Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add RadOrigin-LLC/RAD-Claude-Skills/plugin install rad-context-prompterWrote 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/radorigin-llc/rad-claude-skills/loop-goal-engineering)<a href="https://agentmods.dev/skills/radorigin-llc/rad-claude-skills/loop-goal-engineering"><img src="https://agentmods.dev/badge/skills/radorigin-llc/rad-claude-skills/loop-goal-engineering/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/radorigin-llc/rad-claude-skills/loop-goal-engineering"><img src="https://agentmods.dev/badge/skills/radorigin-llc/rad-claude-skills/loop-goal-engineering.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 | $0.00193 | $0.01438 |
| Opus 5 | $0.00097 | $0.00719 |
| Sonnet 5 | $0.00039 | $0.00288 |
| Haiku 4.5 | $0.00019 | $0.00144 |
Grade A, and why
loop-goal-engineering 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 10d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Loop & Goal Engineering
Author the artifacts that drive autonomous agent runs: loop prompts (re-run cold each iteration until a backlog is done), goal conditions (machine- or evaluator-checkable completion criteria), and the long-horizon file scaffold that carries state between iterations.
The runtime primitives exist — Claude Code /goal and /loop, Stop hooks,
Codex Goal Mode, bash while-loops. They all assume the human can already write
a good loop prompt or goal condition. That authoring step is this skill's job.
Step 1 — Classify the Deliverable
| User wants | Deliverable | Primary reference |
|---|---|---|
| Agent grinds through a backlog / overnight run | Loop prompt (+ scaffold files) | references/loop-patterns.md |
| Agent keeps working until X is true | Goal condition | references/goal-specs.md |
| Multi-hour/multi-day autonomous project | Four-file scaffold + loop prompt + goal | both |
| A check that must gate with zero exceptions | Stop-hook script spec | goal-specs.md §2 |
If the request is really a one-shot prompt with no iteration ("write a prompt that refactors this file"), hand off to the prompt-engineering skill.
Step 2 — Pick the Harness
Use the harness selection table in references/loop-patterns.md §1. Ask at
most one question if the target runtime is ambiguous (Claude Code vs Codex vs
raw bash loop) — it changes the output format:
- Claude Code
/goal→ condition must be provable from the agent's own transcript output (the evaluator runs no commands itself) - Codex Goal Mode → Goal / Context / Constraints / Done-when skeleton
- Stop hook → the condition becomes a script with exit codes
- Bash/Ralph loop → full loop prompt + four-file scaffold + safety rails
Step 3 — Draft
Loop prompts: apply the ten rules in loop-patterns.md §2 — one task per iteration, state in files/git, search-before-assuming, no placeholders, in-iteration verification, protected tests, idempotency, per-iteration commits, bounds, one-deliverable scope. Start from the template in §4.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 124 lines · 193 tokens per session scan A 1d3e80a18336
loop-goal-engineering is a skill published in the GitHub repository RadOrigin-LLC/RAD-Claude-Skills (5 stars, last pushed 24d ago), licensed Apache-2.0. It adds 193 tokens to every session and 1,438 once invoked, about $0.0010 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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