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 agentic-dev3o/devx-plugins --skill explaining-architecturegit clone --depth 1 https://github.com/agentic-dev3o/devx-pluginsWrote 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/agentic-dev3o/devx-plugins/explaining-architecture)<a href="https://agentmods.dev/skills/agentic-dev3o/devx-plugins/explaining-architecture"><img src="https://agentmods.dev/badge/skills/agentic-dev3o/devx-plugins/explaining-architecture/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/agentic-dev3o/devx-plugins/explaining-architecture"><img src="https://agentmods.dev/badge/skills/agentic-dev3o/devx-plugins/explaining-architecture.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 | $0.00043 | $0.00660 |
| Opus 5.5 | $0.00017 | $0.00264 |
| Sonnet 5.5 | $0.00009 | $0.00132 |
| Haiku 4.5 | $0.00004 | $0.00066 |
Grade A, and why
explaining-architecture 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Project Explain
Analyze project architecture and produce ASCII state machine and sequence diagrams for a given codebase or feature.
Target: $ARGUMENTS
Workflow
Copy this checklist and track progress:
Architecture Analysis:
- [ ] Step 1: Analyze project composition
- [ ] Step 2: Find entrypoint
- [ ] Step 3: Build state machine graph
- [ ] Step 4: Generate sequence diagram
- [ ] Step 5: Output synthesis
Step 1: Analyze Project Composition
Run language statistics: tokei || echo 'not installed'
If tokei is unavailable, use Glob to count files by extension and estimate composition.
Detect and read the first package manifest found (package.json, Cargo.toml, go.mod, pyproject.toml, requirements.txt, pom.xml, build.gradle). Identify framework from config files and directory structure.
Step 2: Find Entrypoint
If $ARGUMENTS is specified, locate that feature or module first.
Otherwise search for the main entrypoint: main field in manifest, common files (main.*, index.*, app.*, cmd/main.go), or framework-specific entries (pages/_app.tsx, src/main.rs, manage.py).
From the entrypoint, trace initialization, configuration loading, service bootstrapping, and main event loop or request handler.
Step 3: Build State Machine Graph
Analyze the target for state variables, transition triggers, guards, side effects, and terminal states. Generate an ASCII acyclic graph using box-drawing characters (─ │ ┌ ┐ └ ┘ ├ ┤ ┬ ┴ ┼ ► ◄ ▲ ▼). Max 80 chars wide.
Step 4: Generate Sequence Diagram
Identify actors (client, controllers, services, data layer, external APIs). Generate an ASCII sequence diagram showing control flow between them. Max 80 chars wide.
Step 5: Output Synthesis
Present findings using this template:
╔════════════════════════════════════════╗
║ PROJECT: <name> ║
╠════════════════════════════════════════╣
║ Language: <primary> ║
║ Framework: <detected> ║
║ Type: <CLI/API/Web/Library> ║
╚════════════════════════════════════════╝
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 · 74 lines · 43 tokens per session scan A 838596e1af26
explaining-architecture is a skill published in the GitHub repository agentic-dev3o/devx-plugins (12 stars, last pushed 10d ago), licensed MIT. It adds 43 tokens to every session and 660 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.
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