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 agents/tonik173/generative-ai-explained/agent-loopgit clone --depth 1 https://github.com/tonik173/generative-ai-explainedWhat 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 | $0.00000 | $0.00679 |
| Opus 5 | $0.00000 | $0.00340 |
| Sonnet 5 | $0.00000 | $0.00136 |
| Haiku 4.5 | $0.00000 | $0.00068 |
Grade A, and why
agent-loop 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 yesterday.
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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- yesterday First seen · 60 lines · 0 tokens per session scan A 266557efe364
agent-loop is an agent published in the GitHub repository tonik173/generative-ai-explained (2 stars, last pushed 1mo ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 679 tokens. 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 2026-08-31.
Other agents, from other repositories
aki-conduct
Judge the process rather than the output — was the rule delivered, and was it followed. Separates LOAD-fail from COMPLY-fail using the [RULES] receipts, and runs scythe.py for mechanical file:line evidence. Never proposes a fix to the artifact.
aki-challenger
Attack a finished result from a clean context — never given the reasoning that produced it. Always closes with "what can be cut?" and "does this answer the anchored words?". Spawn before anything solution-shaped closes.
aki-judge
Judge an artifact against exactly one standard, named at spawn (pattern, proportion, ux, db, seo, release, …). Returns a verdict with evidence, never a fix. Spawn one per standard rather than asking one agent to hold several.
aki-maker
Turn an already-made decision into a diff. The only agent permitted to write. Requires the decision, the exact files, and the domain rules named in its brief — it implements, it does not decide.
aki-hands
Retrieval only — find files, lines, counts, and quoted evidence, and return them with file:line. Judgment is forbidden. Use for any sweep, inventory, grep, or read-to-find-out whose paths and question can be named up front.
DocScribe
Write docs/ prose for a completed change, in caveman style, per the repo's Documentation Update Protocol. Use after a change lands to update docs/architecture.md, docs/ .md, README, or docs/AGENTS.md — the Rule-6 delegation target (main agent must NOT edit docs/ directly). Self-contained — give it the diff + target…