Hermes Agent is an AI assistant that learns from its use by creating and improving skills, retaining knowledge, searching past conversations, and adapting to its users. It is for people who want to run an agent through a terminal or messaging platforms while connecting it to different AI models and scheduled tasks.
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 skills add NousResearch/hermes-agent --skill dream-loopgit clone --depth 1 https://github.com/NousResearch/hermes-agentWrote 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/nousresearch/hermes-agent/dream-loop)<a href="https://agentmods.dev/skills/nousresearch/hermes-agent/dream-loop"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/dream-loop/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/nousresearch/hermes-agent/dream-loop"><img src="https://agentmods.dev/badge/skills/nousresearch/hermes-agent/dream-loop.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 | $0.00016 | $0.03317 |
| Opus 5.5 | $0.00006 | $0.01327 |
| Sonnet 5.5 | $0.00003 | $0.00663 |
| Haiku 4.5 | $0.00002 | $0.00332 |
Grade A, and why
dream-loop 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 1mo 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 — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dream Loop Skill
An autonomous process for building extremely impressive visuals, especially 3D scenes (games, apps, usually browser three.js/WebGL): generate photorealistic concept art of the ideal result, build it, screenshot the live build, have a judge score screenshot vs concept against a gated ladder, and iterate until convergence. Goal: the most visually stunning result at an acceptable frame rate for the target platform (e.g. 60 fps browser, 120 fps modern mobile).
This skill does NOT cover general web-app functionality, 2D UI design, or non-visual quality — only the visual-fidelity loop.
When to Use
- User says "dream loop" or asks for a game/scene/app built to a very high level of graphical fidelity.
- Follow-up refinement passes on an existing visual product (see Follow-up loops below).
Prerequisites
- Concept art: the
image_generatetool. If unavailable, stop and ask the user for a concept image (or an image-generation API to connect to). - Screenshots:
browser_exec— serve the build locally (python3 -m http.serverfor static builds), thennew_tab(url),wait_for_load(),capture_screenshot(). - Judging:
vision_analyze(see Judge section for the one-image-per-call workaround). - Optional: Blender for asset modeling — see the
blender-3d-automationskill.delegate_taskfor parallel asset work and fresh-context judging.
If you don't have the tools needed for the full loop, flag that to the user early and stop.
Quick Reference
| Stage | What happens | Artifacts (.dream-loop/) |
|---|---|---|
| 1. Target | Get/confirm the user's description | notes |
| 2. Concept | Generate "in-engine screenshot" concept art | concept.png |
| 3. Budget | Record time budget & start time (if given) | notes |
| 4. Build | Implement the concept as well as possible in one go | source, plans |
| 5. Screenshot | Capture live build at concept resolution | round-N.png |
| 6. Self-check | Rigorous side-by-side audit before judging | assessment log |
| 7. Judge | Ladder-scored comparison, actionable directives | verdict log |
| 8. Iterate/Exit | Address directives or exit per criteria | — |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 283 lines · 16 tokens per session scan A 188dac5e2989
dream-loop is a skill published in the GitHub repository NousResearch/hermes-agent (255,268 stars, last pushed today), licensed MIT. It adds 16 tokens to every session and 3,317 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-14.
Other skills, from other repositories
build-games-and-playful-experiences
A guide for building, changing, diagnosing, and checking two-dimensional games and playful experiences. It covers games, interactive toys, story exploration, rhythm experiences, generative experiences, and local multiplayer.
wow-assistant
Context-aware World of Warcraft lore, progressive hints, builds, and PvP/PvE guidance using the Nanocodex WoW companion's exported character snapshot.
rpg
Unterstützt systemneutral bei Abenteuern, Szenen, Figuren und Kampagnenorganisation.
minecraft-modpack-server
Host modded Minecraft servers (CurseForge, Modrinth).
autonomous-loops
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
docs-governance
Route broad documentation-governance requests to existing ECC skills and run an opt-in, read-only audit of mapped documentation roles, links, ADR indexes, and evidence references. Use when a user asks for documentation governance, project-knowledge organization, documentation integrity, or a deterministic docs audit…