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 DemonDamon/AgenticX --skill agenticx-agent-buildergit clone --depth 1 https://github.com/DemonDamon/AgenticXWrote 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/demondamon/agenticx/agenticx-agent-builder)<a href="https://agentmods.dev/skills/demondamon/agenticx/agenticx-agent-builder"><img src="https://agentmods.dev/badge/skills/demondamon/agenticx/agenticx-agent-builder/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/demondamon/agenticx/agenticx-agent-builder"><img src="https://agentmods.dev/badge/skills/demondamon/agenticx/agenticx-agent-builder.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 | $0.00054 | $0.00894 |
| Opus 5.5 | $0.00022 | $0.00358 |
| Sonnet 5.5 | $0.00011 | $0.00179 |
| Haiku 4.5 | $0.00005 | $0.00089 |
Grade A, and why
agenticx-agent-builder 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 29d 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 — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Near Digital Avatar Builder
Help the user create a persistent desktop digital avatar (数字分身) that appears in the Near sidebar avatar list.
Hard rules
- Must call
create_avatarafter clarifying and distilling the config. That tool writes the avatar into the registry and makes it appear in the sidebar. - Do NOT only write a Markdown file to Desktop / workspace and claim the avatar is created.
- Do NOT tell the user there is "no create-avatar API" —
create_avataris the API. - Do NOT ask the user to open「+ 新建」and fill the manual form unless they explicitly want the GUI.
- Do NOT invent Python SDK /
AgentExecutor/agx agent createworkflows for this task — those are code-layer agents, not Near desktop avatars.
Interview flow (2–3 rounds, not a long questionnaire)
Ask only what is still missing. Prefer short choices (clarification / show_widget) over open essays.
Round 1 — Positioning
- What should this avatar own? (domain / job)
- Who is it for? (usually the current user)
Round 2 — Scope & style
- Coverage (markets, stacks, products, languages…)
- Analysis / work style (trading vs research, concise vs thorough, etc.)
Round 3 — Boundaries (only if needed)
- Risk preference / what it must never do
- Preferred output structure
- Optional default model
Stop early when you already have enough to write a solid name / role / system_prompt.
Distill into these fields
| Field | Meaning | Example |
|---|---|---|
name |
Short character-like display name | 飞廉 |
role |
One-line positioning | 进取型全市场交易分析师 |
system_prompt |
Full behavior contract | See template below |
default_provider / default_model |
Optional; omit to inherit global default |
system_prompt should include
- Identity & coverage
- Analysis / work priorities
- Risk preference & hard boundaries
- Data discipline (no fabricated numbers / facts)
- Output style & fixed structure when useful
- Disclaimer when the domain needs one (finance, medical, legal, etc.)
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.
- 29d ago Changed 282982e2e2f2
- 1mo ago Changed 6b4f237ea085
- 2mo ago First seen · 104 lines · 54 tokens per session scan A 42e0560cbdce
agenticx-agent-builder is a skill published in the GitHub repository DemonDamon/AgenticX (795 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 894 once invoked, about $0.0002 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
skill-creator
Create, update, or package agent skills.
tmux
Use when a CLI task needs an interactive terminal or control of a tmux session.
weather
Get current weather and forecasts.
github
Manage GitHub issues and pull requests, inspect CI runs, and query repository data.
nanobot-document
Answer nanobot usage, configuration, and troubleshooting questions from its bundled documentation.
memory
Search past conversations in the agent's history log.