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/appletechie/perfect-goalnpx agentmods add skills/appletechie/perfect-goal/hermesWrote 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/appletechie/perfect-goal/hermes)<a href="https://agentmods.dev/skills/appletechie/perfect-goal/hermes"><img src="https://agentmods.dev/badge/skills/appletechie/perfect-goal/hermes/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/appletechie/perfect-goal/hermes"><img src="https://agentmods.dev/badge/skills/appletechie/perfect-goal/hermes.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 | $0.00131 | $0.03267 |
| Opus 5.5 | $0.00052 | $0.01307 |
| Sonnet 5.5 | $0.00026 | $0.00653 |
| Haiku 4.5 | $0.00013 | $0.00327 |
Grade A, and why
hermes 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 2mo 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.
ssh dokploy-root 'curl -X POST -H "Authorization: Bearer $DOKPLOY_API_KEY" \ How it starts
The opening of the file, as written. The whole thing — 201 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hermes — Working Knowledge
Hermes is a Python-based AI agent platform forked from NousResearch/hermes-agent and operated as enduru-mc-v<NN> releases. This skill compresses the high-leverage knowledge for working in and deploying hermes — codebase layout, plugin authoring, the v15→v16 upstream-merge regression chain, the fleet deploy flow, and the traps that cost real engineering time when missed.
If you're filing a multi-step goal involving hermes, invoke /perfect-goal after reading this skill. The two compose: this skill gives you the facts, perfect-goal gives you the output shape.
Codebase layout
The canonical repo is hermes-agent-enduru (fork of NousResearch/hermes-agent). Customer pins live in enduru-agent-infra/Dockerfile.v2. Operator fleet builds from /srv/hermes-fleet-repo on dokploy-root.
hermes-agent-enduru/
├── hermes_cli/ # CLI entry (`python -m hermes_cli.main`)
│ ├── main.py
│ └── plugins.py # CLI-side plugin route system (see 37268d0bfd)
├── gateway/ # Long-running gateway (WS RPC + REST)
│ ├── run.py # GatewayRunner — includes spawn_subprocess_agent (restored v16.3 / d2072fb484)
│ ├── api_server.py # FastAPI routes; voice-realtime WS endpoint here
│ ├── mc_worker.py # Pulls + executes MC tasks; the operator/customer task surface
│ ├── mc_routes.py # MC-facing REST routes (transcript reader at L~120 reads JSON in v16.1+, was SQLite in upstream)
│ └── voice_realtime/ # Browser/Discord/Meet realtime voice (browser_bridge.py + amalia_session.py)
├── plugins/ # First-class plugins
│ ├── <name>/
│ │ ├── plugin.yaml # Declaration — name, version, kind, platforms, provides_tools
│ │ └── __init__.py # Implementation — register(ctx) entry point
│ └── agent_manifest/ # Posts profile manifest → MC every 60s (see L~47 _profile_agent_id)
├── plugins-dist/ # Built/bundled MC plugin artifacts
├── scripts/ # Build + release scripts
└── pyproject.toml # MUST include plugins/**/plugin.yaml in package-data (28be8f2527 v16.1 fix)
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.
- 2mo ago First seen · 201 lines · 131 tokens per session scan A c60ef0aec0fa
hermes is a skill published in the GitHub repository appletechie/perfect-goal (2 stars, last pushed 5mo ago), licensed MIT. It adds 131 tokens to every session and 3,267 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…