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 agentmods add skills/silo-code/silo/silo-domain-modelingnpx skills add silo-code/silo --skill silo-domain-modelinggit clone --depth 1 https://github.com/silo-code/siloWrote 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/silo-code/silo/silo-domain-modeling)<a href="https://agentmods.dev/skills/silo-code/silo/silo-domain-modeling"><img src="https://agentmods.dev/badge/skills/silo-code/silo/silo-domain-modeling.svg" alt="Measured on agentmods" 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 | $0.00077 | $0.00989 |
| Opus 5 | $0.00039 | $0.00495 |
| Sonnet 5 | $0.00015 | $0.00198 |
| Haiku 4.5 | $0.00008 | $0.00099 |
Grade A, and why
silo-domain-modeling 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 4d 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Domain Modeling
Actively build and sharpen Silo's domain model as you design — not just read it. Challenging terms, inventing edge-case scenarios, and writing the glossary/decisions down the moment they crystallize is the active half of this skill; merely reading the docs below for vocabulary is a one-line habit any skill can do.
Where things live
docs/domain-language.md— the glossary. Ubiquitous language for the product: Workspaces, Navigator, Panels & Docking, Agents, Worktrees, Keybindings, and any other cluster of terms specific to Silo. Single file, single context — Silo doesn't split into bounded contexts, so there's noCONTEXT-MAP.md-style index to maintain.docs/decisions/(ADRs) — decisions already made. Readdocs/decisions/README.mdfirst for the ADR-vs-RFC test, numbering, and status vocabulary, and usedocs/decisions/template.mdfor the format. Update the index table inREADME.mdwhen adding one.docs/proposals/(RFCs) — forward-looking designs not yet decided. Readdocs/proposals/README.mdand usedocs/proposals/template.md. Same index-update rule.
During the session
Challenge against the glossary
When a term conflicts with docs/domain-language.md, call it out
immediately. "The glossary defines 'cancellation' as X, but you seem to mean
Y — which is it?"
Sharpen fuzzy language
Propose a precise canonical term for vague or overloaded language. "You're saying 'active' — the Navigator's Active View, or a Dock's Active Panel? Those are deliberately different things here."
Discuss concrete scenarios
Stress-test domain relationships with specific, edge-case scenarios that force precision about the boundaries between concepts.
Cross-reference with code and existing docs
Check whether the code, and any existing ADR/RFC, agree with what's being
stated. Surface contradictions rather than silently accepting them:
"terminal-service.ts's focus() checks the live dock (ADR 0034), but you
just described it checking activeWorkspaceId — which is current?"
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.
- 4d ago First seen · 98 lines · 77 tokens per session scan A 01e6ac8eb3df
silo-domain-modeling is a skill published in the GitHub repository silo-code/silo (55 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 989 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
aoe
Use when launching, monitoring, or controlling AI coding agents (Claude Code, Codex, OpenCode, etc.) in tmux via Agent of Empires (aoe). Covers creating sessions, capturing agent output, running parallel worktree agents, and organizing work into groups and profiles. Prefer aoe over raw tmux for agent management.
aoe
Manage AI coding agent sessions via Agent of Empires (aoe).
agtx-sweep
Sweep this conversation into agtx tasks and push them to the kanban board. Use when the user wants to capture, decompose, or hand off conversation results to the agtx board.
agtx-plan
Plan a task implementation. Analyze the codebase, create a detailed plan, write it to .agtx/plan.md, then stop and wait for user approval before making any changes.
agtx-research
Explore the codebase to understand a task before planning. Write findings to .agtx/research.md and stop. This is a read-only exploration — do not modify any files.
agtx-execute
Execute an approved implementation plan. Implement the changes, then write a summary to .agtx/execute.md and stop.