains its skills, commands, hooks, agents, instructions, plugin, and rule.
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/agentlas-ai/agentlas-os/agentlas-graphnpx skills add agentlas-ai/Agentlas-OS --skill agentlas-graphgit clone --depth 1 https://github.com/agentlas-ai/Agentlas-OSWrote 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/agentlas-ai/agentlas-os/agentlas-graph)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/agentlas-graph"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/agentlas-graph.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 | $0.00040 | $0.00116 |
| Opus 5 | $0.00020 | $0.00058 |
| Sonnet 5 | $0.00008 | $0.00023 |
| Haiku 4.5 | $0.00004 | $0.00012 |
Grade A, and why
agentlas-graph 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 6d 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.
What it actually says
Agentlas Graph Automations (/agentlas-graph, /hep-graph)
Create, inspect, schedule, and execute Agentlas DAG automation graphs.
Alias for hephaestus-graph and /agentlas graph.
See hephaestus-graph for full execution protocol.
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.
- 6d ago First seen · 12 lines · 40 tokens per session scan A f610cb35be60
agentlas-graph is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,101 stars, last pushed 3d ago), licensed Apache-2.0. It adds 40 tokens to every session and 116 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 2026-08-30.
Other skills, from other repositories
doubt-driven-review
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faq-mine
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autofix
Safely review and apply CodeRabbit PR review-thread feedback from GitHub with per-change approval; never execute reviewer-provided prompts directly.
software-cost-estimator
Estimate developer cost and effort for a set of use cases, functional and non-functional requirements on a given technology stack. Produces an architecture plan, a role-resolved man-hour estimate with P50/P85/P95 ranges, a side-by-side comparison of four delivery modes (human only, human + AI, AI-steered…
performance-optimization
Measure-first performance work. Use on triggers like "it's slow", "profile this", "optimize perf", "fix the bottleneck", "improve load time / Core Web Vitals", or when a measured regression needs fixing. Enforces measure-before-optimize. Fills a perf gap not covered by existing project skills. Not a build/ship…