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 Ertinox7711/SGRR-AGI-V2 --skill howgit clone --depth 1 https://github.com/Ertinox7711/SGRR-AGI-V2Wrote 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/ertinox7711/sgrr-agi-v2/how)<a href="https://agentmods.dev/skills/ertinox7711/sgrr-agi-v2/how"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/how/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/ertinox7711/sgrr-agi-v2/how"><img src="https://agentmods.dev/badge/skills/ertinox7711/sgrr-agi-v2/how.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 | $0.00028 | $0.01635 |
| Opus 5 | $0.00014 | $0.00817 |
| Sonnet 5 | $0.00006 | $0.00327 |
| Haiku 4.5 | $0.00003 | $0.00163 |
Grade A, and why
how 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 3d 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.
This is a copy
100% identical to how — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
How
Explore the codebase to answer "how does X work?" questions. Produce clear architectural explanations at the level of a senior engineer onboarding onto a subsystem — enough to build a working mental model, not so much that it reads like annotated source code.
Two modes:
- Explain (default) — explore the codebase and produce a clear explanation
- Critique — explain first, then spawn multiple models to independently identify architectural issues
Explain Mode
Step 1 — Understand the Question and Assess Complexity
Parse what the user is asking about. They might say:
- "How does message virtualization work?" — a subsystem
- "How do we handle billing for on-demand usage?" — a feature flow
- "How is the auth service structured?" — an architectural overview
- "Walk me through what happens when a user sends a message" — a runtime trace
Identify the scope. If it's ambiguous, make your best guess and state your interpretation before exploring. Don't ask — explore and let the user redirect if you're off.
Assess complexity to decide the approach:
- Simple (a single module, a small utility, a narrow question like "how does function X work"): Skip explorer agents entirely. The explainer agent explores and explains in a single pass. Go directly to Step 2b.
- Complex (a subsystem spanning multiple files/services, a cross-cutting feature, a full architectural overview): Spawn parallel explorer agents first, then hand off to the explainer. Go to Step 2a.
When in doubt, lean toward the simple path — you can always spawn explorers if the explainer hits a wall.
Step 2a — Explore (complex questions only)
Decompose the question into 2-4 parallel exploration angles. Each angle should cover a distinct slice of the subsystem so the explorers aren't duplicating work. For example, if the question is "how does message virtualization work?", you might split into:
- Explorer 1: the data model and state management
- Explorer 2: the rendering pipeline and DOM interaction
- Explorer 3: the scroll/measurement infrastructure
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.
- 3d ago First seen · 141 lines · 28 tokens per session scan A 2dd4d2818db0
how is a skill published in the GitHub repository Ertinox7711/SGRR-AGI-V2 (1 stars, last pushed 4d ago), licensed MIT. It adds 28 tokens to every session and 1,635 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to how, differing in 0 lines, and is treated as a copy.
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