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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/agentlas-ai/Agentlas-OSnpx agentmods add skills/agentlas-ai/agentlas-os/hep-stormWrote 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/hep-storm)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-os/hep-storm"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/hep-storm/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/skills/agentlas-ai/agentlas-os/hep-storm"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-os/hep-storm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.02160 |
| Opus 5 | $0.00013 | $0.01080 |
| Sonnet 5 | $0.00005 | $0.00432 |
| Haiku 4.5 | $0.00003 | $0.00216 |
Grade A, and why
hep-storm 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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Update fallback: 자동 업데이트가 안 되면 hephaestus update를 한 번 실행하세요. 업데이트하지 않아도 현재 버전 명령은 그대로 동작합니다.
Hephaestus Stormbreaker loop
Raw arguments: everything the user typed after /skill:hep-storm.
Codex plugins cannot register slash commands, so this custom prompt is the
explicit entrypoint (/prompts:hep-storm). The same contract is also available
implicitly via the hep-storm skill. Also triggered by
@Hephaestus storm <goal>.
Drive a goal through the Stormbreaker Loop — Hephaestus' force-robust, verifier-first execution loop. Unlike a one-shot answer or a generic parallel fan-out, Stormbreaker routes the goal to real Agentlas specialists, structures the work as a dependency-ordered pipeline fabric, drives each work packet as a hardened goal loop (it does not stall, run away, or claim false success), and refuses to report success without evidence. In an agentic runtime you are the executor — the engine gives you the verified plan; you carry it out with your own tools.
Use it for loop-worthy work: apps, sites, agents, automations, debugging, multi-step research, data/report generation — anything with files, tools, tests, or external verification. Trivial questions should be answered directly, not stormed.
Core-owned Goal + UltraCode harness
Every result includes execution_harness. Apply
execution_harness.system_prompt verbatim before planning or executing any
packet, and retain its prompt_sha256 in the goal ledger. Do not redefine,
summarize, or replace Goal mode or UltraCode mode in this Codex adapter. If live
session JSON is available, expose it as AGENTLAS_SESSION_INVENTORY; otherwise
use Core's explicit host:primary fallback and do not invent workers or models.
With no external executor, status: materialized plus
final_gate.can_report_success: false is the expected handoff to Codex's native
tools, never a completed run.
1. Resolve the runner and materialize the execution fabric
Resolve the runner — first executable wins; runtime cache fallback:
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 · 162 lines · 26 tokens per session scan A 637df9ff21a5
hep-storm is a skill published in the GitHub repository agentlas-ai/Agentlas-OS (1,105 stars, last pushed 2d ago), licensed Apache-2.0. It adds 26 tokens to every session and 2,160 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
agentfield-use
Whenever you have a discrete task to perform — one the user delegated, or one that arose inside your own work — check FIRST whether an installed AgentField agent covers it, and offload to it by default when one does. Coverage, not task size, is the test: even a small job goes to a covering agent. The check is cheap …
agent-optimization
Improve an Agent State through versioned scores and score-linked Traces from a frozen Benchmark.
quality-loop
Use this workflow recipe when a draft, plan, proposal, or other deliverable should be independently reviewed and revised until it satisfies explicit quality criteria.
cli-skill-design
Design a co CLI surface and its SKILL.md together so an agent can drive it without guessing — every command ends by naming the next one, --help lists everything, and every failure says what to run instead. Use when adding a new CLI command group, writing or rewriting a SKILL.md for one, or auditing an existing one.
update-setup
A one-time setup wizard for creating a personalised upgrade guide for a workspace. It checks for an existing guide, identifies the current version and installation clues, and requires confirmation of the installation method before writing a new guide.
plan_route
Plan a route and return distance + ETA (schema + deterministic result).