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 omnigentx/jarvis --skill jarvis-knowledgegit clone --depth 1 https://github.com/omnigentx/jarvisWrote 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/omnigentx/jarvis/jarvis-knowledge)<a href="https://agentmods.dev/skills/omnigentx/jarvis/jarvis-knowledge"><img src="https://agentmods.dev/badge/skills/omnigentx/jarvis/jarvis-knowledge/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/omnigentx/jarvis/jarvis-knowledge"><img src="https://agentmods.dev/badge/skills/omnigentx/jarvis/jarvis-knowledge.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 | $0.00032 | $0.00590 |
| Opus 5.5 | $0.00013 | $0.00236 |
| Sonnet 5.5 | $0.00006 | $0.00118 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
jarvis-knowledge 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jarvis Codebase Knowledge
GitHub Repositories
| Repo | URL | Access |
|---|---|---|
| Upstream (source of truth) | https://github.com/omnigentx/jarvis | Public read-only |
| Your fork (agents push here) | https://github.com//jarvis | Full access — commit, push, create PR |
🧪 Experimental: Agents push to your personal fork for self-improvement, never to upstream.
High-Level Architecture
jarvis/
├── backend/ ← FastAPI + fast-agent (Python 3.11+)
│ ├── server.py ← Entry point
│ ├── agent.py ← Agent definitions
│ ├── routes/ ← API endpoints
│ ├── services/ ← Business logic
│ ├── core/ ← Infrastructure (DB, auth, logging)
│ ├── tools/ ← Custom MCP tool servers
│ ├── fast-agent/ ← Framework submodule (spawn system)
│ ├── team_templates/ ← Team definitions (agile_team.yaml)
│ └── .fast-agent/ ← Skills, agent cards, sessions
├── frontend/ ← Vue 3 + Vite + Tailwind v4
└── .github/workflows/ ← CI/CD
Key Patterns
| Pattern | Location | Description |
|---|---|---|
| Static agents | agent.py |
Jarvis, PersonalAgent, IoTAgent, etc. |
| Dynamic agents | .fast-agent/agent_cards/*.md |
Hot-reload at startup |
| Team agents | team_templates/agile_team.yaml |
Spawned at runtime |
| Inter-agent email | message_bus.py + email_server.py |
Async messaging |
| Meetings | meeting_room_server.py |
Real-time turn-taking |
| SSE pipeline | spawn events → bridge → SSE/SQLite | Real-time UI updates |
Tech Stack
- Backend: Python, FastAPI, fast-agent, SQLAlchemy, SQLite
- Dashboard: Vue 3, Vite, Tailwind v4, Pinia
- Deploy: Docker Compose, GitHub Actions CI/CD
- LLM: OpenAI-compatible via CLIProxyAPI (key rotation + load balancing)
References
| Topic | File |
|---|---|
| Full project tree + spawn architecture | DETAILED_STRUCTURE.md |
What ships with it
9 files 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.
- references/DETAILED_STRUCTURE.md 5.1 KB
- references/fast-agent-skills/agent-card-hooks.md 4.7 KB
- references/fast-agent-skills/compaction-strategies.md 1.3 KB
- references/fast-agent-skills/fast-agent-automation.md 1.5 KB
- references/fast-agent-skills/hf-space-deployer.md 1.0 KB
- references/fast-agent-skills/hf-static-space-deployer.md 869 B
- references/fast-agent-skills/lsp-setup.md 1.1 KB
- references/fast-agent-skills/marketplace.json 1.4 KB
- references/fast-agent-skills/session-investigator.md 2.5 KB
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 · 60 lines · 32 tokens per session scan A 3090733dc0be
jarvis-knowledge is a skill published in the GitHub repository omnigentx/jarvis (39 stars, last pushed 11d ago), licensed MIT. It adds 32 tokens to every session and 590 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 2026-08-30.
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…