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 notque/vexjoy-agent --skill architecture-deepeninggit clone --depth 1 https://github.com/notque/vexjoy-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/notque/vexjoy-agent/architecture-deepening)<a href="https://agentmods.dev/skills/notque/vexjoy-agent/architecture-deepening"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/architecture-deepening/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/notque/vexjoy-agent/architecture-deepening"><img src="https://agentmods.dev/badge/skills/notque/vexjoy-agent/architecture-deepening.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 | $0.00014 | $0.02791 |
| Opus 5.5 | $0.00006 | $0.01116 |
| Sonnet 5.5 | $0.00003 | $0.00558 |
| Haiku 4.5 | $0.00001 | $0.00279 |
Grade A, and why
architecture-deepening 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 25d 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Architecture Deepening
Find shallow modules and propose deepening opportunities. Not a code review -- does not find bugs or style violations. Finds modules where the interface is too close to the implementation, where users must understand internals to use the API, and where small interface changes would absorb disproportionate complexity.
When to use: After codebase onboarding or review when improvement was requested, before a cross-module feature, after a fix exposes a missing test seam, or when callers repeatedly need source knowledge or multi-module coordination.
Differs from full-repo-review: Full-repo-review finds defects. This skill finds structural improvement opportunities. Pair well: run full-repo-review first to fix defects, then architecture-deepening to raise the bar.
Deep References
Load on demand when working in the named phase.
| Phase | Reference | Content |
|---|---|---|
| Phase 1 | references/maintenance-lifecycle.md |
Entry rules, recent-change scope, decision memory, candidate schema, typed handoff |
| Phase 1 | references/vocabulary.md |
Shared vocabulary: module, depth, seam, leverage, locality, deletion test |
| Phase 2 | references/interface-design.md |
Patterns for exploring alternative interfaces, deletion test |
| Phase 2-3 | references/deepening-strategies.md |
Dependency categorization, safe deepening, testing strategies |
Instructions
Run phases in order until a terminal gate. Survey and design keep source code read-only; Write/Edit apply only to user-approved decision records after selection. Existing delivery workflows own code changes. The user selects candidates and decides whether a handoff proceeds.
Language-agnostic. Vocabulary and strategies apply to Go, Python, TypeScript, or any codebase with module boundaries.
Phase 1: EXPLORE
Goal: Identify shallow modules -- where the interface exposes too much implementation detail.
Step 1: Choose an evidence-fed scope
What ships with it
7 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.
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.
- 26d ago Changed · -167 lines b33dc92a19ad
- 27d ago Changed · +1 lines 94fefa4558ef
- 1mo ago First seen · 202 lines · 14 tokens per session scan A 2e4922976004
architecture-deepening is a skill published in the GitHub repository notque/vexjoy-agent (442 stars, last pushed today), licensed MIT. It adds 14 tokens to every session and 2,791 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 2026-09-03.
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