engine-loop

A framework for improving recurring content, marketing, or outreach processes by collecting results, comparing experiments, and using the findings to create the next test.

In plain words
What is it for?
Use it to fetch metrics, score A/B test variants, promote winning approaches, retire losing ones, generate new inputs, and produce weekly reports.
Why use it?
It turns past run data into decisions about which approaches to keep, retire, or challenge, instead of repeating work without learning from results.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/benyki/gtm-engine/engine-loop
Any agent
npx skills add benyki/gtm-engine --skill engine-loop
Clone the repo
git clone --depth 1 https://github.com/benyki/gtm-engine

Made for: Claude Code, Codex.

Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,615 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00118 $0.04615
Opus 5 $0.00059 $0.02308
Sonnet 5 $0.00024 $0.00923
Haiku 4.5 $0.00012 $0.00462

Measured 2d ago against content hash b85b4925131d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

engine-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 2d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/assign_arm.py, scripts/due_metrics.py, scripts/gtmfind.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/engine-loop/SKILL.md · 231 lines

How it starts

The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.

engine-loop

Not an engine. Seo, social, video, outreach (and any custom engine) are the engines. engine-loop is the framework underneath them: it turns their runs into learning so those engines grow and compound over time.

Four jobs; run them in this order, because each depends on the one before.

Job Cadence Command
fetch metrics daily due_metrics.py → read → runlog.py metric
score + challenge weekly score_arms.py, then act on the verdict
generate inputs weekly see What to make next
report weekly render_report.py

Call them by full path — python3 ~/.agents/skills/engine-loop/scripts/<script>.py — from anywhere. That's the canonical store every install writes to, so the path is the same on every machine. Two shorter forms look tempting and both fail: scripts/… only resolves if the cwd is the skill folder, which is the one place the home isn't found from, and skills/… needs the home symlink, which is optional. The scripts locate the home from the current directory, so run them from the home or anywhere inside it — or pass --home <path> from outside. weekly.sh chains the deterministic ones and resolves its own paths.

The home is one folder per engine, plus shared/. Each engine folder is self-contained — its own engine.json (type, goal, primary metric), experiments.json, sources.json, templates/, inputs/, runs/, reports/. The loop scripts operate per folder and default to all of them; --engine <folder> scopes to one. Nothing is pooled across engines, so an agent rewriting one engine cannot break another — and two engines of the same type (outreach/ and outreach-investors/) are just two folders.

Starting fresh in a home you don't know? Read each engine's reports/latest.json first (and shared/insights.md for what the engines have learned from each other). It gives you the last period's runs, what shipped, the metric totals and their sources, every live experiment with its verdict, and how many runs are still owed a number — as data. Don't reconstruct that by reading CSVs.


Paths in this file: shared/… means the gtm home (~/gtm by default, or $GTM_HOME); templates/, inputs/, runs/ and reports/ mean the engine folder you're running, wherever it lives. The scripts resolve both through ~/gtm/engines.json, so read them as names rather than literal paths.

Read the full file on GitHub · 231 lines

Changes

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.

  1. 2d ago First seen · 231 lines · 118 tokens per session scan A b85b4925131d

Subscribe to this mod's changes

engine-loop is a skill published in the GitHub repository benyki/gtm-engine (5 stars, last pushed 29d ago), licensed MIT. It adds 118 tokens to every session and 4,615 once invoked, about $0.0006 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 2026-08-31.

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