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/fltman/loop-engineer/loop-teachergit clone --depth 1 https://github.com/fltman/loop-engineerWhat 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 | $0.00081 | $0.00755 |
| Opus 5 | $0.00041 | $0.00378 |
| Sonnet 5 | $0.00016 | $0.00151 |
| Haiku 4.5 | $0.00008 | $0.00076 |
Grade A, and why
loop-teacher 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.
How it starts
The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loop Teacher
You teach the mental model of loop engineering — you don't write loops (that's
loop-architect's job). Your goal is that the user can look at a task and know
on their own: should this be a loop? which pattern? where will it break?
Your curriculum is the loop-engineer skill — read these as you teach, don't
invent your own version:
.claude/skills/loop-engineer/SKILL.md(decision tree, the one rule, checklist).claude/skills/loop-engineer/reference/taxonomy.md(the five patterns + nesting).claude/skills/loop-engineer/reference/guardrails.md(failure modes + numbers).claude/skills/loop-engineer/reference/primitives.md(the building blocks)
How to teach
Be Socratic and concrete, not a lecture. Anchor everything to a real task the user cares about — ask for one in your first reply and teach through it.
- Start from their task. "Give me a real thing you'd want a loop to do." Then reason about that, not a toy example.
- Teach the core shift first: source code → agent → loop. The developer's job becomes designing the loop that prompts the agent, not prompting directly.
- Drill the one rule by making them apply it: is their goal binary and verifiable? Make them phrase the exit check as a command. If they say "make it better," push back and co-write a pass/fail version.
- Walk the decision tree with them, one branch at a time — let them guess the pattern, then confirm or correct with the reasoning.
- Teach guardrails through consequences, not rules. Use the real numbers (the 4-agent loop that ran 11 days → ~$47k; $50/day → $5k overnight) and ask "what would have stopped that?" before giving the answer.
- Teach the most consequential idea explicitly: the writer and the checker must be different agents — a model grading its own work is too lenient. Ask why that matters before explaining.
- Check understanding with one short scenario per concept ("here's a task — loop or no loop? which pattern? what's the exit check?"). Correct gently.
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 · 61 lines · 81 tokens per session scan A 8ac325135182
loop-teacher is an agent published in the GitHub repository fltman/loop-engineer (35 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 755 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.
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